diff --git a/.gitignore b/.gitignore index cd9f9d0011bdd9d987d790f972dacb3b7633629a..39b321253ce269d9400f5d8f2f6a8f9cba2ca792 100644 --- a/.gitignore +++ b/.gitignore @@ -31,12 +31,8 @@ __pycache__/ .env *.local -# Dated release checkpoints are intentionally versioned on Hugging Face. +# BioMod model weights and their matching configuration are versioned on Hugging Face. !weight/ weight/* -!weight/0629model/ -!weight/0629model/*.pkl -!weight/0629model/*.pth -!weight/0810model/ -!weight/0810model/*.pkl -!weight/0810model/*.pth +!weight/BioMod.pkl +!weight/BioMod.pth diff --git a/BioMod/README.md b/BioMod/README.md index c0b45ecfed7181545da2bee95a4671ff603ed160..bd91468d66cc30029e3fbdf59f21daa195a464a5 100644 --- a/BioMod/README.md +++ b/BioMod/README.md @@ -1,7 +1,7 @@ -# BioMod / PerformerModule +# BioMod Source code and tutorial notebooks for BioMod single-cell pretraining and -downstream experiments using PerformerModule representations. +downstream experiments using BioMod representations. This source directory contains the pretraining entry point and shared training utilities, alongside downstream task runners, tutorial notebooks, environment @@ -29,67 +29,63 @@ from these repository trees. ```text BioMod/ # Task code: training, evaluation, interpretability and tutorials ├── cell_annotation/ # Cell-type annotation and model fine-tuning -│ ├── module_data/ # Hallmark and Reactome modules with loading utilities -│ │ ├── load_module.py -│ │ ├── module1_hallmarks.json -│ │ └── module2_reactome_2022_filtered.json +│ ├── biomod_data/ # Hallmark and Reactome modules with loading utilities +│ │ ├── load_biomod.py +│ │ ├── biomod1_hallmarks.json +│ │ └── biomod2_reactome_2022_filtered.json │ ├── cmd_finetune.md -│ └── performer_module_finetune.py +│ └── biomod_finetune.py ├── drug_combination/ # Drug-combination synergy prediction │ ├── README.md -│ └── train_drug_combination_performer_module.py +│ └── train_drug_combination_biomod.py ├── drug_response/ # Single-drug response prediction and training │ ├── cmd.txt -│ └── train_drug_performer_module.py +│ └── train_drug_biomod.py ├── gene_perturbation/ # Perturbation response prediction: processing, training and inference │ ├── data_utils_local.py │ ├── gears_reduce_local.py │ ├── inference_local.py -│ ├── model_module.py +│ ├── model_biomod.py │ ├── pertdata_local.py │ ├── README.md │ ├── scfm_utils_local.py -│ ├── train_module.py +│ ├── train_biomod.py │ └── utils.py ├── interpretability/ # Module representations and biological interpretability │ ├── attention/ # Module crosstalk and within-module gene attention │ │ ├── __init__.py -│ │ ├── module_crosstalk.py -│ │ ├── module_gene_attention.py +│ │ ├── biomod_crosstalk.py +│ │ ├── biomod_gene_attention.py │ │ ├── README.md │ │ └── README_gene_attention.md -│ ├── module_importance/ # Module importance, ablation studies and marker-gene validation +│ ├── biomod_importance/ # Module importance, ablation studies and marker-gene validation │ │ ├── __init__.py │ │ ├── cross_class_similarity.py │ │ ├── family_ablation.py │ │ ├── family_importance_analysis.py │ │ ├── marker_gene_verification.py │ │ ├── masking_ablation.py -│ │ ├── module_importance.py -│ │ ├── module_importance_publication.py +│ │ ├── biomod_importance.py +│ │ ├── biomod_importance_publication.py │ │ ├── pub_utils.py │ │ ├── random_baseline.py │ │ ├── README.md │ │ ├── RESULTS.md │ │ ├── tcell_reactome_analysis.py │ │ └── TODO.md -│ ├── module_text_alignment/ # Semantic alignment of module representations and pathway text +│ ├── biomod_text_alignment/ # Semantic alignment of module representations and pathway text │ │ ├── __init__.py │ │ ├── DESIGN.md │ │ ├── fetch_reactome_descriptions.py │ │ ├── MANUSCRIPT_SECTION.md -│ │ ├── module_text_alignment.py -│ │ ├── module_text_alignment_extended.py -│ │ ├── module_text_alignment_publication.py +│ │ ├── biomod_text_alignment.py +│ │ ├── biomod_text_alignment_extended.py +│ │ ├── biomod_text_alignment_publication.py │ │ └── reactome_descriptions.json │ ├── __init__.py -│ ├── module_geometry.py +│ ├── biomod_geometry.py │ ├── README.md │ └── utils.py -├── module_data_all/ # Module definitions, merging and uncovered-gene tools -│ ├── create_uncovered_modules.py -│ ├── merge_modules.py -│ └── merged_modules.json ├── tests/ # CPU tests for shared training utilities │ └── test_training_utils.py ├── toturials/ # Jupyter tutorials for five downstream tasks @@ -103,7 +99,7 @@ BioMod/ # Task code: training, │ │ ├── cmd.txt │ │ ├── make_low_depth_h5ad.py │ │ └── run_zero_shot_low_depth.py -│ ├── performer_module_zero_shot.py +│ ├── biomod_zero_shot.py │ ├── README.md │ └── visualize_results.py ├── .env.example @@ -111,9 +107,9 @@ BioMod/ # Task code: training, ├── .gitignore ├── command.txt ├── environment.yml -├── models_performer_module.py -├── module_dataset.py -├── performer_module.py +├── models_biomod.py +├── biomod_dataset.py +├── biomod.py ├── README.md ├── requirements.txt └── training_utils.py @@ -130,7 +126,7 @@ environment: ```bash cd BioMod conda env create -f environment.yml -conda activate performer_module +conda activate biomod ``` Or install Python packages into an existing CUDA/PyTorch environment: @@ -143,7 +139,7 @@ From this source directory, activate the supplied environment and start Jupyter with the tutorial directory as its working directory: ```bash -conda activate performer_module +conda activate biomod cd toturials jupyter lab ``` @@ -161,26 +157,26 @@ imports after installing the dependencies: ```bash python -B -m unittest discover -s tests -p test_training_utils.py -v -python -B performer_module.py --help +python -B biomod.py --help ``` The utility tests use synthetic tensors and temporary checkpoints; they do not load the supplied pretrained weights. Passing these checks does not validate a full training run. The pretraining entry point requires CUDA and an NCCL process group. Supply `--data_path` explicitly: the pretraining corpus is not -bundled. Configure module inputs with `--module_data_dir` or `--module_data_path`. +bundled. Configure module inputs with `--biomod_data_dir` or `--biomod_data_path`. Generated checkpoints default to `tutorial_runs/pretraining/` under the outer repository root, with periodic latest checkpoints under its `latest/` subdirectory. ## Shared Assets The BioMod model weights and matching configuration are stored in -the outer repository's `weight/0810model/`. Use the supplied `.pth` and `.pkl` +the outer repository's `weight/`. Use the supplied `.pth` and `.pkl` together. -Tutorials expect a pretrained BioMod/PerformerModule checkpoint and shared +Tutorials expect a pretrained BioMod checkpoint and shared gene assets. For the shell examples below, set `BIOMOD_ROOT` to the directory containing -`weight/0810model/` and `model_dependencies/` (the outer `BioMod/` project directory). +`weight/` and `model_dependencies/` (the outer `BioMod/` project directory). ```bash # Run this from the inner BioMod/ source directory. @@ -190,45 +186,33 @@ export BIOMOD_ROOT="$(cd .. && pwd)" Current local asset files: ```text -$BIOMOD_ROOT/weight/0810model/ # BioMod model weights and matching configuration -├── mae_tost_70M_75mask_ec2.pkl -└── mae_tost_70M_75mask_ec2_epoch0.pth +$BIOMOD_ROOT/weight/ # BioMod model weights and matching configuration +├── BioMod.pkl +└── BioMod.pth $BIOMOD_ROOT/model_dependencies/ # Shared gene mappings, gene lists, embeddings and model components -├── module_data_all/ # Shared module definitions and construction tools -│ ├── create_uncovered_modules.py -│ ├── merge_modules.py -│ └── merged_modules.json -├── performer_pytorch_cont/ # Performer attention and model components -│ ├── performer_pytorch_cont/ # Nested package containing copies of the model component source files -│ │ ├── __init__.py -│ │ ├── ding_models.py -│ │ ├── performer_pytorch.py -│ │ ├── performer_pytorch_old.py -│ │ └── reversible.py +├── biomod_data_all/ # Shared module definitions and construction tools +│ ├── create_uncovered_biomods.py +│ ├── merge_biomods.py +│ └── merged_biomods.json +├── pytorch_cont/ # Attention and model components │ ├── __init__.py │ ├── ding_models.py -│ ├── performer_pytorch.py -│ ├── performer_pytorch_old.py +│ ├── pytorch.py +│ ├── pytorch_old.py │ └── reversible.py ├── gene2ens.csv ├── selected_gene2vec_27k.npy └── selected_genes_27k.txt ``` -Project-local module definitions are kept in: - -```text -BioMod/module_data_all/ # Module definitions, merging and uncovered-gene tools -├── create_uncovered_modules.py -├── merge_modules.py -└── merged_modules.json -``` +Module definitions and construction tools are shared through +`$BIOMOD_ROOT/model_dependencies/biomod_data_all/`. ## BioMod model weights -Use `mae_tost_70M_75mask_ec2_epoch0.pth` with -`mae_tost_70M_75mask_ec2.pkl` from the shared-assets directory above. The supplied +Use `BioMod.pth` with +`BioMod.pkl` from the shared-assets directory above. The supplied configuration specifies a 2-layer encoder, a 27,874-gene axis, and 660 module definitions. Keep this configuration paired with the weights so that task runners can restore the corresponding architecture settings. @@ -240,8 +224,8 @@ runners can restore the corresponding architecture settings. | Cell-type annotation | `toturials/cell_annotation.ipynb` | task script under `cell_annotation/` | metrics, classification report, confusion matrix | | Zero-shot batch integration | `toturials/zero_shot_batch_integration.ipynb` | `zero_shot_batch/` runner | UMAP PDF/PNG, avgBatch, avgBio | | Gene perturbation | `toturials/gene_perturbation.ipynb` | `gene_perturbation/` runner | selected model, configuration, parameters, and training log | -| Drug response prediction | `toturials/drug_response_prediction.ipynb` | `drug_response/train_drug_performer_module.py` | scatter, residuals, ROC, per-drug and per-cancer tables | -| Drug-combination synergy | `toturials/drug_combination.ipynb` | `drug_combination/train_drug_combination_performer_module.py` | label balance, loss, validation metrics, and ROC curves | +| Drug response prediction | `toturials/drug_response_prediction.ipynb` | `drug_response/train_drug_biomod.py` | scatter, residuals, ROC, per-drug and per-cancer tables | +| Drug-combination synergy | `toturials/drug_combination.ipynb` | `drug_combination/train_drug_combination_biomod.py` | label balance, loss, validation metrics, and ROC curves | Each notebook derives paths from `PROJECT_ROOT = Path('../..').resolve()` when Jupyter runs from `BioMod/toturials/`. From the repository root, change this to @@ -296,12 +280,12 @@ Run these commands from the inner `BioMod/` source directory after setting `BIOMOD_ROOT` as shown above. Drug response prediction with the BioMod model weights: ```bash -python drug_response/train_drug_performer_module.py \ +python drug_response/train_drug_biomod.py \ --gpu_id 0 \ --data_path "$BIOMOD_ROOT/data/downstream/final_data/single_drug_response/data" \ - --checkpoint_path "$BIOMOD_ROOT/weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth" \ - --hyper_params_path "$BIOMOD_ROOT/weight/0810model/mae_tost_70M_75mask_ec2.pkl" \ - --module_data_dir "$BIOMOD_ROOT/model_dependencies/module_data_all" \ + --checkpoint_path "$BIOMOD_ROOT/weight/BioMod.pth" \ + --hyper_params_path "$BIOMOD_ROOT/weight/BioMod.pkl" \ + --biomod_data_dir "$BIOMOD_ROOT/model_dependencies/biomod_data_all" \ --gene_list_path "$BIOMOD_ROOT/model_dependencies/selected_genes_27k.txt" \ --gene_mapping_path "$BIOMOD_ROOT/model_dependencies/gene2ens.csv" \ --gene2vec_path "$BIOMOD_ROOT/model_dependencies/selected_gene2vec_27k.npy" \ @@ -315,10 +299,10 @@ python drug_response/train_drug_performer_module.py \ Drug-combination synergy prediction: ```bash -python drug_combination/train_drug_combination_performer_module.py \ - --checkpoint_path "$BIOMOD_ROOT/weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth" \ - --hyper_params_path "$BIOMOD_ROOT/weight/0810model/mae_tost_70M_75mask_ec2.pkl" \ - --module_data_dir "$BIOMOD_ROOT/model_dependencies/module_data_all" \ +python drug_combination/train_drug_combination_biomod.py \ + --checkpoint_path "$BIOMOD_ROOT/weight/BioMod.pth" \ + --hyper_params_path "$BIOMOD_ROOT/weight/BioMod.pkl" \ + --biomod_data_dir "$BIOMOD_ROOT/model_dependencies/biomod_data_all" \ --gene_list_path "$BIOMOD_ROOT/model_dependencies/selected_genes_27k.txt" \ --gene_mapping_path "$BIOMOD_ROOT/model_dependencies/gene2ens.csv" \ --gene2vec_path "$BIOMOD_ROOT/model_dependencies/selected_gene2vec_27k.npy" \ @@ -376,7 +360,7 @@ files and shows which entries are missing. Check `BIOMOD_ROOT` for shell commands or `PROJECT_ROOT` in a notebook, and verify that the BioMod model weights, matching `.pkl` configuration, gene2vec file, gene list, gene mapping, and -`performer_pytorch_cont/` directory exist at the paths listed under Shared Assets. +`pytorch_cont/` directory exist at the paths listed under Shared Assets. ### No space left on device diff --git a/BioMod/performer_module.py b/BioMod/biomod.py similarity index 97% rename from BioMod/performer_module.py rename to BioMod/biomod.py index 132c07f1a3afe7a3d5917984bcaa1722b64a7912..fd3d9acebd8388d22b63aff7d7745c05d79a6603 100644 --- a/BioMod/performer_module.py +++ b/BioMod/biomod.py @@ -2,14 +2,14 @@ """ ================================================================================ -Pre-training script for Encoder-only Performer model +Pre-training script for encoder-only BioMod model ================================================================================ -Refer to MAE's data input and masking methods, but use Performer as the encoder +Refer to MAE's data input and masking methods, but use the BioMod attention encoder Main features: 1. Use module data input (refer to MAE) 2. Mask the module (refer to MAE’s random_masking) 3. Use a configurable reconstruction decoder or direct prediction heads -4. Use Performer as encoder (instead of standard Transformer Block) +4. Use the BioMod attention encoder ================================================================================ [Data flow example - taking default parameters as an example] @@ -25,11 +25,11 @@ - 3 = batch_size - 27874 = num_genes (gene expression value) -Step 3: Model forward propagation (see models_performer_module.py for details) +Step 3: Model forward propagation (see models_biomod.py for details) ├─> 3.1 Flat to Module: (3, 27874) → (3, num_modules, module_avg_size) ├─> 3.2 Module Embedding: → (3, num_modules, 1024) ├─> 3.3 Random Masking: mask out 75% of modules and keep 25% - ├─> 3.4 Performer Encoder: Process visible modules + ├─> 3.4 BioMod encoder: Process visible modules ├─> 3.5 Reconstruction: Predict the expression value of the masked module └─> 3.6 Calculate Loss: Calculate MSE only on the masked module @@ -63,11 +63,11 @@ ASSET_ROOT = PROJECT_ROOT / 'model_dependencies' if str(SOURCE_DIR) not in sys.path: sys.path.insert(0, str(SOURCE_DIR)) -from models_performer_module import ( - PerformerEncoderOnly_Module, - sanitize_performer_module_config, +from models_biomod import ( + BioMod, + sanitize_biomod_config, ) -from module_dataset import ModuleSCDataset, load_module_data +from biomod_dataset import BioModSCDataset, load_biomod_data from training_utils import ( CosineAnnealingWarmupRestarts, SequentialDistributedSampler, @@ -125,7 +125,7 @@ parser.add_argument("--dec_heads", type=int, default=8, help='Number of decoder attention heads (valid when performer/cross_attn)') parser.add_argument("--dec_dim_head", type=int, default=64, help='Performer decoder dim per head (ignored when cross_attn)') parser.add_argument("--ckpt_dir", type=str, default=str(PROJECT_ROOT / 'tutorial_runs' / 'pretraining'), help='Directory for generated checkpoints; user relative paths use the current working directory.') -parser.add_argument("--model_name", type=str, default='performer_module_27k', help='Pretrained model name.') +parser.add_argument("--model_name", type=str, default='biomod_27k', help='Pretrained model name.') parser.add_argument("--data_path", type=str, required=True, help='Pretraining input: a directory for npz/txt or a file for h5/h5ad. Supply your own pretraining data; it is not bundled.') parser.add_argument("--data_format", type=str, default="npz", choices=["npz", "h5", "h5ad", "txt"], @@ -133,8 +133,8 @@ parser.add_argument("--h5_dataset_key", type=str, default="expression", help='The data set key name of the expression matrix in h5 format. The default is to read h5[\"expression\"]') # Module related parameters -parser.add_argument("--module_data_path", type=str, default=None, help='Path to module JSON file (single file).') -parser.add_argument("--module_data_dir", type=str, default=None, help='Path to directory containing module JSON files.') +parser.add_argument("--biomod_data_path", "--module_data_path", type=str, default=None, help='Path to module JSON file (single file).', dest='module_data_path') +parser.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=None, help='Path to directory containing module JSON files.', dest='module_data_dir') parser.add_argument("--gene_mapping_path", type=str, default=str(ASSET_ROOT / 'gene2ens.csv'), help='Path to gene symbol to Ensembl ID mapping CSV.') parser.add_argument("--gene2vec_data_path", type=str, default=str(ASSET_ROOT / 'selected_gene2vec_27k.npy')) parser.add_argument("--fusion_method", type=str, default="multiply", @@ -167,8 +167,8 @@ parser.add_argument("--load_opt_and_sched", action='store_true') parser.add_argument("--lr_step_within_epoch", action='store_true', help='Update the learning rate according to the optimizer update step; the cycle length is updates_per_epoch (gradually decreasing in each epoch)') -parser.add_argument("--shuffle_module_genes", action='store_true', - help='Whether to disrupt the gene allocation within the module (used for comparative experiments to verify the effectiveness of module division)') +parser.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action='store_true', + help='Whether to disrupt the gene allocation within the module (used for comparative experiments to verify the effectiveness of module division)', dest='shuffle_module_genes') parser.add_argument("--shuffle_seed", type=int, default=2021, help='Random seed used when scrambling module genes (downstream tasks need to use the same value)') # Gene2vec ablation experiment parameters @@ -278,7 +278,7 @@ def restore_model_args_from_config(args, config): args.model_config_path, ) with open(resolved_model_config_path, "rb") as handle: - resume_model_config = sanitize_performer_module_config( + resume_model_config = sanitize_biomod_config( pickle.load(handle), source=resolved_model_config_path, log_fn=print, @@ -325,11 +325,11 @@ def restore_model_args_from_config(args, config): # WandB resume logic wandb_init_kwargs = { - "project": "pretrain_performer_module", + "project": "pretrain_biomod", "name": args.model_name, "entity": "icml_2023_mlb", "config": { - "architecture": "performer_module", + "architecture": "biomod", "gene_num": args.gene_num, "cell_num": args.cell_num, "load model": args.model_path, @@ -490,7 +490,7 @@ def resolve_total_cells_for_split(): if is_master: print(f"Using {len(module_sets)} modules restored from model config PKL") else: - module_sets = load_module_data( + module_sets = load_biomod_data( module_data_path=args.module_data_path, module_data_dir=args.module_data_dir, gene_list_path=args.gene_list_path, @@ -616,14 +616,14 @@ def resolve_total_cells_for_split(): ) # Step 3.3: Instantiate the model - # PerformerEncoderOnly_Module model process: + # BioMod model process: # Input: (batch_size, num_genes) = (3, 27874) # ├─> Module division: (batch_size, num_modules, module_avg_size) ≈ (3, 500, 55) # ├─> Module Embedding: (batch_size, num_modules, base_dim) = (3, 500, 1024) # ├─> Random Masking (75%): Keep about 125 visible modules - # ├─> Performer Encoder: Process visible modules + # ├─> BioMod encoder: Process visible modules # └─> Reconstruction prediction: Output the reconstruction value of the masked module - model = PerformerEncoderOnly_Module(**model_params) + model = BioMod(**model_params) def configure_trainable_modules(model_obj): for param in model_obj.parameters(): @@ -721,8 +721,8 @@ def save_initialized_checkpoint_and_exit(model_obj): # Step 2.1/2.2 has been completed previously # Step 2.3: Create training and validation datasets - # ModuleSCDataset directly returns the gene expression value, and the module conversion is completed inside the model. - train_dataset = ModuleSCDataset( + # BioModSCDataset directly returns the gene expression value, and the module conversion is completed inside the model. + train_dataset = BioModSCDataset( data_path=args.data_path, module_sets=module_sets, # Pass module_sets to maintain compatibility, but no longer used in __getitem__ data=None, @@ -735,7 +735,7 @@ def save_initialized_checkpoint_and_exit(model_obj): cell_num=None, verbose=is_master ) - val_dataset = ModuleSCDataset( + val_dataset = BioModSCDataset( data_path=args.data_path, module_sets=module_sets, data=None, @@ -880,7 +880,7 @@ def any_rank_true(local_flag: bool) -> bool: # 1. Flat to Module: (3, 27874) -> (3, num_modules, module_avg_size) # 2. Module Embedding: -> (3, num_modules, 1024) # 3. Random Masking: mask out 75% of modules - # 4. Performer Encoder: Process visible modules + # 4. BioMod encoder: Process visible modules # 5. Reconstruction: Predict the masked module # Output: loss=MSE on masked module, preds=reconstructed value, mask=mask mark loss, preds, mask = model(data) diff --git a/BioMod/module_dataset.py b/BioMod/biomod_dataset.py similarity index 99% rename from BioMod/module_dataset.py rename to BioMod/biomod_dataset.py index 7252fc35f46a5eb7ccdb138e29351f162f87bc8a..011bacfdd2cfe6440a2472502ded87289dea7de7 100644 --- a/BioMod/module_dataset.py +++ b/BioMod/biomod_dataset.py @@ -25,7 +25,7 @@ h5py = None -def load_module_data( +def load_biomod_data( module_data_path: Optional[str] = None, module_data_dir: Optional[str] = None, gene_list_path: Optional[str] = None, @@ -219,7 +219,7 @@ def _convert_genes_to_indices(genes: list) -> list[int]: elif isinstance(data, list): modules = data else: - raise ValueError("module_data format is incorrect") + raise ValueError("biomod_data format is incorrect") module_sets = [] for p in modules: @@ -246,13 +246,13 @@ def _convert_genes_to_indices(genes: list) -> list[int]: raise ValueError("module_data_path or module_data_dir must be provided") -class ModuleSCDataset(Dataset): +class BioModSCDataset(Dataset): """ single cell data set Supports .npz (sparse CSR), .h5ad and .txt formats Note: This class no longer performs module conversion (redundant _flat_to_modules removed) - Module conversion is completed by the ModuleEmbedding layer inside the model to avoid repeated calculations + Module conversion is completed by the BioModEmbedding layer inside the model to avoid repeated calculations """ def __init__( diff --git a/BioMod/cell_annotation/module_data/module1_hallmarks.json b/BioMod/cell_annotation/biomod_data/biomod1_hallmarks.json similarity index 100% rename from BioMod/cell_annotation/module_data/module1_hallmarks.json rename to BioMod/cell_annotation/biomod_data/biomod1_hallmarks.json diff --git a/BioMod/cell_annotation/module_data/module2_reactome_2022_filtered.json b/BioMod/cell_annotation/biomod_data/biomod2_reactome_2022_filtered.json similarity index 100% rename from BioMod/cell_annotation/module_data/module2_reactome_2022_filtered.json rename to BioMod/cell_annotation/biomod_data/biomod2_reactome_2022_filtered.json diff --git a/BioMod/cell_annotation/module_data/load_module.py b/BioMod/cell_annotation/biomod_data/load_biomod.py similarity index 87% rename from BioMod/cell_annotation/module_data/load_module.py rename to BioMod/cell_annotation/biomod_data/load_biomod.py index 21d40e9703067e398ff2308e62d699495667fa32..37d6aee441ed3d5b5c2baab3e0ecb8e9736b1b45 100644 --- a/BioMod/cell_annotation/module_data/load_module.py +++ b/BioMod/cell_annotation/biomod_data/load_biomod.py @@ -29,8 +29,8 @@ def load_json_files(file1_path, file2_path): # hallmark_data, reactome_data = load_json_files( -# 'module1_hallmarks.json', -# 'module2_reactome_2022_filtered.json' +# 'biomod1_hallmarks.json', +# 'biomod2_reactome_2022_filtered.json' # ) # len(hallmark_data.keys()) # len(reactome_data.keys()) \ No newline at end of file diff --git a/BioMod/cell_annotation/performer_module_finetune.py b/BioMod/cell_annotation/biomod_finetune.py similarity index 91% rename from BioMod/cell_annotation/performer_module_finetune.py rename to BioMod/cell_annotation/biomod_finetune.py index 025bbc8ede900cf5cda7410503337e411f8bf652..f3bca0e8d65ebe5c29bc09df460776517a2f7abe 100644 --- a/BioMod/cell_annotation/performer_module_finetune.py +++ b/BioMod/cell_annotation/biomod_finetune.py @@ -2,12 +2,12 @@ # -*- coding: utf-8 -*- """ -End-to-end cell annotation fine-tuning for Performer Module. +End-to-end cell annotation fine-tuning for BioMod. This script is intentionally independent from the legacy two-stage pipeline: 1. No feature extraction to .npy files. -2. No imports from other scripts in performer_module/cell_annotation. -3. Reuses the pretrained Performer Module backbone directly. +2. No imports from other scripts in biomod/cell_annotation. +3. Reuses the pretrained BioMod backbone directly. Supported cell embedding heads: - module_mean: mean over restored module tokens @@ -48,26 +48,26 @@ from torch.utils.data.distributed import DistributedSampler SCRIPT_DIR = Path(__file__).resolve().parent -PERFORMER_MODULE_ROOT = SCRIPT_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = SCRIPT_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent REPO_ROOT = Path(__file__).resolve().parents[2] ASSET_DIR = REPO_ROOT / "model_dependencies" DOWNSTREAM_DATA_DIR = REPO_ROOT / "data" / "downstream" / "final_data" -WEIGHTS_DIR = REPO_ROOT / "weight" / "0810model" +WEIGHTS_DIR = REPO_ROOT / "weight" -if str(PERFORMER_MODULE_ROOT) not in sys.path: - sys.path.insert(0, str(PERFORMER_MODULE_ROOT)) +if str(BIOMOD_ROOT) not in sys.path: + sys.path.insert(0, str(BIOMOD_ROOT)) if str(SCLONG_ROOT) not in sys.path: sys.path.insert(0, str(SCLONG_ROOT)) -from models_performer_module import ( - migrate_legacy_module_config, - migrate_legacy_module_state_dict, +from models_biomod import ( + migrate_legacy_biomod_config, + migrate_legacy_biomod_state_dict, ) if TYPE_CHECKING: - from models_performer_module import PerformerEncoderOnly_Module + from models_biomod import BioMod logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") @@ -373,7 +373,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return self.out_layer(x) -class ModuleSelector(nn.Module): +class BioModSelector(nn.Module): """Per-module scorer: soft weighting during training, hard threshold during inference.""" def __init__(self, embed_dim: int): @@ -395,10 +395,10 @@ def forward( return x, scores -class PerformerModuleCellClassifier(nn.Module): +class BioModCellClassifier(nn.Module): def __init__( self, - backbone: "PerformerEncoderOnly_Module", + backbone: "BioMod", module_sets: List[List[int]], num_classes: int, head_type: str, @@ -430,13 +430,13 @@ def __init__( self.use_module_selector = use_module_selector self.selector_tau = 0.5 if use_module_selector: - self.module_selector = ModuleSelector(self.embed_dim) + self.module_selector = BioModSelector(self.embed_dim) else: self.module_selector = None self._selector_scores = None if self.head_type == "gene_mean": - module_idx, gene_idx, gene_counts = self._build_module_gene_index() + module_idx, gene_idx, gene_counts = self._build_biomod_gene_index() self.register_buffer("agg_module_idx", module_idx) self.register_buffer("agg_gene_idx", gene_idx) self.register_buffer("agg_gene_counts", gene_counts) @@ -454,12 +454,12 @@ def __init__( def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False): """Accept current module names and older serialized backbone names.""" return super().load_state_dict( - migrate_legacy_module_state_dict(state_dict), + migrate_legacy_biomod_state_dict(state_dict), strict=strict, assign=assign, ) - def _build_module_gene_index(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + def _build_biomod_gene_index(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: module_indices = [] gene_indices = [] gene_counts = torch.zeros(self.num_genes, dtype=torch.long) @@ -475,7 +475,7 @@ def _build_module_gene_index(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Te gene_counts, ) - def _restore_module_order(self, latent: torch.Tensor, ids_restore: torch.Tensor) -> torch.Tensor: + def _restore_biomod_order(self, latent: torch.Tensor, ids_restore: torch.Tensor) -> torch.Tensor: if self.use_cls: module_tokens = latent[:, 1:, :] else: @@ -483,7 +483,7 @@ def _restore_module_order(self, latent: torch.Tensor, ids_restore: torch.Tensor) gather_index = ids_restore.unsqueeze(-1).expand(-1, -1, module_tokens.shape[-1]) return torch.gather(module_tokens, dim=1, index=gather_index) - def _module_to_gene(self, module_tokens: torch.Tensor) -> torch.Tensor: + def _biomod_to_gene(self, module_tokens: torch.Tensor) -> torch.Tensor: batch_size, _, dim = module_tokens.shape gene_feats = torch.zeros( batch_size, @@ -519,7 +519,7 @@ def forward(self, x_model: torch.Tensor) -> torch.Tensor: if self.head_type == "cls": cell_emb = latent[:, 0, :] else: - module_tokens = self._restore_module_order(latent, ids_restore) + module_tokens = self._restore_biomod_order(latent, ids_restore) if self.module_selector is not None: module_tokens, self._selector_scores = self.module_selector( module_tokens, tau=self.selector_tau, hard=not self.training, @@ -531,7 +531,7 @@ def forward(self, x_model: torch.Tensor) -> torch.Tensor: elif self.head_type == "attn_pool": cell_emb, _ = self.pooler(module_tokens) elif self.head_type == "gene_mean": - gene_tokens = self._module_to_gene(module_tokens) + gene_tokens = self._biomod_to_gene(module_tokens) cell_emb = gene_tokens.mean(dim=1) else: raise ValueError(f"Unsupported head_type: {self.head_type}") @@ -540,13 +540,13 @@ def forward(self, x_model: torch.Tensor) -> torch.Tensor: return self.classifier(cell_emb) -def get_encoder_blocks(backbone: "PerformerEncoderOnly_Module") -> List[nn.Module]: +def get_encoder_blocks(backbone: "BioMod") -> List[nn.Module]: net = backbone.encoder.net if hasattr(net, "layers"): return list(net.layers) if hasattr(net, "blocks"): return list(net.blocks) - raise AttributeError("Cannot find encoder blocks on Performer backbone") + raise AttributeError("Cannot find encoder blocks on BioMod backbone") def set_requires_grad(module: nn.Module, flag: bool) -> None: @@ -555,7 +555,7 @@ def set_requires_grad(module: nn.Module, flag: bool) -> None: def configure_backbone_trainability( - backbone: "PerformerEncoderOnly_Module", + backbone: "BioMod", finetune_mode: str, unfreeze_last_n: int, train_pos_emb: bool, @@ -604,7 +604,7 @@ def configure_backbone_trainability( def build_optimizer( - model: PerformerModuleCellClassifier, + model: BioModCellClassifier, lr_head: float, lr_backbone: float, weight_decay: float, @@ -669,20 +669,20 @@ def resolve_batch_sizes(args, world_size: int) -> Tuple[int, int, int]: return train_batch_size, eval_batch_size, effective_global_batch -def infer_model_config(args, checkpoint: dict, pkl_config: Optional[dict], module_data: List[List[int]]) -> dict: - from models_performer_module import sanitize_performer_module_config +def infer_model_config(args, checkpoint: dict, pkl_config: Optional[dict], biomod_data: List[List[int]]) -> dict: + from models_biomod import sanitize_biomod_config if pkl_config is not None and isinstance(pkl_config, dict): - config = sanitize_performer_module_config( + config = sanitize_biomod_config( pkl_config, source=str(args.config_pkl), log_fn=logger.info, ) logger.info("Using config from PKL: %s", args.config_pkl) if "module_sets" in config: - module_data = config["module_sets"] + biomod_data = config["module_sets"] elif isinstance(checkpoint, dict) and "config" in checkpoint: - config = sanitize_performer_module_config( + config = sanitize_biomod_config( checkpoint["config"], source=str(resolve_path(args.performer_checkpoint)), log_fn=logger.info, @@ -695,7 +695,7 @@ def infer_model_config(args, checkpoint: dict, pkl_config: Optional[dict], modul ) return { - "module_sets": module_data, + "module_sets": biomod_data, "num_genes": config["num_genes"], "base_dim": config["base_dim"], "enc_depth": config["enc_depth"], @@ -722,9 +722,9 @@ def infer_model_config(args, checkpoint: dict, pkl_config: Optional[dict], modul } -def compute_module_family_ids(module_data_dir: str) -> List[int]: - """Read merged_modules.json, return family ID (0-3) for each module.""" - merged_path = Path(module_data_dir) / "merged_modules.json" +def compute_biomod_family_ids(module_data_dir: str) -> List[int]: + """Read merged_biomods.json, return family ID (0-3) for each module.""" + merged_path = Path(module_data_dir) / "merged_biomods.json" with open(merged_path) as f: modules = json.load(f) PREFIXES = [ @@ -744,32 +744,32 @@ def compute_module_family_ids(module_data_dir: str) -> List[int]: return family_ids -def load_backbone(args, device: torch.device) -> Tuple["PerformerEncoderOnly_Module", List[List[int]], dict]: - from models_performer_module import PerformerEncoderOnly_Module - from module_dataset import load_module_data +def load_backbone(args, device: torch.device) -> Tuple["BioMod", List[List[int]], dict]: + from models_biomod import BioMod + from biomod_dataset import load_biomod_data config_pkl = None config_path = resolve_path(args.config_pkl) if config_path.exists(): with open(config_path, "rb") as handle: - config_pkl = migrate_legacy_module_config(pickle.load(handle)) + config_pkl = migrate_legacy_biomod_config(pickle.load(handle)) else: logger.warning("Config PKL not found at %s; fallback to checkpoint config", config_path) ckpt_path = resolve_path(args.performer_checkpoint) checkpoint = torch.load(ckpt_path, map_location=device) - module_data = None + biomod_data = None if isinstance(config_pkl, dict) and "module_sets" in config_pkl: - module_data = config_pkl["module_sets"] - logger.info("Using module_sets from PKL (%d modules)", len(module_data)) + biomod_data = config_pkl["module_sets"] + logger.info("Using module_sets from PKL (%d modules)", len(biomod_data)) elif isinstance(checkpoint, dict) and isinstance(checkpoint.get("config"), dict) and "module_sets" in checkpoint["config"]: - module_data = checkpoint["config"]["module_sets"] - logger.info("Using module_sets from checkpoint config (%d modules)", len(module_data)) + biomod_data = checkpoint["config"]["module_sets"] + logger.info("Using module_sets from checkpoint config (%d modules)", len(biomod_data)) else: if not args.module_data_dir: raise ValueError("module_data_dir is required when module_sets are not stored in the PKL/checkpoint config") - module_data = load_module_data( + biomod_data = load_biomod_data( module_data_dir=str(resolve_path(args.module_data_dir)), gene_list_path=str(resolve_path(args.gene_list_path)), gene_mapping_path=str(resolve_path(args.gene_mapping_path)), @@ -779,18 +779,18 @@ def load_backbone(args, device: torch.device) -> Tuple["PerformerEncoderOnly_Mod if args.shuffle_module_genes: logger.info("Shuffling genes across modules with seed=%d", args.shuffle_seed) rng = random.Random(args.shuffle_seed) - module_sizes = [len(module) for module in module_data] - all_genes = [gene for module in module_data for gene in module] + module_sizes = [len(module) for module in biomod_data] + all_genes = [gene for module in biomod_data for gene in module] rng.shuffle(all_genes) shuffled = [] start = 0 for size in module_sizes: shuffled.append(all_genes[start:start + size]) start += size - module_data = shuffled + biomod_data = shuffled - model_config = infer_model_config(args, checkpoint, config_pkl, module_data) - module_data = model_config["module_sets"] + model_config = infer_model_config(args, checkpoint, config_pkl, biomod_data) + biomod_data = model_config["module_sets"] if "model_state_dict" in checkpoint: state_dict = checkpoint["model_state_dict"] @@ -807,7 +807,7 @@ def load_backbone(args, device: torch.device) -> Tuple["PerformerEncoderOnly_Mod model_config["base_dim"], model_config["enc_depth"], model_config["enc_heads"], - len(module_data), + len(biomod_data), use_cls_token, ) @@ -815,13 +815,13 @@ def load_backbone(args, device: torch.device) -> Tuple["PerformerEncoderOnly_Mod if getattr(args, "use_family_emb", False): pdd = getattr(args, "module_data_dir", None) if pdd: - module_family_ids = compute_module_family_ids(str(resolve_path(pdd))) + module_family_ids = compute_biomod_family_ids(str(resolve_path(pdd))) logger.info("Family embedding enabled: %d module family IDs computed", len(module_family_ids)) - backbone = PerformerEncoderOnly_Module( - module_sets=module_data, + backbone = BioMod( + module_sets=biomod_data, num_genes=model_config["num_genes"], - max_seq_len=len(module_data), + max_seq_len=len(biomod_data), base_dim=model_config["base_dim"], enc_depth=model_config["enc_depth"], enc_heads=model_config["enc_heads"], @@ -866,7 +866,7 @@ def load_backbone(args, device: torch.device) -> Tuple["PerformerEncoderOnly_Mod logger.warning("Unexpected keys: %s", unexpected_keys[:10]) backbone.to(device) - return backbone, module_data, model_config + return backbone, biomod_data, model_config def stratified_split( @@ -908,7 +908,7 @@ def stratified_train_val_split( @torch.no_grad() def evaluate( - model: PerformerModuleCellClassifier, + model: BioModCellClassifier, data_loader: DataLoader, preprocessor: DataPreprocessor, reindex_state: dict, @@ -948,7 +948,7 @@ def evaluate( def train_one_epoch( - model: PerformerModuleCellClassifier, + model: BioModCellClassifier, data_loader: DataLoader, preprocessor: DataPreprocessor, reindex_state: dict, @@ -1075,7 +1075,7 @@ def apply_dataset_preset(args) -> None: def parse_args(): - parser = argparse.ArgumentParser(description="End-to-end fine-tuning for Performer Module cell annotation") + parser = argparse.ArgumentParser(description="End-to-end fine-tuning for BioMod cell annotation") parser.add_argument("--seed", type=int, default=2021) parser.add_argument("--device", type=str, default="cuda") @@ -1117,13 +1117,13 @@ def parse_args(): ) parser.add_argument("--unfreeze_last_n", type=int, default=1) parser.add_argument("--train_pos_emb", action="store_true") - parser.add_argument("--train_module_embed", action="store_true") + parser.add_argument("--train_biomod_embed", "--train_module_embed", action="store_true", dest='train_module_embed') parser.add_argument("--use_family_emb", action="store_true", help="Add family-type embedding (Hallmarks/Reactome/Hotspot/WGCNA)") - parser.add_argument("--use_module_gate", action="store_true", - help="Add learnable sparse gate per module") - parser.add_argument("--use_module_selector", action="store_true", - help="Dynamic module selector with sparsity loss (soft train, hard inference)") + parser.add_argument("--use_biomod_gate", "--use_module_gate", action="store_true", + help="Add learnable sparse gate per module", dest='use_module_gate') + parser.add_argument("--use_biomod_selector", "--use_module_selector", action="store_true", + help="Dynamic module selector with sparsity loss (soft train, hard inference)", dest='use_module_selector') parser.add_argument("--selector_sparsity_weight", type=float, default=0.1, help="Lambda for sparsity loss on module selector scores") parser.add_argument("--selector_tau", type=float, default=0.5, @@ -1152,32 +1152,33 @@ def parse_args(): parser.add_argument("--min_cells", type=int, default=10) parser.add_argument("--max_cells", type=int, default=None) - parser.add_argument("--config_pkl", type=str, default=str(WEIGHTS_DIR / "mae_tost_70M_75mask_ec2.pkl")) + parser.add_argument("--config_pkl", type=str, default=str(WEIGHTS_DIR / "BioMod.pkl")) parser.add_argument( - "--performer_checkpoint", + "--biomod_checkpoint", "--performer_checkpoint", type=str, - default=str(WEIGHTS_DIR / "mae_tost_70M_75mask_ec2_epoch0.pth"), + default=str(WEIGHTS_DIR / "BioMod.pth"), + dest='performer_checkpoint', ) - parser.add_argument("--module_data_dir", type=str, default=str(ASSET_DIR / "module_data_all")) + parser.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=str(ASSET_DIR / "biomod_data_all"), dest='module_data_dir') parser.add_argument("--gene_list_path", type=str, default=str(ASSET_DIR / "selected_genes_27k.txt")) parser.add_argument("--gene_mapping_path", type=str, default=str(ASSET_DIR / "gene2ens.csv")) parser.add_argument("--gene2vec_data_path", type=str, default=str(ASSET_DIR / "selected_gene2vec_27k.npy")) parser.add_argument("--gene2ens_file", type=str, default=str(ASSET_DIR / "gene2ens.csv")) parser.add_argument("--gene2ens_column", type=str, default="Ensembl gene ID") parser.add_argument( - "--shuffle_module_genes", + "--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", help="Apply an extra downstream reshuffle on top of module_sets restored from PKL. " "Do not pass this when reproducing the pretrained backbone as-is.", - ) + dest='shuffle_module_genes') parser.add_argument( "--shuffle_seed", type=int, default=2021, - help="Random seed for the downstream extra reshuffle when --shuffle_module_genes is enabled.", + help="Random seed for the downstream extra reshuffle when --shuffle_biomod_genes is enabled.", ) - parser.add_argument("--output_dir", type=str, default="./ckpts_performer_module_finetune") + parser.add_argument("--output_dir", type=str, default="./ckpts_biomod_finetune") parser.add_argument("--run_name", type=str, default="") parser.add_argument( "--save_test_artifacts", @@ -1384,7 +1385,7 @@ def main(): train_module_embed=args.train_module_embed, ) - model = PerformerModuleCellClassifier( + model = BioModCellClassifier( backbone=backbone, module_sets=module_sets, num_classes=num_classes, diff --git a/BioMod/cell_annotation/cmd_finetune.md b/BioMod/cell_annotation/cmd_finetune.md index c6b2c1f7e0034ca01b917b3549dc9385e3e751a9..2904a602b3078ca2effda271f080f785d636570a 100644 --- a/BioMod/cell_annotation/cmd_finetune.md +++ b/BioMod/cell_annotation/cmd_finetune.md @@ -3,9 +3,9 @@ Run this command from the repository root in a CUDA/NCCL environment. Resource defaults resolve from the script location; explicit relative paths resolve from the working directory. ```bash -torchrun --nproc_per_node=4 BioMod/cell_annotation/performer_module_finetune.py \ ---config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ ---performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ +torchrun --nproc_per_node=4 BioMod/cell_annotation/biomod_finetune.py \ +--config_pkl weight/BioMod.pkl \ +--biomod_checkpoint weight/BioMod.pth \ --data_path data/downstream/final_data/Zheng68K.h5ad \ --label_column celltype \ --head_type attn_pool \ @@ -16,6 +16,6 @@ torchrun --nproc_per_node=4 BioMod/cell_annotation/performer_module_finetune.py --lr_backbone 1e-5 \ --batch_size 256 \ --epochs 300 \ ---module_data_dir model_dependencies/module_data_all \ +--biomod_data_dir model_dependencies/biomod_data_all \ --output_dir tutorial_runs/cell_annotation_zheng68k ``` diff --git a/BioMod/command.txt b/BioMod/command.txt index 35da82fc602192e5ef95aad354a780dcd5cdea03..69d2f025c9354ae66cb8c886a4be2c347744505a 100644 --- a/BioMod/command.txt +++ b/BioMod/command.txt @@ -1,10 +1,10 @@ # Run from the outer repository root in a Linux CUDA/NCCL environment. # Set BIOMOD_PRETRAIN_DATA to your preprocessed per-cell NPZ directory. # The pretraining corpus is not included in this repository. -conda activate performer_module +conda activate biomod : "${BIOMOD_PRETRAIN_DATA:?Set BIOMOD_PRETRAIN_DATA to the pretraining NPZ directory}" torchrun --standalone --nproc_per_node="${NPROC_PER_NODE:-1}" \ - BioMod/performer_module.py \ + BioMod/biomod.py \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --cell_num 4000000 \ --epoch 15 \ @@ -15,11 +15,11 @@ torchrun --standalone --nproc_per_node="${NPROC_PER_NODE:-1}" \ --log_every 100 \ --mask_ratio 0.75 \ --data_path "$BIOMOD_PRETRAIN_DATA" \ - --module_data_dir model_dependencies/module_data_all \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ --ckpt_dir tutorial_runs/pretraining \ - --model_name performer_module_pretraining \ + --model_name biomod_pretraining \ --fusion_method multiply \ --loss_mode gene_mean \ --decoder_type simple \ diff --git a/BioMod/drug_combination/README.md b/BioMod/drug_combination/README.md index c50393bbba008bc76515dd7c0bd13186619e5afb..15060fdd752dc1ed776cea859be9e9a192bc1141 100644 --- a/BioMod/drug_combination/README.md +++ b/BioMod/drug_combination/README.md @@ -1,19 +1,19 @@ # Drug Combination Synergy Prediction with BioMod This task predicts drug-combination synergy using BioMod cell embeddings from -the PerformerModule encoder and GCN-based drug molecular graph encoders. Use the +the BioMod encoder and GCN-based drug molecular graph encoders. Use the [BioMod model weights and matching configuration](../../README.md#biomod-model-weights). It is adapted from scLong's drug-combination implementation (DeepDDs), replacing the scfm/geneformer cell-line representation with -PerformerModule. +BioMod. ## Model Architecture | Component | Description | | --- | --- | | Drug branch | Three PyG GCNConv layers (78 -> 78 -> 156 -> 312), `global_max_pool`, and two fully connected layers to 128 dimensions | -| Cell branch | Pretrained PerformerModule encoder, module mean pooling, and an MLP to 128 dimensions | +| Cell branch | Pretrained BioMod encoder, module mean pooling, and an MLP to 128 dimensions | | Fusion layer | `concat(drug1, drug2, cell) = 384 -> MLP -> 2-class prediction` | The drug1 and drug2 branches share weights, matching scLong. @@ -55,20 +55,20 @@ pip install RDKit -i https://pypi.tuna.tsinghua.edu.cn/simple ### Basic Run (Domain Generalization Mode, Default) ```bash -python BioMod/drug_combination/train_drug_combination_performer_module.py \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/drug_combination/train_drug_combination_biomod.py \ + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --data_dir data/downstream/final_data/drug_combination_synergy/data_summary ``` ### Custom Paths ```bash -python BioMod/drug_combination/train_drug_combination_performer_module.py \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/drug_combination/train_drug_combination_biomod.py \ + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt\ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_path model_dependencies/selected_gene2vec_27k.npy \ @@ -87,20 +87,20 @@ Reference note: `1e-4 500 0.6824301861702127`. ### Five-Fold Cross-Validation (Disable Domain Generalization) ```bash -python BioMod/drug_combination/train_drug_combination_performer_module.py \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/drug_combination/train_drug_combination_biomod.py \ + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --no_domain_gen ``` ### Force Reprocessing After Changing the Model or Data ```bash -python BioMod/drug_combination/train_drug_combination_performer_module.py \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/drug_combination/train_drug_combination_biomod.py \ + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --force_preprocess ``` @@ -109,8 +109,8 @@ python BioMod/drug_combination/train_drug_combination_performer_module.py \ | Argument | Default | Description | | --- | --- | --- | | `--checkpoint_path` | Required | Path to the BioMod model weights | -| `--hyper_params_path` | Required | Path to the PerformerModule hyper-parameter pkl file | -| `--module_data_dir` | None | Directory containing module data files | +| `--hyper_params_path` | Required | Path to the BioMod hyper-parameter pkl file | +| `--biomod_data_dir` | None | Directory containing module data files | | `--data_dir` | `data/downstream/final_data/drug_combination_synergy/data_summary` | Data directory | | `--validation_label_file` | `test_data/independent_input.csv` | Validation labels for checkpoint selection; legacy alias: `--test_label_file` | | `--validation_cell_file` | `test_data/independent_cell_features_954.csv` | Validation cell features; legacy alias: `--test_cell_file` | @@ -120,7 +120,7 @@ python BioMod/drug_combination/train_drug_combination_performer_module.py \ | `--n_folds` | 5 | Number of folds | | `--domain_gen` | True | Domain generalization mode: train on the full training set and select checkpoints using a separate validation set | | `--eval_interval` | 5 | Evaluate every N epochs | -| `--pool_method` | mean | PerformerModule pooling method (`mean`, `max`, or `concat`) | +| `--pool_method` | mean | BioMod pooling method (`mean`, `max`, or `concat`) | | `--cache_emb` | True | Cache cell embeddings to a `.npy` file | | `--force_preprocess` | False | Force regeneration of the PyG dataset | @@ -130,7 +130,7 @@ Results are saved under `--output_dir` (default: `BioMod/drug_combination/result - `best_model_fold{i}.pt` - model weights selected by the highest validation AUC for each fold - `fpr_tpr_fold{i}.pkl` - validation FPR/TPR curve data for drawing ROC curves -- `cell_emb_cache_{pool_method}.npy` - cached PerformerModule cell embeddings +- `cell_emb_cache_{pool_method}.npy` - cached BioMod cell embeddings ## Evaluation Metrics diff --git a/BioMod/drug_combination/train_drug_combination_performer_module.py b/BioMod/drug_combination/train_drug_combination_biomod.py similarity index 87% rename from BioMod/drug_combination/train_drug_combination_performer_module.py rename to BioMod/drug_combination/train_drug_combination_biomod.py index ddcb98f2e8ed45171a9f40b9a8a0b1abfcad41ba..a721dbf9f4e85d6be01dd18f5b813dda5af363b9 100644 --- a/BioMod/drug_combination/train_drug_combination_performer_module.py +++ b/BioMod/drug_combination/train_drug_combination_biomod.py @@ -1,18 +1,18 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- """ -Drug Combination Synergy Prediction with PerformerModule cell embeddings. +Drug Combination Synergy Prediction with BioMod cell embeddings. Aligned with scLong/drug_combination (DeepDDs) architecture: - Drug branch: PyG GCNConv + global_max_pool (shared weights for drug1 & drug2) - - Cell branch: PerformerModule pretrained encoder -> MLP + - Cell branch: BioMod pretrained encoder -> MLP - Fusion: concat(drug1, drug2, cell) -> MLP -> 2-class classification Usage: # Run from the repository root. - python BioMod/drug_combination/train_drug_combination_performer_module.py \ + python BioMod/drug_combination/train_drug_combination_biomod.py \ --checkpoint_path --hyper_params_path \ - --module_data_dir model_dependencies/module_data_all \ + --biomod_data_dir model_dependencies/biomod_data_all \ --data_dir data/downstream/final_data/drug_combination_synergy/data_summary """ @@ -54,35 +54,35 @@ # ===================== Path setup ===================== SCRIPT_DIR = Path(__file__).resolve().parent -PERFORMER_MODULE_ROOT = SCRIPT_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = SCRIPT_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent REPO_ROOT = Path(__file__).resolve().parents[2] ASSET_DIR = REPO_ROOT / "model_dependencies" DOWNSTREAM_DATA_DIR = REPO_ROOT / "data" / "downstream" / "final_data" -WEIGHTS_DIR = REPO_ROOT / "weight" / "0810model" +WEIGHTS_DIR = REPO_ROOT / "weight" -if str(PERFORMER_MODULE_ROOT) not in sys.path: - sys.path.insert(0, str(PERFORMER_MODULE_ROOT)) +if str(BIOMOD_ROOT) not in sys.path: + sys.path.insert(0, str(BIOMOD_ROOT)) if str(SCLONG_ROOT) not in sys.path: sys.path.insert(0, str(SCLONG_ROOT)) import importlib.util -# Import PerformerModule model -_models_path = os.path.join(PERFORMER_MODULE_ROOT, 'models_performer_module.py') -_spec = importlib.util.spec_from_file_location("models_performer_module", _models_path) +# Import BioMod model +_models_path = os.path.join(BIOMOD_ROOT, 'models_biomod.py') +_spec = importlib.util.spec_from_file_location("models_biomod", _models_path) _models_mod = importlib.util.module_from_spec(_spec) _spec.loader.exec_module(_models_mod) -PerformerEncoderOnly_Module = _models_mod.PerformerEncoderOnly_Module -sanitize_performer_module_config = _models_mod.sanitize_performer_module_config +BioMod = _models_mod.BioMod +sanitize_biomod_config = _models_mod.sanitize_biomod_config -# Import module_dataset -_dataset_path = os.path.join(PERFORMER_MODULE_ROOT, 'module_dataset.py') -_spec2 = importlib.util.spec_from_file_location("module_dataset", _dataset_path) +# Import biomod_dataset +_dataset_path = os.path.join(BIOMOD_ROOT, 'biomod_dataset.py') +_spec2 = importlib.util.spec_from_file_location("biomod_dataset", _dataset_path) _module_mod = importlib.util.module_from_spec(_spec2) _spec2.loader.exec_module(_module_mod) -load_module_data = _module_mod.load_module_data +load_biomod_data = _module_mod.load_biomod_data # ===================== Utilities ===================== @@ -111,19 +111,19 @@ def reindex_tensor_universal(tensor, index_positions, dim, filler="0", device="c return reindexed -# ===================== PerformerModule Loading ===================== +# ===================== BioMod Loading ===================== -def load_performer_module_model( +def load_biomod_model( checkpoint_path, hyper_params_path, module_data_dir, module_data_path, gene_list_path, gene_mapping_path, gene2vec_path, device, shuffle_module_genes=False, shuffle_seed=2021, ): - print_sys(f"Loading PerformerModule model from: {checkpoint_path}") + print_sys(f"Loading BioMod model from: {checkpoint_path}") with open(hyper_params_path, 'rb') as f: model_params = pickle.load(f) model_params.pop('module_embed_chunk_padded_gene_budget', None) - model_params = sanitize_performer_module_config( + model_params = sanitize_biomod_config( model_params, source=hyper_params_path, log_fn=print_sys, ) @@ -133,7 +133,7 @@ def load_performer_module_model( if 'module_sets' not in model_params or model_params.get('module_sets') is None: if module_data_dir or module_data_path: - module_sets = load_module_data( + module_sets = load_biomod_data( module_data_path=module_data_path, module_data_dir=module_data_dir, gene_list_path=gene_list_path, @@ -143,8 +143,8 @@ def load_performer_module_model( model_params['module_sets'] = module_sets else: raise ValueError( - "Model needs module_sets but neither --module_data_dir " - "nor --module_data_path provided" + "Model needs module_sets but neither --biomod_data_dir " + "nor --biomod_data_path provided" ) if shuffle_module_genes: @@ -164,7 +164,7 @@ def load_performer_module_model( model_params['module_sets'] = shuffled_module_sets print_sys(f"Shuffled {len(all_gene_indices)} genes across {len(shuffled_module_sets)} modules") - model = PerformerEncoderOnly_Module(**model_params).to(device) + model = BioMod(**model_params).to(device) checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) state_dict = checkpoint.get('model_state_dict', checkpoint) @@ -173,7 +173,7 @@ def load_performer_module_model( if 'go_conv' not in k and 'G_go' not in k.lower() } model.load_state_dict(filtered, strict=True) - print_sys("Loaded pretrained PerformerModule weights.") + print_sys("Loaded pretrained BioMod weights.") model.eval() for p in model.parameters(): @@ -182,7 +182,7 @@ def load_performer_module_model( return model, len(model_params['module_sets']), model_params.get('base_dim', 1024) -def build_performer_module_gene_mapping(symbol2ens_path, gene_list_path, expr_genes): +def build_biomod_gene_mapping(symbol2ens_path, gene_list_path, expr_genes): gene2ens = pd.read_csv(symbol2ens_path, index_col=0) symbol2ens = gene2ens["Ensembl gene ID"].to_dict() @@ -191,15 +191,15 @@ def build_performer_module_gene_mapping(symbol2ens_path, gene_list_path, expr_ge input_mapping = {gid: i for i, gid in enumerate(input_genes)} with open(gene_list_path, "r") as f: - performer_genes = [line.rstrip("\n") for line in f.readlines()] - performer_genes_pad = performer_genes + ["PAD"] + biomod_genes = [line.rstrip("\n") for line in f.readlines()] + biomod_genes_pad = biomod_genes + ["PAD"] - performer_index_positions = [ - input_mapping.get(gid, input_gene_num) for gid in performer_genes_pad + biomod_index_positions = [ + input_mapping.get(gid, input_gene_num) for gid in biomod_genes_pad ] - intersect = np.intersect1d(input_genes, performer_genes) - print_sys(f"Target and PerformerModule genes intersect: {len(intersect)}") - return input_mapping, performer_index_positions + intersect = np.intersect1d(input_genes, biomod_genes) + print_sys(f"Target and BioMod genes intersect: {len(intersect)}") + return input_mapping, biomod_index_positions def build_cell_embeddings( @@ -208,14 +208,14 @@ def build_cell_embeddings( module_data_path, device, pool_method="mean", shuffle_module_genes=False, shuffle_seed=2021, ): - model, num_modules, encoder_embed_dim = load_performer_module_model( + model, num_modules, encoder_embed_dim = load_biomod_model( checkpoint_path, hyper_params_path, module_data_dir, module_data_path, gene_list_path, gene_mapping_path, gene2vec_path, device, shuffle_module_genes=shuffle_module_genes, shuffle_seed=shuffle_seed, ) gene_columns = list(cell_features_df.columns) - _, performer_index_positions = build_performer_module_gene_mapping( + _, biomod_index_positions = build_biomod_gene_mapping( symbol2ens_path, gene_list_path, gene_columns, ) @@ -225,10 +225,10 @@ def build_cell_embeddings( with torch.no_grad(): for cell_line_id, row in cell_features_df.iterrows(): x = torch.tensor(row.values[None, :], dtype=torch.float32, device=device) - x_performer = reindex_tensor_universal( - x, performer_index_positions, dim=1, filler="0", device=device, + x_biomod = reindex_tensor_universal( + x, biomod_index_positions, dim=1, filler="0", device=device, ) - out = model(x_performer, mask_ratio=0.0, return_encodings=True) + out = model(x_biomod, mask_ratio=0.0, return_encodings=True) if 'module_encodings' in out: module_enc = out['module_encodings'] @@ -251,7 +251,7 @@ def build_cell_embeddings( first_emb_dim = cell_emb_np.shape[0] print_sys( - f"Built PerformerModule embeddings for {len(cell_emb_dict)} cell lines, " + f"Built BioMod embeddings for {len(cell_emb_dict)} cell lines, " f"dim={first_emb_dim}, pool_method={pool_method}" ) return cell_emb_dict, first_emb_dim @@ -319,7 +319,7 @@ class TestbedDataset(InMemoryDataset): """ PyG InMemoryDataset for drug combination prediction. Same pattern as scLong/drug_combination/utils_test.py::TestbedDataset, - but uses PerformerModule cell embeddings instead of scfm/geneformer. + but uses BioMod cell embeddings instead of scfm/geneformer. """ def __init__( @@ -412,7 +412,7 @@ class GCNNet(torch.nn.Module): Drug branch: 3-layer GCNConv (78->78->156->312) + global_max_pool + 2 FC layers Drug1 and drug2 share weights (drug2 uses drug1_conv* layers, same as scLong). - Cell branch: PerformerModule embedding -> MLP -> output_dim + Cell branch: BioMod embedding -> MLP -> output_dim Fusion: concat(drug1, drug2, cell) -> fc1(256) -> fc2(128) -> out(2) """ @@ -432,7 +432,7 @@ def __init__( self.drug1_fc_g1 = nn.Linear(num_features_xd * 4, num_features_xd * 2) self.drug1_fc_g2 = nn.Linear(num_features_xd * 2, output_dim) - # Cell branch (PerformerModule embedding -> MLP) + # Cell branch (BioMod embedding -> MLP) self.reduction = nn.Sequential( nn.Linear(cell_emb_dim, 512), nn.ReLU(), @@ -584,18 +584,18 @@ def load_cell_features(cell_file): def main(): parser = argparse.ArgumentParser( - description='Drug Combination Synergy Prediction with PerformerModule' + description='Drug Combination Synergy Prediction with BioMod' ) - # PerformerModule model args + # BioMod model args parser.add_argument('--checkpoint_path', type=str, required=True, - help='Path to PerformerModule pretrained checkpoint') + help='Path to BioMod pretrained checkpoint') parser.add_argument('--hyper_params_path', type=str, required=True, - help='Path to PerformerModule hyper parameters pkl') - parser.add_argument('--module_data_dir', type=str, default=None, - help='Directory containing module data files') - parser.add_argument('--module_data_path', type=str, default=None, - help='Path to single module data file') + help='Path to BioMod hyper parameters pkl') + parser.add_argument('--biomod_data_dir', '--module_data_dir', type=str, default=None, + help='Directory containing module data files', dest='module_data_dir') + parser.add_argument('--biomod_data_path', '--module_data_path', type=str, default=None, + help='Path to single module data file', dest='module_data_path') parser.add_argument('--gene_list_path', type=str, default=str(ASSET_DIR / 'selected_genes_27k.txt')) parser.add_argument('--gene_mapping_path', type=str, @@ -606,8 +606,8 @@ def main(): default=str(ASSET_DIR / 'gene2ens.csv')) parser.add_argument('--pool_method', type=str, default='mean', choices=['mean', 'max', 'concat']) - parser.add_argument('--shuffle_module_genes', action='store_true', - help='Disrupt the gene allocation within the module (used for comparative experiments to verify the effectiveness of module division)') + parser.add_argument('--shuffle_biomod_genes', '--shuffle_module_genes', action='store_true', + help='Disrupt the gene allocation within the module (used for comparative experiments to verify the effectiveness of module division)', dest='shuffle_module_genes') parser.add_argument('--shuffle_seed', type=int, default=2021, help='The random seed used when scrambling the module gene (needs to be consistent with the value used during pre-training)') @@ -638,7 +638,7 @@ def main(): default=os.path.join(SCRIPT_DIR, 'results')) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--cache_emb', action='store_true', default=True, - help='Cache PerformerModule cell embeddings to .npy') + help='Cache BioMod cell embeddings to .npy') parser.add_argument('--no_cache_emb', dest='cache_emb', action='store_false') parser.add_argument('--force_preprocess', action='store_true', default=False, help='Force re-creation of PyG .pt datasets') @@ -656,7 +656,7 @@ def main(): os.makedirs(args.output_dir, exist_ok=True) - # -------- Step 1: Build PerformerModule cell embeddings -------- + # -------- Step 1: Build BioMod cell embeddings -------- train_cell_file = os.path.join(args.data_dir, args.cell_file) train_cell_df = load_cell_features(train_cell_file) print_sys(f'Loaded train cell features: {train_cell_df.shape}') @@ -737,12 +737,12 @@ def main(): labels_train = train_labels['label'].tolist() train_d1_dataset = TestbedDataset( - root=args.data_dir, dataset='train_drug1_performer_module', + root=args.data_dir, dataset='train_drug1_biomod', xd=drug1_train, xt=cells_train, y=labels_train, cell_emb_dict=cell_emb_dict, smile_graph=smile_graph, ) train_d2_dataset = TestbedDataset( - root=args.data_dir, dataset='train_drug2_performer_module', + root=args.data_dir, dataset='train_drug2_biomod', xd=drug2_train, xt=cells_train, y=labels_train, cell_emb_dict=cell_emb_dict, smile_graph=smile_graph, ) @@ -755,12 +755,12 @@ def main(): labels_validation = validation_labels['label'].tolist() validation_d1_dataset = TestbedDataset( - root=args.data_dir, dataset='test_drug1_performer_module', + root=args.data_dir, dataset='test_drug1_biomod', xd=drug1_validation, xt=cells_validation, y=labels_validation, cell_emb_dict=cell_emb_dict, smile_graph=smile_graph, ) validation_d2_dataset = TestbedDataset( - root=args.data_dir, dataset='test_drug2_performer_module', + root=args.data_dir, dataset='test_drug2_biomod', xd=drug2_validation, xt=cells_validation, y=labels_validation, cell_emb_dict=cell_emb_dict, smile_graph=smile_graph, ) diff --git a/BioMod/drug_response/cmd.txt b/BioMod/drug_response/cmd.txt index 88b89a1c07ecd1132eab366c1389dc73735f6b87..342ec1e657ef03730dc300f31cdac4e51b394125 100644 --- a/BioMod/drug_response/cmd.txt +++ b/BioMod/drug_response/cmd.txt @@ -1,10 +1,10 @@ # Run from the repository root. -python BioMod/drug_response/train_drug_performer_module.py \ +python BioMod/drug_response/train_drug_biomod.py \ --gpu_id 0 \ --data_path data/downstream/final_data/single_drug_response/data \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl \ --epochs 300 \ - --module_data_dir model_dependencies/module_data_all \ + --biomod_data_dir model_dependencies/biomod_data_all \ --save_dir tutorial_runs/drug_response \ --pool_method max diff --git a/BioMod/drug_response/train_drug_performer_module.py b/BioMod/drug_response/train_drug_biomod.py similarity index 86% rename from BioMod/drug_response/train_drug_performer_module.py rename to BioMod/drug_response/train_drug_biomod.py index 9d049e28baa14805b4060d15605135e14964279d..c480cc2a7ccc67bdaf8da420f3104f4cd24c0431 100644 --- a/BioMod/drug_response/train_drug_performer_module.py +++ b/BioMod/drug_response/train_drug_biomod.py @@ -1,20 +1,20 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- """ -Drug response prediction: using drug molecular structure + PerformerModule cell embedding +Drug response prediction: using drug molecular structure + BioMod cell embedding Architecture: - Drug branch: 3-layer GCN + residual connection + double pooling -- Cell branch: extract module-level features from pre-trained PerformerModule +- Cell branch: extract module-level features from pre-trained BioMod - Fusion layer: attention mechanism + deep MLP - Optimization strategy: AdamW + cosine annealing + early stopping How to use: # Run from the repository root; see drug_response/cmd.txt for a full example. - python BioMod/drug_response/train_drug_performer_module.py \ + python BioMod/drug_response/train_drug_biomod.py \ --gpu_id 0 --epochs 300 \ - --checkpoint_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl + --checkpoint_path weight/BioMod.pth \ + --hyper_params_path weight/BioMod.pkl """ import argparse @@ -27,7 +27,7 @@ REPO_ROOT = Path(__file__).resolve().parents[2] ASSET_DIR = REPO_ROOT / "model_dependencies" DOWNSTREAM_DATA_DIR = REPO_ROOT / "data" / "downstream" / "final_data" -WEIGHTS_DIR = REPO_ROOT / "weight" / "0810model" +WEIGHTS_DIR = REPO_ROOT / "weight" from typing import Dict, List, Tuple, Union, Optional import hickle as hkl @@ -39,7 +39,7 @@ from scipy.stats import pearsonr from torch.utils.data import DataLoader, Dataset -# Import PerformerModule model +# Import BioMod model project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if project_root not in sys.path: sys.path.insert(0, project_root) @@ -47,26 +47,26 @@ # Use direct path import to avoid conflicts with files with the same name import importlib.util -performer_module_dir = project_root +biomod_dir = project_root -# import models_performer_module -models_path = os.path.join(performer_module_dir, 'models_performer_module.py') +# import models_biomod +models_path = os.path.join(biomod_dir, 'models_biomod.py') if not os.path.exists(models_path): raise FileNotFoundError(f"Model file not found: {models_path}") -spec = importlib.util.spec_from_file_location("models_performer_module", models_path) -models_performer_module = importlib.util.module_from_spec(spec) -spec.loader.exec_module(models_performer_module) -PerformerEncoderOnly_Module = models_performer_module.PerformerEncoderOnly_Module -sanitize_performer_module_config = models_performer_module.sanitize_performer_module_config - -# Import module_dataset -dataset_path = os.path.join(performer_module_dir, 'module_dataset.py') +spec = importlib.util.spec_from_file_location("models_biomod", models_path) +models_biomod = importlib.util.module_from_spec(spec) +spec.loader.exec_module(models_biomod) +BioMod = models_biomod.BioMod +sanitize_biomod_config = models_biomod.sanitize_biomod_config + +# Import biomod_dataset +dataset_path = os.path.join(biomod_dir, 'biomod_dataset.py') if not os.path.exists(dataset_path): raise FileNotFoundError(f"Dataset file not found: {dataset_path}") -spec = importlib.util.spec_from_file_location("module_dataset", dataset_path) -module_dataset_module = importlib.util.module_from_spec(spec) -spec.loader.exec_module(module_dataset_module) -load_module_data = module_dataset_module.load_module_data +spec = importlib.util.spec_from_file_location("biomod_dataset", dataset_path) +biomod_dataset_module = importlib.util.module_from_spec(spec) +spec.loader.exec_module(biomod_dataset_module) +load_biomod_data = biomod_dataset_module.load_biomod_data # ===================== Utility functions ===================== @@ -105,9 +105,9 @@ def reindex_tensor_universal( return reindexed -# ===================== PerformerModule related ===================== +# ===================== BioMod related ===================== -def load_performer_module_model( +def load_biomod_model( checkpoint_path: str, hyper_params_path: str, module_data_dir: Optional[str], @@ -118,16 +118,16 @@ def load_performer_module_model( device: torch.device, shuffle_module_genes: bool = False, seed: int = 0, -) -> PerformerEncoderOnly_Module: - """Load a pretrained PerformerModule model""" - print_sys(f"Loading PerformerModule model from: {checkpoint_path}") +) -> BioMod: + """Load a pretrained BioMod model""" + print_sys(f"Loading BioMod model from: {checkpoint_path}") # Load model hyperparameters print_sys(f"Loading hyper parameters from: {hyper_params_path}") with open(hyper_params_path, 'rb') as f: model_params = pickle.load(f) model_params.pop('module_embed_chunk_padded_gene_budget', None) - model_params = sanitize_performer_module_config( + model_params = sanitize_biomod_config( model_params, source=hyper_params_path, log_fn=print_sys, @@ -150,7 +150,7 @@ def load_performer_module_model( if 'module_sets' not in model_params or model_params.get('module_sets') is None: if module_data_dir or module_data_path: print_sys("Loading module data...") - module_sets = load_module_data( + module_sets = load_biomod_data( module_data_path=module_data_path, module_data_dir=module_data_dir, gene_list_path=gene_list_path, @@ -159,7 +159,7 @@ def load_performer_module_model( ) model_params['module_sets'] = module_sets else: - raise ValueError("Model requires module_sets, but --module_data_dir or --module_data_path was not provided") + raise ValueError("Model requires module_sets, but --biomod_data_dir or --biomod_data_path was not provided") else: module_sets = model_params['module_sets'] print_sys(f"Using module_sets from hyper parameters ({len(module_sets)} modules)") @@ -191,11 +191,11 @@ def load_performer_module_model( model_params['module_sets'] = module_sets print_sys(f"Shuffled {len(all_gene_indices)} genes across {len(module_sets)} modules") - print_sys(f"PerformerModule config: base_dim={model_params.get('base_dim', 'N/A')}, " + print_sys(f"BioMod config: base_dim={model_params.get('base_dim', 'N/A')}, " f"num_modules={len(module_sets)}") # Build model - model = PerformerEncoderOnly_Module(**model_params).to(device) + model = BioMod(**model_params).to(device) # Load weights print_sys(f"Loading checkpoint from: {checkpoint_path}") @@ -216,7 +216,7 @@ def load_performer_module_model( # Use strict=True to ensure the weights match exactly (except for filtered GO parameters) model.load_state_dict(filtered_state_dict, strict=True) - print_sys("Loaded pretrained PerformerModule weights.") + print_sys("Loaded pretrained BioMod weights.") # freeze model model.eval() @@ -226,7 +226,7 @@ def load_performer_module_model( return model, len(module_sets), model_params.get('base_dim', 1024) -def build_performer_module_gene_mapping( +def build_biomod_gene_mapping( symbol2ens_path: str, gene_list_path: str, expr_genes: List[str], @@ -240,18 +240,18 @@ def build_performer_module_gene_mapping( input_mapping = {gid: i for i, gid in enumerate(input_genes)} with open(gene_list_path, "r") as f: - performer_genes = [line.rstrip("\n") for line in f.readlines()] - performer_genes_pad = performer_genes + ["PAD"] + biomod_genes = [line.rstrip("\n") for line in f.readlines()] + biomod_genes_pad = biomod_genes + ["PAD"] - performer_index_positions = [ - input_mapping.get(gid, input_gene_num) for gid in performer_genes_pad + biomod_index_positions = [ + input_mapping.get(gid, input_gene_num) for gid in biomod_genes_pad ] - intersect = np.intersect1d(input_genes, performer_genes) - print_sys(f"Target and PerformerModule genes intersect: {len(intersect)}") - return input_mapping, performer_index_positions + intersect = np.intersect1d(input_genes, biomod_genes) + print_sys(f"Target and BioMod genes intersect: {len(intersect)}") + return input_mapping, biomod_index_positions -def build_cell_embeddings_with_performer_module( +def build_cell_embeddings_with_biomod( gexpr_feature: pd.DataFrame, symbol2ens_path: str, gene_list_path: str, @@ -267,7 +267,7 @@ def build_cell_embeddings_with_performer_module( seed: int = 0, ) -> Tuple[Dict[str, np.ndarray], int]: """ - Use PerformerModule to construct embedding for each cell line + Use BioMod to construct embedding for each cell line Args: pool_method: pooling method, 'mean', 'max', 'cls', or 'concat' @@ -277,7 +277,7 @@ def build_cell_embeddings_with_performer_module( - 'concat': concatenate all modules (the dimension will be very large) use_gene2vec: automatically restored by pre-trained PKL, no longer covered downstream """ - model, num_modules, encoder_embed_dim = load_performer_module_model( + model, num_modules, encoder_embed_dim = load_biomod_model( checkpoint_path=checkpoint_path, hyper_params_path=hyper_params_path, module_data_dir=module_data_dir, @@ -290,7 +290,7 @@ def build_cell_embeddings_with_performer_module( seed=seed, ) - _, performer_index_positions = build_performer_module_gene_mapping( + _, biomod_index_positions = build_biomod_gene_mapping( symbol2ens_path=symbol2ens_path, gene_list_path=gene_list_path, expr_genes=list(gexpr_feature.columns), @@ -302,12 +302,12 @@ def build_cell_embeddings_with_performer_module( with torch.no_grad(): for cell_line_id, row in gexpr_feature.iterrows(): x = torch.tensor(row.values[None, :], dtype=torch.float32, device=device) - x_performer = reindex_tensor_universal( - x, performer_index_positions, dim=1, filler="0", device=device + x_biomod = reindex_tensor_universal( + x, biomod_index_positions, dim=1, filler="0", device=device ) # Extract features through encoder (without masking, mask_ratio=0.0) - out = model(x_performer, mask_ratio=0.0, return_encodings=True) + out = model(x_biomod, mask_ratio=0.0, return_encodings=True) # Extract module encodings if 'module_encodings' in out: @@ -318,7 +318,7 @@ def build_cell_embeddings_with_performer_module( if model.use_cls: # Use CLS token (in latent, but module_encodings does not contain CLS) # Need to get the complete latent from forward_encoder - enc_out = model.forward_encoder(x_performer, mask_ratio=0.0) + enc_out = model.forward_encoder(x_biomod, mask_ratio=0.0) latent = enc_out[0] if model.use_cls: cell_emb = latent[:, 0, :] # (1, encoder_embed_dim) @@ -345,7 +345,7 @@ def build_cell_embeddings_with_performer_module( first_emb_dim = cell_emb_np.shape[0] print_sys( - f"Built PerformerModule embeddings for {len(cell_emb_dict)} cell lines, " + f"Built BioMod embeddings for {len(cell_emb_dict)} cell lines, " f"dim={first_emb_dim}, pool_method={pool_method}" ) return cell_emb_dict, first_emb_dim @@ -525,8 +525,8 @@ def feature_extract( # ===================== Dataset ===================== -class DrugPerformerModuleDataset(Dataset): - """Drug + PerformerModule Dataset""" +class DrugBioModDataset(Dataset): + """Drug + BioMod Dataset""" def __init__( self, @@ -550,7 +550,7 @@ def __getitem__(self, idx: int): drug_feat = torch.from_numpy(feat).float() drug_adj = torch.from_numpy(adj).float() - # PerformerModule cell embedding + # BioMod cell embedding cell_line_id = self.index_list[idx][0] cell_emb_np = self.cell_emb_dict[cell_line_id] cell_emb = torch.from_numpy(cell_emb_np).float() @@ -595,13 +595,13 @@ def forward(self, x: torch.Tensor, adj: torch.Tensor) -> torch.Tensor: return out -class DrugPerformerModuleModel(nn.Module): +class DrugBioModModel(nn.Module): """ - Drugs + PerformerModule Predictive Model + Drugs + BioMod Predictive Model Architecture: - Drug branch: 3-layer GraphConv + residual connection + double pooling - - Cell branch: 2-layer MLP (processing PerformerModule module embeddings) + - Cell branch: 2-layer MLP (processing BioMod module embeddings) - Fusion layer: 4-layer MLP + attention mechanism """ @@ -624,7 +624,7 @@ def __init__( self.drug_bn2 = nn.BatchNorm1d(256) self.drug_bn3 = nn.BatchNorm1d(128) - # Cell branch: 2-layer MLP (processing PerformerModule embeddings) + # Cell branch: 2-layer MLP (processing BioMod embeddings) self.cell_fc1 = nn.Linear(cell_emb_dim, 256) self.cell_bn1 = nn.BatchNorm1d(256) self.cell_fc2 = nn.Linear(256, 128) @@ -689,7 +689,7 @@ def forward( x_drug_mean = x_drug.mean(dim=1) # (B, 128) x_drug = x_drug_max + x_drug_mean # Fusion - # Cell branching (handling PerformerModule embeddings) + # Cell branching (handling BioMod embeddings) x_cell = self.cell_fc1(cell_emb) # (B, 256) x_cell = self.cell_bn1(x_cell) x_cell = self.relu(x_cell) @@ -783,7 +783,7 @@ def eval_epoch(model, loader, device): def main(): parser = argparse.ArgumentParser( - description="Drug response prediction using Drug + PerformerModule" + description="Drug response prediction using Drug + BioMod" ) parser.add_argument("--gpu_id", type=str, default="0") parser.add_argument("--data_path", type=str, default=str(DOWNSTREAM_DATA_DIR / "single_drug_response" / "data")) @@ -795,31 +795,31 @@ def main(): parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--patience", type=int, default=30) - # PerformerModule related paths + # BioMod related paths parser.add_argument( "--checkpoint_path", type=str, required=True, - help="Path to pre-trained PerformerModule checkpoint" + help="Path to pre-trained BioMod checkpoint" ) parser.add_argument( "--hyper_params_path", type=str, required=True, - help="Path to PerformerModule model hyper parameters pkl file" + help="Path to BioMod model hyper parameters pkl file" ) parser.add_argument( - "--module_data_dir", + "--biomod_data_dir", "--module_data_dir", type=str, default=None, help="Directory containing module JSON files" - ) + , dest='module_data_dir') parser.add_argument( - "--module_data_path", + "--biomod_data_path", "--module_data_path", type=str, default=None, help="Path to module JSON file (single file)" - ) + , dest='module_data_path') parser.add_argument( "--gene_list_path", type=str, @@ -849,25 +849,25 @@ def main(): type=str, default="mean", choices=["mean", "max", "cls", "concat"], - help="Pooling method for PerformerModule module embeddings" + help="Pooling method for BioMod module embeddings" ) parser.add_argument( - "--shuffle_module_genes", + "--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", help="Optional downstream-only extra reshuffle on top of module_sets restored from PKL. " "Do not pass this when reproducing the pretrained backbone as-is." - ) + , dest='shuffle_module_genes') parser.add_argument( "--shuffle_seed", type=int, default=2021, - help="Random seed for the downstream extra reshuffle when --shuffle_module_genes is enabled." + help="Random seed for the downstream extra reshuffle when --shuffle_biomod_genes is enabled." ) # Output path parser.add_argument( "--save_dir", type=str, - default="./models_drug_performer_module", + default="./models_drug_biomod", help="Directory to save the model" ) @@ -877,7 +877,7 @@ def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print_sys("=" * 70) - print_sys("Drug Response Prediction: Drug + PerformerModule") + print_sys("Drug Response Prediction: Drug + BioMod") print_sys("=" * 70) print_sys(f"Device: {device}") print_sys(f"Epochs: {args.epochs}") @@ -916,9 +916,9 @@ def main(): gene_expression_file, ) - # 2) Build PerformerModule embeddings - print_sys("\n[Step 2/6] Building PerformerModule embeddings...") - cell_emb_dict, cell_emb_dim = build_cell_embeddings_with_performer_module( # cell_emb_dict │ Dict[str, np.ndarray] │ {cell_line_id: embedding} mapping dictionary + # 2) Build BioMod embeddings + print_sys("\n[Step 2/6] Building BioMod embeddings...") + cell_emb_dict, cell_emb_dim = build_cell_embeddings_with_biomod( # cell_emb_dict │ Dict[str, np.ndarray] │ {cell_line_id: embedding} mapping dictionary gexpr_feature=gexpr_feature, symbol2ens_path=args.symbol2ens_path, gene_list_path=args.gene_list_path, @@ -950,10 +950,10 @@ def main(): # 5) Build a data set print_sys("\n[Step 5/6] Creating datasets...") - train_ds = DrugPerformerModuleDataset( + train_ds = DrugBioModDataset( X_drug_train, Y_train, train_index_list, cell_emb_dict ) - validation_ds = DrugPerformerModuleDataset( + validation_ds = DrugBioModDataset( X_drug_validation, Y_validation, validation_index_list, cell_emb_dict ) @@ -977,7 +977,7 @@ def main(): # 6) Build the model print_sys("\n[Step 6/6] Building model...") - model = DrugPerformerModuleModel( + model = DrugBioModModel( drug_feat_dim=drug_dim, cell_emb_dim=cell_emb_dim, hidden_dim=args.hidden_dim, diff --git a/BioMod/environment.yml b/BioMod/environment.yml index 39cc3c3917d0277686ef0fd66d3531cdf11ef80e..d15547ab851adc4c848fc8442731c4ddf64e12cd 100644 --- a/BioMod/environment.yml +++ b/BioMod/environment.yml @@ -1,4 +1,4 @@ -name: performer_module +name: biomod channels: - pytorch - nvidia @@ -45,7 +45,7 @@ dependencies: - rdkit - pip: - # PerformerModule model utilities + # BioMod model utilities - einops==0.8.1 - local-attention==1.11.2 - hickle>=5.0.0 diff --git a/BioMod/gene_perturbation/README.md b/BioMod/gene_perturbation/README.md index 225701b1a88517ca9217396837a1b489a7a823c7..98a41749a5e0690ff92e6854e070c940a94c9278 100644 --- a/BioMod/gene_perturbation/README.md +++ b/BioMod/gene_perturbation/README.md @@ -1,14 +1,14 @@ # Gene Perturbation Prediction with BioMod This document describes the BioMod gene perturbation workflow (GEARS), using -the `PerformerEncoderOnly_Module` encoder. The +the `BioMod` encoder. The [BioMod model weights and matching configuration](../../README.md#biomod-model-weights) are shared with the other downstream tasks. ## Overview This adaptation replaces the original GEARS gene perturbation model based on -`DualEncoderSCFM` with `PerformerEncoderOnly_Module`. The main adaptation points +`DualEncoderSCFM` with `BioMod`. The main adaptation points are: 1. **Model architecture adaptation**: replace the original dual-encoder model with a module-based encoder-only model. @@ -17,12 +17,12 @@ are: ## Main Adaptations -### 1. `SCFM_Module` (`model_module.py`) +### 1. `SCFM_BioMod` (`model_biomod.py`) -`SCFM_Module` wraps `PerformerEncoderOnly_Module` and provides these functions: +`SCFM_BioMod` wraps `BioMod` and provides these functions: - **Input handling**: reindex input gene expression values to the BioMod gene order. -- **Module encoding**: obtain module-level encodings from `PerformerEncoderOnly_Module`. +- **Module encoding**: obtain module-level encodings from `BioMod`. - **Gene embedding recovery**: recover gene-level embeddings from module encodings. - For each gene, find all modules that contain it. - Average the encodings of those modules to obtain the gene embedding. @@ -42,22 +42,22 @@ for gene_idx in range(len(self.scfm_index_positions) - 1): gene_emb = module_encodings.mean(dim=1) # B, D (global average) ``` -### 2. `GEARS_Model_Module` (`model_module.py`) +### 2. `GEARS_Model_BioMod` (`model_biomod.py`) -`GEARS_Model_Module` inherits the original GEARS architecture. Main changes: +`GEARS_Model_BioMod` inherits the original GEARS architecture. Main changes: -- **Use `SCFM_Module`** instead of the original `SCFM_different_genes`. +- **Use `SCFM_BioMod`** instead of the original `SCFM_different_genes`. - **Module data loading** supports loading module data from hyper-parameters or file paths. - **Other components remain unchanged**, including the GNN layers and decoder. -### 3. Training Script (`train_module.py`) +### 3. Training Script (`train_biomod.py`) The training script is based on `GEARS/train_dual_ddp_reduce_dec.py`. Main changes: -- **Model class**: use `GEARS_Model_Module` instead of `GEARS_Model_Acc`. +- **Model class**: use `GEARS_Model_BioMod` instead of `GEARS_Model_Acc`. - **New arguments**: - - `--module_data_path`: path to one module JSON file. - - `--module_data_dir`: directory containing module JSON files. + - `--biomod_data_path`: path to one module JSON file. + - `--biomod_data_dir`: directory containing module JSON files. - `--gene_mapping_path`: CSV file mapping gene symbols to Ensembl IDs. ## Usage @@ -78,12 +78,12 @@ Run from the repository root in a Linux CUDA/NCCL environment. Built-in asset de ```bash # DDP training on multiple GPUs. python -m torch.distributed.run --standalone --nproc_per_node=4 \ - BioMod/gene_perturbation/train_module.py \ + BioMod/gene_perturbation/train_biomod.py \ --data_dir data/downstream/final_data \ --data_name norman \ - --scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --module_data_dir model_dependencies/module_data_all \ + --scfm_hyper_params_path weight/BioMod.pkl \ + --scfm_ckpt_path weight/BioMod.pth \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_mapping_path model_dependencies/gene2ens.csv \ --scfm_genes_list_path model_dependencies/selected_genes_27k.txt \ --scfm_gene2vec_file model_dependencies/selected_gene2vec_27k.npy \ @@ -100,7 +100,7 @@ python -m torch.distributed.run --standalone --nproc_per_node=4 \ - `--scfm_hyper_params_path`: path to the BioMod model configuration file. - `--scfm_ckpt_path`: path to the BioMod model weights. -- `--module_data_path` or `--module_data_dir`: required only when the model configuration does not already contain module definitions. +- `--biomod_data_path` or `--biomod_data_dir`: required only when the model configuration does not already contain module definitions. **Recommended arguments**: @@ -110,7 +110,7 @@ python -m torch.distributed.run --standalone --nproc_per_node=4 \ **Optional arguments**: -- `--use_module_level`: use module-level features. This is experimental and is not fully supported at the moment. +- `--use_biomod_level`: use module-level features. This is experimental and is not fully supported at the moment. - Default: False, meaning gene-level features are used and recommended. ## Comparison with Original sclong @@ -119,7 +119,7 @@ python -m torch.distributed.run --standalone --nproc_per_node=4 \ 1. **Model architecture**: - **Original sclong**: uses `DualEncoderSCFM` and directly processes gene sequences. - - **Module version**: uses `PerformerEncoderOnly_Module` and processes module-based data. + - **Module version**: uses `BioMod` and processes module-based data. 2. **Data representation**: - **Original sclong**: gene-level embeddings come directly from encoder outputs. @@ -144,7 +144,7 @@ To compare model performance: ```bash # Run from the BioMod repository root. - python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_module.py [arguments...] + python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_biomod.py [arguments...] ``` 3. **Compare metrics**: @@ -161,7 +161,7 @@ module version. ### Module-to-Gene Mapping -In `SCFM_Module`, `gene_to_modules` records which modules contain each gene: +In `SCFM_BioMod`, `gene_to_modules` records which modules contain each gene: ```python self.gene_to_modules = {} # {gene_idx: [module_idx1, module_idx2, ...]} @@ -193,11 +193,11 @@ BioMod/gene_perturbation/ # Perturbation response ├── data_utils_local.py ├── gears_reduce_local.py ├── inference_local.py -├── model_module.py +├── model_biomod.py ├── pertdata_local.py ├── README.md ├── scfm_utils_local.py -├── train_module.py +├── train_biomod.py └── utils.py ``` @@ -212,7 +212,7 @@ BioMod/gene_perturbation/ # Perturbation response ### Issue 1: Module Data Not Found -- Check whether `--module_data_path` or `--module_data_dir` is correct. +- Check whether `--biomod_data_path` or `--biomod_data_dir` is correct. - Make sure the module data file exists and has the expected format. ### Issue 2: Gene Mapping Failed @@ -228,13 +228,13 @@ BioMod/gene_perturbation/ # Perturbation response ## Gene-Based vs Module-Based Methods This implementation supports two feature extraction modes controlled by -`--use_module_level`. +`--use_biomod_level`. ### Method 1: Gene-Based (Default, Recommended) ```bash -# Default mode; do not pass --use_module_level or set it to False. -python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_module.py [other arguments...] +# Default mode; do not pass --use_biomod_level or set it to False. +python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_biomod.py [other arguments...] ``` **Characteristics**: @@ -248,7 +248,7 @@ python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_pert ```bash # Enable module-level mode. -python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_module.py --use_module_level [other arguments...] +python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_perturbation/train_biomod.py --use_biomod_level [other arguments...] ``` **Characteristics**: @@ -262,8 +262,8 @@ python -m torch.distributed.run --standalone --nproc_per_node=1 BioMod/gene_pert ## References - Original GEARS code is an external reference and is not included in this source directory. -- PerformerEncoderOnly_Module model: `BioMod/models_performer_module.py` -- Module data processing: `BioMod/module_dataset.py` +- BioMod model: `BioMod/models_biomod.py` +- Module data processing: `BioMod/biomod_dataset.py` ## Optional external annotation resources diff --git a/BioMod/gene_perturbation/gears_reduce_local.py b/BioMod/gene_perturbation/gears_reduce_local.py index 9331c841cb1bb6492ed914727519273fadace819..c1a4687571a6dc85f55c187d0e64f775960b78f7 100644 --- a/BioMod/gene_perturbation/gears_reduce_local.py +++ b/BioMod/gene_perturbation/gears_reduce_local.py @@ -46,7 +46,7 @@ # ============================================================ # Use native modules (optimized versions) # ============================================================ -from model_module import GEARS_Model_Module +from model_biomod import GEARS_Model_BioMod from inference_local import evaluate, compute_metrics, deeper_analysis, \ non_dropout_analysis, compute_synergy_loss from utils import loss_fct, uncertainty_loss_fct, parse_any_pert, \ @@ -74,7 +74,7 @@ class GEARS: [Usage process] ```python gears = GEARS(pert_data, local_rank, is_master, world_size, ...) - gears.model_initialize(hidden_size=64, model_class=GEARS_Model_Module, ...) + gears.model_initialize(hidden_size=64, model_class=GEARS_Model_BioMod, ...) gears.train(epochs=20, result_dir='./results', lr=1e-3) ``` """ diff --git a/BioMod/gene_perturbation/model_module.py b/BioMod/gene_perturbation/model_biomod.py similarity index 93% rename from BioMod/gene_perturbation/model_module.py rename to BioMod/gene_perturbation/model_biomod.py index f40aaa1359db87da6b329c5f5acfaa93a73cb735..fde65ac11add8d52f9bbad7eff824de242d4d221 100644 --- a/BioMod/gene_perturbation/model_module.py +++ b/BioMod/gene_perturbation/model_biomod.py @@ -1,24 +1,24 @@ """ ================================================================================ -Genetic perturbation task model definition (model_module.py) +Genetic perturbation task model definition (model_biomod.py) ================================================================================ [Model Architecture Overview] This document implements a deep learning model for genetic perturbation prediction. The core components include: -1. ModuleEmbedding: Convert gene-level expression values into module-level tokens +1. BioModEmbedding: Convert gene-level expression values into module-level tokens - Supports multiple integration methods (multiply/add/gated_residual) - Use attention pooling to aggregate genetic information within the module -2. PerformerEncoderOnly_Module: Encoder-only Transformer model +2. BioMod: Encoder-only Transformer model - Use Performer (Linear Complexity Attention) to process large numbers of module tokens - Support Masked Autoencoder (MAE) pre-training paradigm -3. SCFM_Module: wrapper for pre-trained models +3. SCFM_BioMod: wrapper for pre-trained models - Process the mapping of input genes to pre-trained model genes - Recover gene-level embeddings from module encoding -4. GEARS_Model_Module: Genetic perturbation prediction model +4. GEARS_Model_BioMod: Genetic perturbation prediction model - Integrate pre-trained encoder + GNN + decoder - Predict expression changes after gene perturbation @@ -49,19 +49,19 @@ # Performer is a linear complexity Transformer variant suitable for processing long sequences # ============================================================ try: - from BioMod.models_performer_module import ( - ModuleEmbedding, - PerformerEncoderOnly_Module, - sanitize_performer_module_config, + from BioMod.models_biomod import ( + BioModEmbedding, + BioMod, + sanitize_biomod_config, ) except ModuleNotFoundError: project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) if project_root not in sys.path: sys.path.insert(0, project_root) - from BioMod.models_performer_module import ( - ModuleEmbedding, - PerformerEncoderOnly_Module, - sanitize_performer_module_config, + from BioMod.models_biomod import ( + BioModEmbedding, + BioMod, + sanitize_biomod_config, ) # ============================================================ @@ -114,13 +114,13 @@ def reindex_tensor_universal(tensor, index_positions, dim, filler='0', device='c return reindexed -class SCFM_Module(torch.nn.Module): +class SCFM_BioMod(torch.nn.Module): """ ================================================================================ - SCFM_Module: wrapper for pre-trained single cell base models + SCFM_BioMod: wrapper for pre-trained single cell base models [Core functions] - Adapt the PerformerEncoderOnly_Module model to the genetic perturbation task to address the following key issues: + Adapt the BioMod model to the genetic perturbation task to address the following key issues: 1. Gene mapping: The gene order of the input data may be different from that of the pre-trained model, and re-indexing is required. 2. Module-to-gene recovery: Recovering gene-level embeddings from module encodings @@ -150,7 +150,7 @@ def __init__(self, input_genes, scfm_genes, device='cpu', output_dim=200, use_module_level=False): - super(SCFM_Module, self).__init__() + super(SCFM_BioMod, self).__init__() self.device = device self.use_module_level = use_module_level @@ -242,12 +242,12 @@ def __init__(self, input_genes, scfm_genes, self.register_buffer('scatter_module_idx', torch.tensor(module_indices, dtype=torch.long)) self.register_buffer('scatter_gene_idx', torch.tensor(gene_indices, dtype=torch.long)) - # Create PerformerEncoderOnly_Module model + # Create BioMod model scfm_hyper_params['module_sets'] = module_sets scfm_hyper_params['num_genes'] = len(scfm_genes) scfm_hyper_params['max_seq_len'] = len(module_sets) - self.scfm = PerformerEncoderOnly_Module(**scfm_hyper_params) + self.scfm = BioMod(**scfm_hyper_params) # ============================================================ # [Key] Load pre-trained weights @@ -298,7 +298,7 @@ def forward(self, x): [Processing Process] 1. Re-index the input gene expression into the gene order of the pre-trained model - 2. Get the module encoding through PerformerEncoderOnly_Module + 2. Get the module encoding through BioMod 3. Restore gene-level embedding from module encoding (or return module encoding directly) 4. Reindex gene embeddings back to input gene order @@ -395,17 +395,17 @@ def forward(self, x): return output_reindex -class GEARS_Model_Module(torch.nn.Module): +class GEARS_Model_BioMod(torch.nn.Module): """ ================================================================================ - GEARS_Model_Module: Genetic perturbation prediction model + GEARS_Model_BioMod: Genetic perturbation prediction model [Core functions] Predicting transcriptomic changes in cells following genetic perturbation. Enter a perturbation combination (such as knockout gene A), Output the expression value prediction of each gene after perturbation. [Model Architecture] - 1. Pretrained encoder (SCFM_Module): Encode gene expression into embedding vectors + 1. Pretrained encoder (SCFM_BioMod): Encode gene expression into embedding vectors 2. Perturbation embedding layer: generate perturbation features for perturbed genes 3. GO graph neural network (GNN): using gene ontology knowledge to enhance perturbation representation 4. Decoder: predict gene expression values after perturbation @@ -424,7 +424,7 @@ class GEARS_Model_Module(torch.nn.Module): ================================================================================ """ def __init__(self, args): - super(GEARS_Model_Module, self).__init__() + super(GEARS_Model_BioMod, self).__init__() self.args = args # Forecast record related settings @@ -462,7 +462,7 @@ def __init__(self, args): with open(args["scfm_hyper_params_path"], 'rb') as handle: self.scfm_hyper_params = pickle5.load(handle) self.scfm_hyper_params.pop('module_embed_chunk_padded_gene_budget', None) - self.scfm_hyper_params = sanitize_performer_module_config( + self.scfm_hyper_params = sanitize_biomod_config( self.scfm_hyper_params, source=args["scfm_hyper_params_path"], log_fn=print, @@ -480,8 +480,8 @@ def __init__(self, args): if 'module_sets' in self.scfm_hyper_params and self.scfm_hyper_params['module_sets'] is not None: module_sets = self.scfm_hyper_params['module_sets'] elif args.get('module_data_path') or args.get('module_data_dir'): - from BioMod.module_dataset import load_module_data - module_sets = load_module_data( + from BioMod.biomod_dataset import load_biomod_data + module_sets = load_biomod_data( module_data_path=args.get('module_data_path'), module_data_dir=args.get('module_data_dir'), gene_list_path=args.get('scfm_genes_list_path'), @@ -499,7 +499,7 @@ def __init__(self, args): if args.get('shuffle_module_genes', False): shuffle_seed = args.get('shuffle_seed', 2021) print( - f"[GEARS_Model_Module] Applying downstream-only extra module reshuffle " + f"[GEARS_Model_BioMod] Applying downstream-only extra module reshuffle " f"(seed={shuffle_seed}). Pretraining-time module_sets from PKL will be reshuffled again." ) all_gene_indices = [] @@ -518,7 +518,7 @@ def __init__(self, args): shuffled_module_sets.append(all_gene_indices[start_idx:start_idx + size]) start_idx += size module_sets = shuffled_module_sets - print(f"[GEARS_Model_Module] Shuffled {len(all_gene_indices)} genes across {len(module_sets)} modules") + print(f"[GEARS_Model_BioMod] Shuffled {len(all_gene_indices)} genes across {len(module_sets)} modules") # ============================================================ # [Perturbation Embedding Layer] @@ -585,13 +585,13 @@ def __init__(self, args): self.uncertainty_w = MLP([hidden_size, hidden_size*2, hidden_size, 1], last_layer_act='linear') # ============================================================ - # [Core] Initialize pre-trained encoder (SCFM_Module) + # [Core] Initialize pre-trained encoder (SCFM_BioMod) # requires_grad: whether to fine-tune pre-training parameters # - finetune_method='frozen': freeze pre-training parameters # - Others: Full parameter fine-tuning # ============================================================ self.use_module_level = args.get('use_module_level', False) - self.singlecell_model = SCFM_Module( + self.singlecell_model = SCFM_BioMod( self.input_genes_list, self.scfm_genes_list, self.scfm_hyper_params, diff --git a/BioMod/gene_perturbation/train_module.py b/BioMod/gene_perturbation/train_biomod.py similarity index 90% rename from BioMod/gene_perturbation/train_module.py rename to BioMod/gene_perturbation/train_biomod.py index 06f6652fd2a27f3fe9e395d94030db93ae30aa4a..3d144c093b6e1b093ccc2cb03977784ea306a9d2 100644 --- a/BioMod/gene_perturbation/train_module.py +++ b/BioMod/gene_perturbation/train_biomod.py @@ -1,6 +1,6 @@ """ ================================================================================ -Genetic perturbation task training script (train_module.py) +Genetic perturbation task training script (train_biomod.py) ================================================================================ [Task Overview] @@ -10,7 +10,7 @@ [Core process] 1. Load perturbation data sets (such as Norman, Adamson and other public data sets) -2. Initialize the pre-trained single-cell basic model (PerformerEncoderOnly_Module) +2. Initialize the pre-trained single-cell basic model (BioMod) 3. Fine-tune the model on perturbed data and learn to predict the effect of perturbation 4. Evaluate model performance (MSE, Pearson correlation coefficient and other indicators) @@ -47,7 +47,7 @@ REPO_ROOT = Path(__file__).resolve().parents[2] ASSET_DIR = REPO_ROOT / "model_dependencies" DOWNSTREAM_DATA_DIR = REPO_ROOT / "data" / "downstream" / "final_data" -WEIGHTS_DIR = REPO_ROOT / "weight" / "0810model" +WEIGHTS_DIR = REPO_ROOT / "weight" # ============================================================ # [Key] Correctly set the order of sys.path @@ -58,7 +58,7 @@ current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -# First add the project root directory (for performer_pytorch_cont) +# First add the project root directory (for pytorch_cont) if project_root not in sys.path: sys.path.append(project_root) @@ -87,7 +87,7 @@ from pertdata_local import PertData # Local version, does not rely on external gears_reduce # Import module model -from model_module import GEARS_Model_Module +from model_biomod import GEARS_Model_BioMod def main(parser): """ @@ -105,7 +105,7 @@ def main(parser): args = parser.parse_args() if args.module_data_path is None and args.module_data_dir is None: - args.module_data_dir = str(ASSET_DIR / 'module_data_all') + args.module_data_dir = str(ASSET_DIR / 'biomod_data_all') # ============================================================ # [Distributed training initialization] @@ -190,7 +190,7 @@ def main(parser): # ============================================================ # [Model parameter configuration] - # Use GEARS_Model_Module instead of GEARS_Model_Acc + # Use GEARS_Model_BioMod instead of GEARS_Model_Acc # Key parameter description: # - hidden_size: hidden layer dimension, affecting model capacity # - finetune_method: fine-tuning method ('frozen' freezes pre-training parameters, None means fine-tuning all parameters) @@ -212,7 +212,7 @@ def main(parser): key_enc=None, scfm_gene2vec_file=args.scfm_gene2vec_file, record_pred=args.record_pred, - model_class=GEARS_Model_Module, # Use module model class + model_class=GEARS_Model_BioMod, # Use module model class # ============================================================ # [Module related parameters] The following parameters affect how the model processes module-based data # module_data_path/module_data_dir: module divides the data path @@ -243,7 +243,7 @@ def main(parser): param_pd.to_csv(f'{args.result_dir}/params.csv') if __name__ == '__main__': - parser = argparse.ArgumentParser(description='GEARS with PerformerEncoderOnly_Module') + parser = argparse.ArgumentParser(description='GEARS with BioMod') # ============================================================ # [Distributed training parameters] @@ -297,9 +297,9 @@ def main(parser): parser.add_argument('--scfm_genes_list_path', type=str, default=str(ASSET_DIR / "selected_genes_27k.txt")) parser.add_argument('--scfm_hyper_params_path', type=str, - default=str(WEIGHTS_DIR / "mae_tost_70M_75mask_ec2.pkl")) + default=str(WEIGHTS_DIR / "BioMod.pkl")) parser.add_argument('--scfm_ckpt_path', type=str, - default=str(WEIGHTS_DIR / "mae_tost_70M_75mask_ec2_epoch0.pth")) + default=str(WEIGHTS_DIR / "BioMod.pth")) parser.add_argument('--run_name', type=str, default="default_run_module") parser.add_argument('--scfm_gene2vec_file', type=str, @@ -314,20 +314,20 @@ def main(parser): # fusion_method: Read from pkl to ensure consistency with pre-training # shuffle_module_genes: [Ablation experiment] Disrupt the genes in the module to verify the importance of the module structure # ============================================================ - parser.add_argument('--module_data_path', type=str, default=None, - help='Path to module JSON file (single file)') - parser.add_argument('--module_data_dir', type=str, default=None, - help='Module JSON directory; defaults to bundled module_data_all only when no module file or directory is supplied') + parser.add_argument('--biomod_data_path', '--module_data_path', type=str, default=None, + help='Path to module JSON file (single file)', dest='module_data_path') + parser.add_argument('--biomod_data_dir', '--module_data_dir', type=str, default=None, + help='Module JSON directory; defaults to bundled biomod_data_all only when no module file or directory is supplied', dest='module_data_dir') parser.add_argument('--gene_mapping_path', type=str, default=str(ASSET_DIR / 'gene2ens.csv'), help='Path to gene symbol to Ensembl ID mapping CSV') - parser.add_argument('--use_module_level', action='store_true', + parser.add_argument('--use_biomod_level', '--use_module_level', action='store_true', help='Use module-level features directly instead of recovering to gene level. ' - 'Note: This requires compatible downstream architecture (not fully implemented).') - parser.add_argument('--shuffle_module_genes', action='store_true', + 'Note: This requires compatible downstream architecture (not fully implemented).', dest='use_module_level') + parser.add_argument('--shuffle_biomod_genes', '--shuffle_module_genes', action='store_true', help='[Ablation experiment] Additionally shuffle module_sets after restoring them from the pretrained PKL. ' - 'Do not pass this parameter when reproducing the experiment normally.') + 'Do not pass this parameter when reproducing the experiment normally.', dest='shuffle_module_genes') parser.add_argument('--shuffle_seed', type=int, default=2021, - help='Only takes effect when --shuffle_module_genes is turned on and is used for additional downstream shuffling.') + help='Only takes effect when --shuffle_biomod_genes is turned on and is used for additional downstream shuffling.') parser.add_argument('--lr', type=float, default=1e-3) main(parser) diff --git a/BioMod/interpretability/README.md b/BioMod/interpretability/README.md index 68267b82c51f48803b10df5c68388e7d10d8b9a6..3e47bfdf32c33693928871e42f32ac54d7d315df 100644 --- a/BioMod/interpretability/README.md +++ b/BioMod/interpretability/README.md @@ -11,11 +11,11 @@ when loading the pretrained backbone. | # | Script | Core question | Status | | --- | --- | --- | --- | -| 1 | `module_geometry.py` | Does the module latent space self-organize by pathway family, | Completed | -| 2 | `module_text_alignment/module_text_alignment.py` | Do module embeddings align with natural-language pathway descriptions, | Implemented | -| 3 | `module_importance/module_importance.py` | Does task-driven module importance match known pathway-cell-type or pathway-disease associations, | Implemented | +| 1 | `biomod_geometry.py` | Does the module latent space self-organize by pathway family, | Completed | +| 2 | `biomod_text_alignment/biomod_text_alignment.py` | Do module embeddings align with natural-language pathway descriptions, | Implemented | +| 3 | `biomod_importance/biomod_importance.py` | Does task-driven module importance match known pathway-cell-type or pathway-disease associations, | Implemented | | 4 | `module_vs_gene.py` | Is BioMod better than GenePT/scGPT for pathway-level interpretability, | TODO | -| 6 | `attention/module_crosstalk.py` | Do module interactions match Reactome/KEGG pathway relationships, | Implemented | +| 6 | `attention/biomod_crosstalk.py` | Do module interactions match Reactome/KEGG pathway relationships, | Implemented | Experiments 1 and 2 are the core differentiators because gene-level baselines cannot structurally reproduce them. Experiment 3 provides task-level functional @@ -24,7 +24,7 @@ experiments, such as module crosstalk vs Reactome, module dynamics along developmental trajectories, and retraining-based module-granularity ablation, can be pursued after the core experiments are complete. -## Experiment 1: Intrinsic Geometry of Module Embeddings (`module_geometry.py`) +## Experiment 1: Intrinsic Geometry of Module Embeddings (`biomod_geometry.py`) **Hypothesis:** the BioMod encoder is trained with a masked-module reconstruction task, but it should still encode semantically related modules, @@ -36,7 +36,7 @@ never seen during training. 1. Load the pretrained backbone and run inference with `mask_ratio=0.0` and `return_encodings=True` to obtain module encodings shaped `[B, num_modules, base_dim]`. 2. Average over the cell population to obtain `[num_modules, base_dim]`, using streaming accumulation rather than storing the full table in memory. -3. Parse family assignments from the keys in `merged_modules.json`: `Hallmarks` (50), `Reactome` (491), `Hotspot` (73), and `Processed` (46). Hallmark modules are also annotated with one of eight MSigDB biological-process categories: immune, metabolic, signaling, proliferation, development, dna_damage, pathway, and cellular_component. +3. Parse family assignments from the keys in `merged_biomods.json`: `Hallmarks` (50), `Reactome` (491), `Hotspot` (73), and `Processed` (46). Hallmark modules are also annotated with one of eight MSigDB biological-process categories: immune, metabolic, signaling, proliferation, development, dna_damage, pathway, and cellular_component. 4. Quantify family separation: - silhouette coefficient, using both cosine and Euclidean metrics. - kNN purity at `k=10`, using both cosine and Euclidean metrics. @@ -56,36 +56,36 @@ aggregation end to end during pretraining. BioMod/interpretability/ # Module representations and biological interpretability ├── attention/ # Module crosstalk and within-module gene attention │ ├── __init__.py -│ ├── module_crosstalk.py -│ ├── module_gene_attention.py +│ ├── biomod_crosstalk.py +│ ├── biomod_gene_attention.py │ ├── README.md │ └── README_gene_attention.md -├── module_importance/ # Module importance, ablation studies and marker-gene validation +├── biomod_importance/ # Module importance, ablation studies and marker-gene validation │ ├── __init__.py │ ├── cross_class_similarity.py │ ├── family_ablation.py │ ├── family_importance_analysis.py │ ├── marker_gene_verification.py │ ├── masking_ablation.py -│ ├── module_importance.py -│ ├── module_importance_publication.py +│ ├── biomod_importance.py +│ ├── biomod_importance_publication.py │ ├── pub_utils.py │ ├── random_baseline.py │ ├── README.md │ ├── RESULTS.md │ ├── tcell_reactome_analysis.py │ └── TODO.md -├── module_text_alignment/ # Semantic alignment of module representations and pathway text +├── biomod_text_alignment/ # Semantic alignment of module representations and pathway text │ ├── __init__.py │ ├── DESIGN.md │ ├── fetch_reactome_descriptions.py │ ├── MANUSCRIPT_SECTION.md -│ ├── module_text_alignment.py -│ ├── module_text_alignment_extended.py -│ ├── module_text_alignment_publication.py +│ ├── biomod_text_alignment.py +│ ├── biomod_text_alignment_extended.py +│ ├── biomod_text_alignment_publication.py │ └── reactome_descriptions.json ├── __init__.py -├── module_geometry.py +├── biomod_geometry.py ├── README.md └── utils.py ``` @@ -104,10 +104,10 @@ bundled; run the corresponding upstream analysis before using those paths. ```bash # General h5ad input: cell annotation, zero-shot, or any annotated dataset. -python BioMod/interpretability/module_geometry.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -123,10 +123,10 @@ For pretraining data, where each cell is an `.npz` file already in the 27,874 model-gene space: ```bash -python BioMod/interpretability/module_geometry.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -144,10 +144,10 @@ processes a subset of cells, embeddings are aggregated with `all_reduce`, and rank 0 computes metrics and figures. Single-GPU and CPU behavior is unchanged. ```bash -torchrun --nproc_per_node=2 BioMod/interpretability/module_geometry.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +torchrun --nproc_per_node=2 BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -167,7 +167,7 @@ torchrun --nproc_per_node=2 BioMod/interpretability/module_geometry.py \ | `--knn_k` | 10 | `k` for kNN purity. | | `--null_permutations` | 200 | Number of label permutations for the empirical null distribution. | | `--include_other_in_metrics` | Off | By default, modules from the `Other` family are excluded. | -| `--shuffle_module_genes` | Off | Randomly shuffle gene-to-module assignments as a negative control or ablation. Uses the same flag as pretraining and downstream finetuning. | +| `--shuffle_biomod_genes` | Off | Randomly shuffle gene-to-module assignments as a negative control or ablation. Uses the same flag as pretraining and downstream finetuning. | | `--umap_n_neighbors` | 15 | UMAP graph-construction parameter. | | `--umap_min_dist` | 0.3 | UMAP layout parameter. | @@ -190,9 +190,9 @@ Files are written to `/`: Two control experiments make Experiment 1 more rigorous: -1. **Random module assignment**: rerun with `--shuffle_module_genes`. This flag preserves module sizes but randomly assigns genes to modules, matching the same ablation flag used for pretraining and finetuning. If geometry is pathway-driven rather than a generic property of the encoder, family separation should drop close to the null distribution. +1. **Random module assignment**: rerun with `--shuffle_biomod_genes`. This flag preserves module sizes but randomly assigns genes to modules, matching the same ablation flag used for pretraining and finetuning. If geometry is pathway-driven rather than a generic property of the encoder, family separation should drop close to the null distribution. -2. **Untrained backbone**: set `--performer_checkpoint` to the `*_init.pth` checkpoint generated during pretraining with `--save_init_ckpt_and_exit`. Family separation should be close to the null distribution. The gap between trained and untrained models quantifies the contribution of pretraining. +2. **Untrained backbone**: set `--biomod_checkpoint` to the `*_init.pth` checkpoint generated during pretraining with `--save_init_ckpt_and_exit`. Family separation should be close to the null distribution. The gap between trained and untrained models quantifies the contribution of pretraining. Together, these controls support the claim that the family structure of the module latent space is produced by pathway-based module design plus pretraining, rather @@ -200,16 +200,16 @@ than by module design alone or by a generic encoder architecture. ### Control 1: Random Module Assignment (`shuffle_module_genes`) -Add `--shuffle_module_genes` to the original command and write to a control output +Add `--shuffle_biomod_genes` to the original command and write to a control output directory. For h5ad data, using `pancreas_scib` as an example: ```bash -python BioMod/interpretability/module_geometry.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -218,17 +218,17 @@ python BioMod/interpretability/module_geometry.py \ --data_mode h5ad \ --gene_name_column auto \ --batch_size 640 \ - --shuffle_module_genes \ + --shuffle_biomod_genes \ --output_dir outputs/interpretability/geometry/pancreas_scib_shuffle ``` For pretraining npz data: ```bash -python BioMod/interpretability/module_geometry.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -237,11 +237,11 @@ python BioMod/interpretability/module_geometry.py \ --data_mode pretrain_npz \ --max_cells 50000 \ --batch_size 640 \ - --shuffle_module_genes \ + --shuffle_biomod_genes \ --output_dir outputs/interpretability/geometry/pretrain_subset_shuffle ``` -How `--shuffle_module_genes` works: inside `load_backbone()`, module sizes are +How `--shuffle_biomod_genes` works: inside `load_backbone()`, module sizes are kept unchanged, all genes are collected into one list, the list is randomly shuffled, and genes are reassigned to modules. The seed is controlled by `--shuffle_seed`, defaulting to 2021. Model weights are unchanged; only the @@ -253,7 +253,7 @@ records `"shuffle_module_genes": true`. ### Control 2: Untrained Backbone with Random Initialization -First generate a randomly initialized checkpoint, then run `module_geometry.py` +First generate a randomly initialized checkpoint, then run `biomod_geometry.py` with it. A matching randomly initialized checkpoint and configuration are not bundled. @@ -263,8 +263,8 @@ Keep the generated control files in a separate output directory. Then pass their paths explicitly; do not use the pretrained BioMod weights for this control. ```bash -python BioMod/interpretability/module_geometry.py \ - --performer_checkpoint /absolute/path/to/random_init.pth \ +python BioMod/interpretability/biomod_geometry.py \ + --biomod_checkpoint /absolute/path/to/random_init.pth \ --config_pkl /absolute/path/to/random_init.pkl \ --data_path data/downstream/final_data/human_pancreas_norm_complexBatch_with_gene_name.h5ad \ --data_mode h5ad \ @@ -292,7 +292,7 @@ The three experiments jointly show that the family structure of the module laten space is generated by both pathway-based module design and pretraining; both are necessary. -## Experiment 2: Module Embedding to Pathway Text Alignment (`module_text_alignment/`) +## Experiment 2: Module Embedding to Pathway Text Alignment (`biomod_text_alignment/`) **Hypothesis:** the structure of the BioMod module embedding space significantly aligns with natural-language pathway semantics. Semantically similar pathways @@ -301,7 +301,7 @@ experiment because individual gene names do not carry phrase-level semantics. **Method:** -1. Parse 541 semantically named modules from `merged_modules.json`: 50 Hallmark and 491 Reactome. +1. Parse 541 semantically named modules from `merged_biomods.json`: 50 Hallmark and 491 Reactome. 2. Encode pathway names with S-PubMedBert-MS-MARCO, a sentence-level fine-tuned PubMedBERT model. 3. Load `module_embeddings.npy` from Experiment 1, containing cell-averaged module embeddings. 4. Measure alignment: @@ -322,19 +322,19 @@ raw expression data are not required. # Network access or an existing local model cache is required. # Main experiment: RSA + CKA + LOO retrieval. -python BioMod/interpretability/module_text_alignment/module_text_alignment.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --n_permutations 1000 \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_v2 # Extended analysis: broad Reactome categories and Hallmark deep dive. -python BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ - --descriptions_file BioMod/interpretability/module_text_alignment/reactome_descriptions.json \ + --descriptions_file BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json \ --n_permutations 1000 \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_v2_extended ``` diff --git a/BioMod/interpretability/__init__.py b/BioMod/interpretability/__init__.py index bfe8ebb1174eae1a1122df4ece54fe5ee0904bf7..4c8ddc73d1e3b329685fdae5eeaac9acb96a182b 100644 --- a/BioMod/interpretability/__init__.py +++ b/BioMod/interpretability/__init__.py @@ -1,8 +1,8 @@ -"""Module interpretability experiments for scLong (performer_module). +"""Module interpretability experiments for scLong (biomod). Roadmap (priority order): - 1. module_geometry.py — intrinsic module-embedding geometry (current). - 2. module_text_alignment.py — module ↔ pathway natural-language alignment. - 3. module_importance.py — task-driven ablation + literature enrichment. + 1. biomod_geometry.py — intrinsic module-embedding geometry (current). + 2. biomod_text_alignment.py — module ↔ pathway natural-language alignment. + 3. biomod_importance.py — task-driven ablation + literature enrichment. 4. module_vs_gene.py — comparison with gene-level baselines (GenePT, scGPT, ...). """ diff --git a/BioMod/interpretability/attention/README.md b/BioMod/interpretability/attention/README.md index 0b17f528e282c771059c1e6633eda391e1ffb669..0c25f4e8ec481707ddd57829aa5e1573de0f3f64 100644 --- a/BioMod/interpretability/attention/README.md +++ b/BioMod/interpretability/attention/README.md @@ -30,10 +30,10 @@ included; replace `/absolute/path/to/pretraining_cell_npz` with your own directo # Run from the BioMod repository root. # Full run, methods A + C. -python BioMod/interpretability/attention/module_crosstalk.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/attention/biomod_crosstalk.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -46,10 +46,10 @@ python BioMod/interpretability/attention/module_crosstalk.py \ --pool_mode max # Method A only, for quick validation. -python BioMod/interpretability/attention/module_crosstalk.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/attention/biomod_crosstalk.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -61,10 +61,10 @@ python BioMod/interpretability/attention/module_crosstalk.py \ --output_dir outputs/interpretability/crosstalk/pancreas_cosine_only # Pretraining npz data. -python BioMod/interpretability/attention/module_crosstalk.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/attention/biomod_crosstalk.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ diff --git a/BioMod/interpretability/attention/README_gene_attention.md b/BioMod/interpretability/attention/README_gene_attention.md index 64b5e65a1d9b829534e9a112066adaac1193a294..7b0427641e107d7ea7ba32b010b7222bbf06cc42 100644 --- a/BioMod/interpretability/attention/README_gene_attention.md +++ b/BioMod/interpretability/attention/README_gene_attention.md @@ -1,7 +1,7 @@ -# Gene Attention Interpretability: ModuleEmbedding +# Gene Attention Interpretability: BioModEmbedding Analyze gene contributions within modules using the content-based attention -pooling in ModuleEmbedding. +pooling in BioModEmbedding. ## Analyses @@ -32,10 +32,10 @@ the column name has not been assumed for the bundled pancreas file. ```bash # Run from the BioMod repository root. -python BioMod/interpretability/attention/module_gene_attention.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/attention/biomod_gene_attention.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -50,10 +50,10 @@ python BioMod/interpretability/attention/module_gene_attention.py \ ### With DEG Comparison ```bash -python BioMod/interpretability/attention/module_gene_attention.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/interpretability/attention/biomod_gene_attention.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -89,7 +89,7 @@ Add `--max_cells 5000` to the complete basic command above and change | `--marker_db` | None | Custom marker CSV; built-in pancreas markers are used when omitted | | `--deg_csv` | None | DEG result CSV; DEG correlation analysis is skipped when omitted | | `--celltype_column` | celltype | Cell-type column name in h5ad | -| `--shuffle_module_genes` | off | Randomly shuffle gene-to-module assignment as a negative control | +| `--shuffle_biomod_genes` | off | Randomly shuffle gene-to-module assignment as a negative control | ## Output Files diff --git a/BioMod/interpretability/attention/module_crosstalk.py b/BioMod/interpretability/attention/biomod_crosstalk.py similarity index 92% rename from BioMod/interpretability/attention/module_crosstalk.py rename to BioMod/interpretability/attention/biomod_crosstalk.py index c83530710dacf57bb926befbea0859d26a890f9f..ea2b6d519e85cb9812426d4388585067909fad91 100644 --- a/BioMod/interpretability/attention/module_crosstalk.py +++ b/BioMod/interpretability/attention/biomod_crosstalk.py @@ -19,10 +19,10 @@ - Permutation null p-values for all metrics Usage (run from the BioMod repository root): - python BioMod/interpretability/attention/module_crosstalk.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + python BioMod/interpretability/attention/biomod_crosstalk.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -53,14 +53,14 @@ from sklearn.metrics import roc_auc_score HERE = Path(__file__).resolve() -PERFORMER_MODULE = HERE.parents[2] +BIOMOD_SOURCE = HERE.parents[2] REPO_ROOT = HERE.parents[3] ASSET_ROOT = REPO_ROOT / "model_dependencies" -for p in (str(PERFORMER_MODULE), str(REPO_ROOT)): +for p in (str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) -from cell_annotation.performer_module_finetune import ( +from cell_annotation.biomod_finetune import ( DataPreprocessor, load_backbone, seed_all, @@ -69,9 +69,9 @@ from interpretability.utils import ( MatrixRowDataset, NpzCellDataset, - build_module_metadata, + build_biomod_metadata, ensure_dir, - load_module_names, + load_biomod_names, ) logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") @@ -84,16 +84,16 @@ def parse_args() -> argparse.Namespace: p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - p.add_argument("--performer_checkpoint", type=str, required=True) + p.add_argument("--biomod_checkpoint", "--performer_checkpoint", type=str, required=True, dest='performer_checkpoint') p.add_argument("--config_pkl", type=str, required=True) - p.add_argument("--module_data_dir", type=str, default=str(ASSET_ROOT / "module_data_all")) - p.add_argument("--module_json", type=str, default=None) + p.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=str(ASSET_ROOT / "biomod_data_all"), dest='module_data_dir') + p.add_argument("--biomod_json", "--module_json", type=str, default=None, dest='module_json') p.add_argument("--gene_list_path", type=str, default=str(ASSET_ROOT / "selected_genes_27k.txt")) p.add_argument("--gene_mapping_path", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) p.add_argument("--gene2vec_data_path", type=str, default=str(ASSET_ROOT / "selected_gene2vec_27k.npy")) p.add_argument("--gene2ens_file", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) p.add_argument("--gene2ens_column", type=str, default="Ensembl gene ID") - p.add_argument("--shuffle_module_genes", action="store_true") + p.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", dest='shuffle_module_genes') p.add_argument("--shuffle_seed", type=int, default=2021) p.add_argument("--data_path", type=str, required=True) @@ -129,7 +129,7 @@ def parse_args() -> argparse.Namespace: # --------------------------------------------------------------------------- -# Data loading (reuses patterns from module_geometry.py) +# Data loading (reuses patterns from biomod_geometry.py) # --------------------------------------------------------------------------- def build_dataset(args, preprocessor): @@ -273,9 +273,9 @@ def _compute_single_cell_jacobian(backbone, cell, num_modules, base_dim, device) with torch.enable_grad(): if backbone.mask_style == "replace": - x = backbone._add_module_position_embedding(x_modules) + x = backbone._add_biomod_position_embedding(x_modules) else: - x = backbone._add_module_position_embedding(x_modules) + x = backbone._add_biomod_position_embedding(x_modules) if backbone.use_cls and backbone.cls_token is not None: cls_token = backbone.cls_token + backbone.pos_emb[:, :1, :] @@ -477,16 +477,16 @@ def main(): setattr(args, attr, str(Path(val).resolve())) logger.info("Loading backbone...") - backbone, module_data, model_config = load_backbone(args, device) + backbone, biomod_data, model_config = load_backbone(args, device) backbone.eval() - num_modules = len(module_data) + num_modules = len(biomod_data) base_dim = int(model_config["base_dim"]) expected_genes = int(model_config.get("num_genes", 27874)) logger.info("Model: %d modules, dim=%d, genes=%d", num_modules, base_dim, expected_genes) - module_json_path = args.module_json or str(Path(args.module_data_dir) / "merged_modules.json") - module_names = load_module_names(module_json_path) + module_json_path = args.module_json or str(Path(args.module_data_dir) / "merged_biomods.json") + module_names = load_biomod_names(module_json_path) with open(module_json_path, "r", encoding="utf-8") as f: module_gene_dict = json.load(f) @@ -571,7 +571,7 @@ def main(): json.dump(results, f, indent=2, ensure_ascii=False) # --- Plots --- - meta = build_module_metadata(module_names) + meta = build_biomod_metadata(module_names) families = [row["family"] for row in meta] plot_interaction_matrix(cos_matrix, "Method A: Cosine Similarity", diff --git a/BioMod/interpretability/attention/module_gene_attention.py b/BioMod/interpretability/attention/biomod_gene_attention.py similarity index 95% rename from BioMod/interpretability/attention/module_gene_attention.py rename to BioMod/interpretability/attention/biomod_gene_attention.py index 2f0cc58b7e5aba7ff4cacd688a8e0e45351848ce..93931156aaf5a93fce6e3466e147be843b4a7728 100644 --- a/BioMod/interpretability/attention/module_gene_attention.py +++ b/BioMod/interpretability/attention/biomod_gene_attention.py @@ -1,6 +1,6 @@ -"""ModuleEmbedding Attention-based Gene Interpretability Analysis (Streaming). +"""BioModEmbedding Attention-based Gene Interpretability Analysis (Streaming). -Interpretability analysis using single-head content-based attention pooling in ModuleEmbedding. +Interpretability analysis using single-head content-based attention pooling in BioModEmbedding. Streaming computing mode: memory usage O(n_celltypes × n_genes). Analysis content: @@ -14,10 +14,10 @@ 8. DEG comparison Usage (run from the BioMod repository root): - python BioMod/interpretability/attention/module_gene_attention.py \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + python BioMod/interpretability/attention/biomod_gene_attention.py \ + --biomod_checkpoint weight/BioMod.pth \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -51,30 +51,30 @@ from scipy.stats import spearmanr, t as t_dist HERE = Path(__file__).resolve() -PERFORMER_MODULE = HERE.parents[2] +BIOMOD_SOURCE = HERE.parents[2] REPO_ROOT = HERE.parents[3] ASSET_ROOT = REPO_ROOT / "model_dependencies" -for p in (str(PERFORMER_MODULE), str(REPO_ROOT)): +for p in (str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) # Publication utilities -MODULE_IMPORTANCE_DIR = PERFORMER_MODULE / "interpretability" / "module_importance" -if str(MODULE_IMPORTANCE_DIR) not in sys.path: - sys.path.insert(0, str(MODULE_IMPORTANCE_DIR)) +BIOMOD_IMPORTANCE_DIR = BIOMOD_SOURCE / "interpretability" / "biomod_importance" +if str(BIOMOD_IMPORTANCE_DIR) not in sys.path: + sys.path.insert(0, str(BIOMOD_IMPORTANCE_DIR)) from pub_utils import setup_publication_style, savefig as pub_savefig -from cell_annotation.performer_module_finetune import ( +from cell_annotation.biomod_finetune import ( DataPreprocessor, load_backbone, seed_all, ) from interpretability.utils import ( MatrixRowDataset, - build_module_metadata, + build_biomod_metadata, ensure_dir, - load_module_names, + load_biomod_names, ) logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") @@ -88,16 +88,16 @@ def parse_args() -> argparse.Namespace: p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - p.add_argument("--performer_checkpoint", type=str, required=True) + p.add_argument("--biomod_checkpoint", "--performer_checkpoint", type=str, required=True, dest='performer_checkpoint') p.add_argument("--config_pkl", type=str, required=True) - p.add_argument("--module_data_dir", type=str, default=str(ASSET_ROOT / "module_data_all")) - p.add_argument("--module_json", type=str, default=None) + p.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=str(ASSET_ROOT / "biomod_data_all"), dest='module_data_dir') + p.add_argument("--biomod_json", "--module_json", type=str, default=None, dest='module_json') p.add_argument("--gene_list_path", type=str, default=str(ASSET_ROOT / "selected_genes_27k.txt")) p.add_argument("--gene_mapping_path", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) p.add_argument("--gene2vec_data_path", type=str, default=str(ASSET_ROOT / "selected_gene2vec_27k.npy")) p.add_argument("--gene2ens_file", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) p.add_argument("--gene2ens_column", type=str, default="Ensembl gene ID") - p.add_argument("--shuffle_module_genes", action="store_true") + p.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", dest='shuffle_module_genes') p.add_argument("--shuffle_seed", type=int, default=2021) p.add_argument("--data_path", type=str, required=True) @@ -915,7 +915,7 @@ def main(): # --- Load model --- logger.info("Loading backbone from %s", args.performer_checkpoint) - backbone, module_data, model_config = load_backbone(args, device) + backbone, biomod_data, model_config = load_backbone(args, device) backbone.eval() # --- Build module gene name mapping --- diff --git a/BioMod/interpretability/module_geometry.py b/BioMod/interpretability/biomod_geometry.py similarity index 91% rename from BioMod/interpretability/module_geometry.py rename to BioMod/interpretability/biomod_geometry.py index 6ce19e5f2f5007db686f04a73a8e4669c7601d88..2bd3fb6e164fedcb5243f6708fae4ef3fbd0909c 100644 --- a/BioMod/interpretability/module_geometry.py +++ b/BioMod/interpretability/biomod_geometry.py @@ -12,7 +12,7 @@ 1. Load pretrained backbone (forward with mask_ratio=0). 2. Iterate over a population of cells, accumulate the per-module encoder output in a streaming average -> [num_modules, base_dim]. -3. Parse each module's family from merged_modules.json keys +3. Parse each module's family from merged_biomods.json keys (Hallmarks / Reactome / Hotspot / Processed) and, where applicable, the MSigDB hallmark process subcategory (immune / metabolic / signaling / ...). 4. Quantify family separation: @@ -44,8 +44,8 @@ CLI --- -python -m performer_module.interpretability.module_geometry \\ - --performer_checkpoint \\ +python BioMod/interpretability/biomod_geometry.py \\ + --biomod_checkpoint \\ --config_pkl \\ --data_path \\ --data_mode h5ad \\ @@ -79,14 +79,14 @@ # Make sibling modules importable when launched as a script HERE = Path(__file__).resolve() -PERFORMER_MODULE = HERE.parents[1] +BIOMOD_SOURCE = HERE.parents[1] REPO_ROOT = HERE.parents[2] ASSET_ROOT = REPO_ROOT / "model_dependencies" -for p in (str(PERFORMER_MODULE), str(REPO_ROOT)): +for p in (str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) -from cell_annotation.performer_module_finetune import ( # noqa: E402 +from cell_annotation.biomod_finetune import ( # noqa: E402 DataPreprocessor, get_rank, get_world_size, @@ -102,9 +102,9 @@ from interpretability.utils import ( # noqa: E402 MatrixRowDataset, NpzCellDataset, - build_module_metadata, + build_biomod_metadata, ensure_dir, - load_module_names, + load_biomod_names, ) logger = logging.getLogger(__name__) @@ -115,19 +115,19 @@ def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - # Pretrained backbone (mirrors cell_annotation.performer_module_finetune) - parser.add_argument("--performer_checkpoint", type=str, required=True) + # Pretrained backbone (mirrors cell_annotation.biomod_finetune) + parser.add_argument("--biomod_checkpoint", "--performer_checkpoint", type=str, required=True, dest='performer_checkpoint') parser.add_argument("--config_pkl", type=str, required=True) - parser.add_argument("--module_data_dir", type=str, default=str(ASSET_ROOT / "module_data_all")) - parser.add_argument("--module_json", type=str, default=None, - help="Path to merged_modules.json. Defaults to /merged_modules.json.") + parser.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=str(ASSET_ROOT / "biomod_data_all"), dest='module_data_dir') + parser.add_argument("--biomod_json", "--module_json", type=str, default=None, + help="Path to merged_biomods.json. Defaults to /merged_biomods.json.", dest='module_json') parser.add_argument("--gene_list_path", type=str, default=str(ASSET_ROOT / "selected_genes_27k.txt")) parser.add_argument("--gene_mapping_path", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) parser.add_argument("--gene2vec_data_path", type=str, default=str(ASSET_ROOT / "selected_gene2vec_27k.npy")) parser.add_argument("--gene2ens_file", type=str, default=str(ASSET_ROOT / "gene2ens.csv")) parser.add_argument("--gene2ens_column", type=str, default="Ensembl gene ID") - parser.add_argument("--shuffle_module_genes", action="store_true", - help="Randomly reshuffle gene-to-module assignment (control / ablation).") + parser.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", + help="Randomly reshuffle gene-to-module assignment (control / ablation).", dest='shuffle_module_genes') parser.add_argument("--shuffle_seed", type=int, default=2021) # Data @@ -245,7 +245,7 @@ def build_inference_loader( # ---------- inference: cell-averaged module embeddings ---------- @torch.no_grad() -def extract_cell_averaged_module_embeddings( +def extract_cell_averaged_biomod_embeddings( backbone, loader: DataLoader, preprocessor: Optional[DataPreprocessor], @@ -285,7 +285,7 @@ def extract_cell_averaged_module_embeddings( raise RuntimeError( f"Encoder output shape mismatch: got {tuple(enc.shape)}, " f"expected (*, {num_modules}, {base_dim}). " - "Module order between merged_modules.json and the loaded backbone may have drifted." + "Module order between merged_biomods.json and the loaded backbone may have drifted." ) sum_emb += enc.to(torch.float64).sum(dim=0) n_cells += enc.shape[0] @@ -497,13 +497,13 @@ def setup_logging(output_dir: Path) -> None: ) -def resolve_module_json(args: argparse.Namespace) -> str: - """Resolve relative paths against performer_module/cell_annotation/ to stay +def resolve_biomod_json(args: argparse.Namespace) -> str: + """Resolve relative paths against biomod/cell_annotation/ to stay consistent with how `load_backbone` reads `module_data_dir`. """ if args.module_json is not None: return str(ca_resolve_path(args.module_json)) - return str(ca_resolve_path(args.module_data_dir) / "merged_modules.json") + return str(ca_resolve_path(args.module_data_dir) / "merged_biomods.json") def main() -> None: @@ -526,15 +526,15 @@ def main() -> None: logger.info("DDP: use_ddp=%s, rank=%d/%d, device=%s", use_ddp, rank, world_size, device) # 1. Load backbone - backbone, module_data, model_config = load_backbone(args, device) + backbone, biomod_data, model_config = load_backbone(args, device) backbone.eval() base_dim = int(model_config["base_dim"]) - num_modules = len(module_data) + num_modules = len(biomod_data) logger.info("Loaded backbone: num_modules=%d, base_dim=%d", num_modules, base_dim) - # 2. Load module names (must align with module_data order) - module_json_path = resolve_module_json(args) - module_names = load_module_names(module_json_path) + # 2. Load module names (must align with biomod_data order) + module_json_path = resolve_biomod_json(args) + module_names = load_biomod_names(module_json_path) if len(module_names) != num_modules: raise RuntimeError( f"Module name count ({len(module_names)} from {module_json_path}) does not match " @@ -542,7 +542,7 @@ def main() -> None: "model and the one given here must be identical, with no modules dropped during " "gene-symbol resolution." ) - metadata = build_module_metadata(module_names) + metadata = build_biomod_metadata(module_names) metadata_df = pd.DataFrame(metadata) if is_main_process(): logger.info("Family distribution:\n%s", metadata_df["family"].value_counts().to_string()) @@ -569,7 +569,7 @@ def main() -> None: # 4. Inference logger.info("Extracting cell-averaged module embeddings over %d cells...", n_cells) - mean_emb, processed = extract_cell_averaged_module_embeddings( + mean_emb, processed = extract_cell_averaged_biomod_embeddings( backbone, loader, preprocessor, reindex_state, device, num_modules=num_modules, base_dim=base_dim, expected_input_genes=expected_input_genes, diff --git a/BioMod/interpretability/module_importance/README.md b/BioMod/interpretability/biomod_importance/README.md similarity index 83% rename from BioMod/interpretability/module_importance/README.md rename to BioMod/interpretability/biomod_importance/README.md index e6a85aa260a1ce4ed5fbdaa6e9f167c3b0466a1e..22c7c657ed48d8224e4a93eaf066704f62d8dba9 100644 --- a/BioMod/interpretability/module_importance/README.md +++ b/BioMod/interpretability/biomod_importance/README.md @@ -33,10 +33,10 @@ preceding steps; they are not bundled inputs. ```bash # Run from the BioMod repository root. -python BioMod/interpretability/module_importance/module_importance.py \ +python BioMod/interpretability/biomod_importance/biomod_importance.py \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -52,10 +52,10 @@ python BioMod/interpretability/module_importance/module_importance.py \ Pre-encoder mode, more accurate but slower: ```bash -python BioMod/interpretability/module_importance/module_importance.py \ +python BioMod/interpretability/biomod_importance/biomod_importance.py \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -72,10 +72,10 @@ python BioMod/interpretability/module_importance/module_importance.py \ Pre-encoder mode on all cells with DDP multi-GPU acceleration: ```bash -torchrun --nproc_per_node=4 BioMod/interpretability/module_importance/module_importance.py \ +torchrun --nproc_per_node=4 BioMod/interpretability/biomod_importance/biomod_importance.py \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -132,10 +132,10 @@ If the model learns meaningful module importance: Progressively mask top, bottom, or random modules and measure the accuracy drop. ```bash -python BioMod/interpretability/module_importance/masking_ablation.py \ +python BioMod/interpretability/biomod_importance/masking_ablation.py \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -154,11 +154,11 @@ python BioMod/interpretability/module_importance/masking_ablation.py \ ```bash # Mode A: shuffle modules, GPU and slow. -python BioMod/interpretability/module_importance/random_baseline.py \ +python BioMod/interpretability/biomod_importance/random_baseline.py \ --mode shuffle_modules \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -170,7 +170,7 @@ python BioMod/interpretability/module_importance/random_baseline.py \ --output_dir outputs/interpretability/importance/permutation/ # Mode B: shuffle labels, CPU and fast, requires per_cell_deltas from ablation. -python BioMod/interpretability/module_importance/random_baseline.py \ +python BioMod/interpretability/biomod_importance/random_baseline.py \ --mode shuffle_labels \ --importance_matrix outputs/interpretability/importance/zheng68k_pre_13690/importance_matrix.npy \ --per_cell_deltas outputs/interpretability/importance/ablation/per_cell_deltas.npy \ @@ -182,21 +182,21 @@ python BioMod/interpretability/module_importance/random_baseline.py \ ### 3-C: Marker Gene Verification, CPU Only ```bash -python BioMod/interpretability/module_importance/marker_gene_verification.py \ +python BioMod/interpretability/biomod_importance/marker_gene_verification.py \ --importance_matrix outputs/interpretability/importance/zheng68k_pre_13690/importance_matrix.npy \ --importance_csv \ - --module_data_dir model_dependencies/module_data_all \ + --biomod_data_dir model_dependencies/biomod_data_all \ --output_dir outputs/interpretability/importance/marker_verification/ ``` ### Publication Figures ```bash -python BioMod/interpretability/module_importance/module_importance_publication.py \ +python BioMod/interpretability/biomod_importance/biomod_importance_publication.py \ --results_dir outputs/interpretability/importance/zheng68k_pre_13690/ \ --ablation_dir outputs/interpretability/importance/ablation \ --permutation_dir outputs/interpretability/importance/permutation/ \ - --module_data_dir model_dependencies/module_data_all \ + --biomod_data_dir model_dependencies/biomod_data_all \ --output_dir outputs/interpretability/importance/publication_figures/ ``` @@ -206,7 +206,7 @@ Analyze importance distributions, contribution fractions, and enrichment tests b module source family: Hallmarks, Reactome, Hotspot, and WGCNA. ```bash -python BioMod/interpretability/module_importance/family_importance_analysis.py \ +python BioMod/interpretability/biomod_importance/family_importance_analysis.py \ --results_dir outputs/interpretability/importance/zheng68k_pre \ --output_dir outputs/interpretability/importance/family_analysis \ --top_k 10 20 50 \ @@ -231,10 +231,10 @@ Ablate whole families by removing or keeping one module family and measuring classification-accuracy changes. ```bash -python BioMod/interpretability/module_importance/family_ablation.py \ +python BioMod/interpretability/biomod_importance/family_ablation.py \ --finetune_checkpoint tutorial_runs/cell_annotation_zheng68k_biomod/best_model.pt \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --module_data_dir model_dependencies/module_data_all \ + --config_pkl weight/BioMod.pkl \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -257,7 +257,7 @@ Analyze correlations and similarity between important modules across the 11 cell classes. ```bash -python BioMod/interpretability/module_importance/cross_class_similarity.py \ +python BioMod/interpretability/biomod_importance/cross_class_similarity.py \ --results_dir outputs/interpretability/importance/zheng68k_pre \ --output_dir outputs/interpretability/importance/publication_figures \ --top_k 50 @@ -275,10 +275,10 @@ parameters are trained from zero initialization. ```bash # Baseline, no new modules. -python BioMod/cell_annotation/performer_module_finetune.py \ - --config_pkl weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --performer_checkpoint weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --module_data_dir model_dependencies/module_data_all \ +python BioMod/cell_annotation/biomod_finetune.py \ + --config_pkl weight/BioMod.pkl \ + --biomod_checkpoint weight/BioMod.pth \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_list_path model_dependencies/selected_genes_27k.txt \ --gene_mapping_path model_dependencies/gene2ens.csv \ --gene2vec_data_path model_dependencies/selected_gene2vec_27k.npy \ @@ -296,7 +296,7 @@ python BioMod/cell_annotation/performer_module_finetune.py \ The same command can be extended with: - `--use_family_emb` to enable family-type embedding, adding `4 x 200 = 800` parameters. -- `--use_module_gate` to enable sparse module gating, adding LayerNorm + Linear, 601 parameters. +- `--use_biomod_gate` to enable sparse module gating, adding LayerNorm + Linear, 601 parameters. - Both flags can be used together. The new module parameters use `lr_head`, a higher learning rate, and are @@ -306,11 +306,11 @@ independent of frozen backbone parameters. | File | Description | | --- | --- | -| `module_importance.py` | Core importance computation with DDP support | +| `biomod_importance.py` | Core importance computation with DDP support | | `masking_ablation.py` | Ablation-curve experiment, GPU | | `random_baseline.py` | Permutation tests, GPU/CPU | | `marker_gene_verification.py` | Marker verification, CPU | -| `module_importance_publication.py` | Publication figures | +| `biomod_importance_publication.py` | Publication figures | | `family_importance_analysis.py` | Family-level importance analysis, CPU | | `family_ablation.py` | Family-level ablation, GPU | | `cross_class_similarity.py` | Cross-class similarity analysis, CPU | diff --git a/BioMod/interpretability/module_importance/RESULTS.md b/BioMod/interpretability/biomod_importance/RESULTS.md similarity index 94% rename from BioMod/interpretability/module_importance/RESULTS.md rename to BioMod/interpretability/biomod_importance/RESULTS.md index 7b21a71f60c89892e0f3784884f483914aa9f97b..c55aa84bc9db3fecb3c6b31aba438bfe5d153cba 100644 --- a/BioMod/interpretability/module_importance/RESULTS.md +++ b/BioMod/interpretability/biomod_importance/RESULTS.md @@ -5,7 +5,7 @@ ## Dataset - **Zheng68K**: 11 cell types, 68,450 cells, with 13,690 test cells. -- Model: PerformerModuleCellClassifier with attention-pooling head and 48M backbone. +- Model: BioModCellClassifier with attention-pooling head and 48M backbone. - Baseline test accuracy: **83.1%**. ## Data Sources @@ -13,9 +13,9 @@ These are historical experiment results. The analysis output files below are not included in the repository; recreate them using the commands in `README.md` or point the analysis scripts to the original run directory. The shared module JSON -is bundled under `model_dependencies/module_data_all/` at the repository root. +is bundled under `model_dependencies/biomod_data_all/` at the repository root. -All analyses are based on the following core files, generated by `module_importance.py` +All analyses are based on the following core files, generated by `biomod_importance.py` with `pre_encoder` over all 13,690 test cells: | File | Path | Description | @@ -24,10 +24,10 @@ with `pre_encoder` over all 13,690 test cells: | `importance_matrix.csv` | `outputs/interpretability/importance/zheng68k_pre_13690/importance_matrix.csv` | Same matrix with row and column labels | | `enrichment_results.csv` | `outputs/interpretability/importance/zheng68k_pre_13690/enrichment_results.csv` | Enrichment-test results | | `top_modules_per_celltype.json` | `outputs/interpretability/importance/zheng68k_pre_13690/top_modules_per_celltype.json` | Top-module details | -| `merged_modules.json` | `model_dependencies/module_data_all/merged_modules.json` | Gene composition of 660 modules | +| `merged_biomods.json` | `model_dependencies/biomod_data_all/merged_biomods.json` | Gene composition of 660 modules | Marker verification uses `importance_matrix.npy`, `importance_matrix.csv`, and -`merged_modules.json`. +`merged_biomods.json`. Publication figures use all files above plus ablation and permutation results. diff --git a/BioMod/interpretability/module_importance/TODO.md b/BioMod/interpretability/biomod_importance/TODO.md similarity index 97% rename from BioMod/interpretability/module_importance/TODO.md rename to BioMod/interpretability/biomod_importance/TODO.md index c7b96215ae4287088bea97696f9eee93e538c397..e24f4103df58e46560da800493af4d15cd9b38eb 100644 --- a/BioMod/interpretability/module_importance/TODO.md +++ b/BioMod/interpretability/biomod_importance/TODO.md @@ -14,7 +14,7 @@ - [x] `marker_gene_verification.py`: canonical marker verification. - Result: 80% of markers are in the top 5%, median percentile = 0.9%. - Figures: `fig_marker_ranks.pdf`, `fig_marker_summary_bar.pdf`. -- [x] `module_importance_publication.py`: publication-figure generator. +- [x] `biomod_importance_publication.py`: publication-figure generator. - [x] `RESULTS.md`: result documentation. - [x] `TODO.md`: this file. diff --git a/BioMod/interpretability/module_importance/__init__.py b/BioMod/interpretability/biomod_importance/__init__.py similarity index 100% rename from BioMod/interpretability/module_importance/__init__.py rename to BioMod/interpretability/biomod_importance/__init__.py diff --git a/BioMod/interpretability/module_importance/module_importance.py b/BioMod/interpretability/biomod_importance/biomod_importance.py similarity index 95% rename from BioMod/interpretability/module_importance/module_importance.py rename to BioMod/interpretability/biomod_importance/biomod_importance.py index 04792e91d0ee81f120ddeea73fe4143a8f22767a..62f52deea2aa7d1c7b02692820035bbb74752fcf 100644 --- a/BioMod/interpretability/module_importance/module_importance.py +++ b/BioMod/interpretability/biomod_importance/biomod_importance.py @@ -33,10 +33,10 @@ SCRIPT_DIR = Path(__file__).resolve().parent INTERP_DIR = SCRIPT_DIR.parent -PERFORMER_MODULE_ROOT = INTERP_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = INTERP_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent -for p in (str(PERFORMER_MODULE_ROOT), str(SCLONG_ROOT)): +for p in (str(BIOMOD_ROOT), str(SCLONG_ROOT)): if p not in sys.path: sys.path.insert(0, p) @@ -89,8 +89,8 @@ def __getitem__(self, idx): def load_finetuned_model(args, device: torch.device): """Load the full finetuned classifier (backbone + pooler + head).""" - from models_performer_module import PerformerEncoderOnly_Module - from module_dataset import load_module_data + from models_biomod import BioMod + from biomod_dataset import load_biomod_data ckpt_path = Path(args.finetune_checkpoint) checkpoint = torch.load(ckpt_path, map_location=device) @@ -106,13 +106,13 @@ def load_finetuned_model(args, device: torch.device): with open(config_pkl_path, "rb") as f: config_pkl = pickle.load(f) - module_data = None + biomod_data = None if isinstance(config_pkl, dict) and "module_sets" in config_pkl: - module_data = config_pkl["module_sets"] + biomod_data = config_pkl["module_sets"] elif "module_sets" in backbone_config: - module_data = backbone_config["module_sets"] + biomod_data = backbone_config["module_sets"] else: - module_data = load_module_data( + biomod_data = load_biomod_data( module_data_dir=args.module_data_dir, gene_list_path=args.gene_list_path, gene_mapping_path=args.gene_mapping_path, @@ -141,10 +141,10 @@ def load_finetuned_model(args, device: torch.device): dec_heads = backbone_config.get("dec_heads", 8) dec_dim_head = backbone_config.get("dec_dim_head", 64) - backbone = PerformerEncoderOnly_Module( - module_sets=module_data, + backbone = BioMod( + module_sets=biomod_data, num_genes=num_genes, - max_seq_len=len(module_data), + max_seq_len=len(biomod_data), base_dim=base_dim, enc_depth=enc_depth, enc_heads=enc_heads, @@ -171,10 +171,10 @@ def load_finetuned_model(args, device: torch.device): dec_dim_head=dec_dim_head, ) - sys.path.insert(0, str(PERFORMER_MODULE_ROOT / "cell_annotation")) - from performer_module_finetune import ( + sys.path.insert(0, str(BIOMOD_ROOT / "cell_annotation")) + from biomod_finetune import ( GatedAttentionPool, - PerformerModuleCellClassifier, + BioModCellClassifier, ScGPTClsDecoder, ) @@ -183,9 +183,9 @@ def load_finetuned_model(args, device: torch.device): pool_hidden_dim = saved_args.get("pool_hidden_dim", 0) cls_decoder_layers = saved_args.get("cls_decoder_layers", 3) - model = PerformerModuleCellClassifier( + model = BioModCellClassifier( backbone=backbone, - module_sets=module_data, + module_sets=biomod_data, num_classes=num_classes, head_type=head_type, pool_hidden_dim=pool_hidden_dim, @@ -199,9 +199,9 @@ def load_finetuned_model(args, device: torch.device): logger.info( "Loaded finetuned model: head=%s, classes=%d, modules=%d", - head_type, num_classes, len(module_data), + head_type, num_classes, len(biomod_data), ) - return model, module_data, label_classes, head_type + return model, biomod_data, label_classes, head_type # ─── Data Loading ─────────────────────────────────────────────────────────── @@ -210,8 +210,8 @@ def load_test_data(args, label_classes: List[str], device: torch.device): """Load Zheng68K and reproduce the test split.""" import anndata as ad - sys.path.insert(0, str(PERFORMER_MODULE_ROOT / "cell_annotation")) - from performer_module_finetune import DataPreprocessor + sys.path.insert(0, str(BIOMOD_ROOT / "cell_annotation")) + from biomod_finetune import DataPreprocessor preprocessor = DataPreprocessor( gene2ens_file=args.gene2ens_file, @@ -276,7 +276,7 @@ def compute_importance_post_encoder( if model.head_type == "cls": module_tokens = latent[:, 1:, :] else: - module_tokens = model._restore_module_order(latent, ids_restore) + module_tokens = model._restore_biomod_order(latent, ids_restore) baseline_logits = _classify_from_tokens(model, module_tokens) y_tensor = torch.from_numpy(y_batch).to(device) @@ -340,7 +340,7 @@ def _classify_from_tokens(model: nn.Module, module_tokens: torch.Tensor) -> torc elif model.head_type == "attn_pool": cell_emb, _ = model.pooler(module_tokens) elif model.head_type == "gene_mean": - gene_tokens = model._module_to_gene(module_tokens) + gene_tokens = model._biomod_to_gene(module_tokens) cell_emb = gene_tokens.mean(dim=1) else: raise ValueError(f"Unsupported head_type: {model.head_type}") @@ -533,12 +533,12 @@ def run_enrichment( # ─── Module Gene Mapping ───────────────────────────────────────────────────── -def build_module_gene_mapping( +def build_biomod_gene_mapping( module_data_dir: str, gene_list_path: str, ) -> Tuple[List[str], Dict[str, List[str]]]: """Return (module_names, {module_name: [gene_symbols]}).""" - merged_path = Path(module_data_dir) / "merged_modules.json" + merged_path = Path(module_data_dir) / "merged_biomods.json" with open(merged_path, "r") as f: merged = json.load(f) @@ -582,7 +582,7 @@ def plot_importance_heatmap( logger.info("Saved heatmap: %s", output_path) -def plot_top_modules_bar( +def plot_top_biomods_bar( importance: np.ndarray, label_classes: List[str], module_names: List[str], @@ -628,8 +628,8 @@ def parse_args(): help="Path to best_model.pt from cell annotation finetuning") p.add_argument("--config_pkl", required=True, help="Path to pretrained model .pkl config") - p.add_argument("--module_data_dir", required=True, - help="Directory containing merged_modules.json") + p.add_argument("--biomod_data_dir", "--module_data_dir", required=True, + help="Directory containing merged_biomods.json", dest='module_data_dir') p.add_argument("--gene_list_path", required=True, help="Path to selected_genes_27k.txt") p.add_argument("--gene_mapping_path", required=True) @@ -677,8 +677,8 @@ def main(): setattr(args, attr, str(Path(val).resolve())) logger.info("Loading finetuned model...") - model, module_data, label_classes, head_type = load_finetuned_model(args, device) - num_modules = len(module_data) + model, biomod_data, label_classes, head_type = load_finetuned_model(args, device) + num_modules = len(biomod_data) num_classes = len(label_classes) logger.info("Loading test data...") @@ -752,7 +752,7 @@ def main(): # Only rank 0 does enrichment, plotting, saving if rank == 0: logger.info("Building module-gene mapping...") - module_names, module_gene_symbols = build_module_gene_mapping( + module_names, module_gene_symbols = build_biomod_gene_mapping( args.module_data_dir, args.gene_list_path ) @@ -810,7 +810,7 @@ def main(): plot_importance_heatmap( importance, label_classes, output_dir / "heatmap_importance.png" ) - plot_top_modules_bar( + plot_top_biomods_bar( importance, label_classes, module_names, output_dir ) diff --git a/BioMod/interpretability/module_importance/module_importance_publication.py b/BioMod/interpretability/biomod_importance/biomod_importance_publication.py similarity index 97% rename from BioMod/interpretability/module_importance/module_importance_publication.py rename to BioMod/interpretability/biomod_importance/biomod_importance_publication.py index eb8711c4b506a3a142bf7ebb89bd013313fe36f8..a1c6983b511776ac62d179e45db3e461ad425356 100644 --- a/BioMod/interpretability/module_importance/module_importance_publication.py +++ b/BioMod/interpretability/biomod_importance/biomod_importance_publication.py @@ -31,9 +31,9 @@ from pub_utils import ( CELL_TYPE_COLORS, MODULE_FAMILY_COLORS, - get_module_families, + get_biomod_families, load_importance_results, - load_module_gene_mapping, + load_biomod_gene_mapping, savefig, setup_publication_style, ) @@ -204,7 +204,7 @@ def plot_importance_heatmap_clustered(results_dir, module_data_dir, output_dir): label_classes = data["label_classes"] module_names = data["module_names"] - families = get_module_families(module_names) + families = get_biomod_families(module_names) family_colors = [MODULE_FAMILY_COLORS.get(f, "#CCCCCC") for f in families] # Cluster modules @@ -329,8 +329,8 @@ def parse_args(): help="Ablation results directory") p.add_argument("--permutation_dir", help="Permutation results directory") - p.add_argument("--module_data_dir", required=True, - help="Module data directory (merged_modules.json)") + p.add_argument("--biomod_data_dir", "--module_data_dir", required=True, + help="Module data directory (merged_biomods.json)", dest='module_data_dir') p.add_argument("--output_dir", required=True) return p.parse_args() diff --git a/BioMod/interpretability/module_importance/cross_class_similarity.py b/BioMod/interpretability/biomod_importance/cross_class_similarity.py similarity index 100% rename from BioMod/interpretability/module_importance/cross_class_similarity.py rename to BioMod/interpretability/biomod_importance/cross_class_similarity.py diff --git a/BioMod/interpretability/module_importance/family_ablation.py b/BioMod/interpretability/biomod_importance/family_ablation.py similarity index 97% rename from BioMod/interpretability/module_importance/family_ablation.py rename to BioMod/interpretability/biomod_importance/family_ablation.py index 7afc1627a26e43b209ff642b9d346eaf8801ba21..0c28e430a27388b778c426336c386223dbe1faa0 100644 --- a/BioMod/interpretability/module_importance/family_ablation.py +++ b/BioMod/interpretability/biomod_importance/family_ablation.py @@ -29,10 +29,10 @@ SCRIPT_DIR = Path(__file__).resolve().parent INTERP_DIR = SCRIPT_DIR.parent -PERFORMER_MODULE_ROOT = INTERP_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = INTERP_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent -for p in (str(SCRIPT_DIR), str(PERFORMER_MODULE_ROOT), str(SCLONG_ROOT)): +for p in (str(SCRIPT_DIR), str(BIOMOD_ROOT), str(SCLONG_ROOT)): if p not in sys.path: sys.path.insert(0, p) @@ -40,10 +40,10 @@ compute_masked_accuracy_post_encoder, encode_all_cells, ) -from module_importance import ( +from biomod_importance import ( CellDataset, _classify_from_tokens, - build_module_gene_mapping, + build_biomod_gene_mapping, load_finetuned_model, load_test_data, ) @@ -296,7 +296,7 @@ def parse_args(): p = argparse.ArgumentParser(description="Family-level ablation experiment") p.add_argument("--finetune_checkpoint", required=True) p.add_argument("--config_pkl", required=True) - p.add_argument("--module_data_dir", required=True) + p.add_argument("--biomod_data_dir", "--module_data_dir", required=True, dest='module_data_dir') p.add_argument("--gene_list_path", required=True) p.add_argument("--gene_mapping_path", required=True) p.add_argument("--gene2vec_data_path", required=True) @@ -329,8 +329,8 @@ def main(): setattr(args, attr, str(Path(val).resolve())) logger.info("Loading model...") - model, module_data, label_classes, head_type = load_finetuned_model(args, device) - module_names, _ = build_module_gene_mapping(args.module_data_dir, args.gene_list_path) + model, biomod_data, label_classes, head_type = load_finetuned_model(args, device) + module_names, _ = build_biomod_gene_mapping(args.module_data_dir, args.gene_list_path) family_indices = get_family_indices(module_names) for fam in FAMILY_ORDER: logger.info(" %s: %d modules", fam, len(family_indices[fam])) diff --git a/BioMod/interpretability/module_importance/family_importance_analysis.py b/BioMod/interpretability/biomod_importance/family_importance_analysis.py similarity index 100% rename from BioMod/interpretability/module_importance/family_importance_analysis.py rename to BioMod/interpretability/biomod_importance/family_importance_analysis.py diff --git a/BioMod/interpretability/module_importance/marker_gene_verification.py b/BioMod/interpretability/biomod_importance/marker_gene_verification.py similarity index 96% rename from BioMod/interpretability/module_importance/marker_gene_verification.py rename to BioMod/interpretability/biomod_importance/marker_gene_verification.py index 088bd58e31a32c66816a1fdad7fd92d7206f8660..38f1ad911697bbc6ba346c320b1f5a91783a7b1d 100644 --- a/BioMod/interpretability/module_importance/marker_gene_verification.py +++ b/BioMod/interpretability/biomod_importance/marker_gene_verification.py @@ -28,11 +28,11 @@ from pub_utils import ( CANONICAL_MARKERS, CELL_TYPE_COLORS, - find_gene_modules, - get_module_family, + find_gene_biomods, + get_biomod_family, importance_rank_percentile, load_importance_results, - load_module_gene_mapping, + load_biomod_gene_mapping, savefig, setup_publication_style, ) @@ -55,7 +55,7 @@ def compute_marker_ranks( ranked_indices = np.argsort(-imp_row) for marker in markers: - containing_modules = find_gene_modules(marker, module_gene_symbols) + containing_modules = find_gene_biomods(marker, module_gene_symbols) if not containing_modules: rows.append({ "cell_type": cls_name, @@ -89,7 +89,7 @@ def compute_marker_ranks( "found_in_modules": True, "num_modules_containing": len(containing_modules), "best_module": module_names[best_pi], - "best_module_family": get_module_family(module_names[best_pi]), + "best_module_family": get_biomod_family(module_names[best_pi]), "best_rank": best_rank + 1, "rank_percentile": 100.0 * best_rank / num_modules, "best_importance": float(imp_row[best_pi]), @@ -252,8 +252,8 @@ def parse_args(): help="Path to importance_matrix.npy") p.add_argument("--importance_csv", required=True, help="Path to importance_matrix.csv (for labels)") - p.add_argument("--module_data_dir", required=True, - help="Directory containing merged_modules.json") + p.add_argument("--biomod_data_dir", "--module_data_dir", required=True, + help="Directory containing merged_biomods.json", dest='module_data_dir') p.add_argument("--output_dir", required=True) return p.parse_args() @@ -268,7 +268,7 @@ def main(): label_classes = list(imp_csv.index) module_names = list(imp_csv.columns) - _, module_gene_symbols = load_module_gene_mapping(args.module_data_dir) + _, module_gene_symbols = load_biomod_gene_mapping(args.module_data_dir) print(f"Importance matrix: {importance.shape}") print(f"Cell types: {len(label_classes)}") diff --git a/BioMod/interpretability/module_importance/masking_ablation.py b/BioMod/interpretability/biomod_importance/masking_ablation.py similarity index 97% rename from BioMod/interpretability/module_importance/masking_ablation.py rename to BioMod/interpretability/biomod_importance/masking_ablation.py index ac005bab82d36c7b7b3ccec2894a0759a75e6c7b..d3bc830dc2a462725cc9046cc22eddaf8cbc734b 100644 --- a/BioMod/interpretability/module_importance/masking_ablation.py +++ b/BioMod/interpretability/biomod_importance/masking_ablation.py @@ -29,14 +29,14 @@ SCRIPT_DIR = Path(__file__).resolve().parent INTERP_DIR = SCRIPT_DIR.parent -PERFORMER_MODULE_ROOT = INTERP_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = INTERP_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent -for p in (str(SCRIPT_DIR), str(PERFORMER_MODULE_ROOT), str(SCLONG_ROOT)): +for p in (str(SCRIPT_DIR), str(BIOMOD_ROOT), str(SCLONG_ROOT)): if p not in sys.path: sys.path.insert(0, p) -from module_importance import ( +from biomod_importance import ( CellDataset, _classify_from_tokens, load_finetuned_model, @@ -63,7 +63,7 @@ def encode_all_cells(model, dataloader, preprocessor, reindex_state, device): if model.head_type == "cls": module_tokens = latent[:, 1:, :] else: - module_tokens = model._restore_module_order(latent, ids_restore) + module_tokens = model._restore_biomod_order(latent, ids_restore) all_tokens.append(module_tokens.cpu()) all_labels.append(y_batch.numpy()) @@ -248,7 +248,7 @@ def parse_args(): p = argparse.ArgumentParser(description="Masking ablation curves") p.add_argument("--finetune_checkpoint", required=True) p.add_argument("--config_pkl", required=True) - p.add_argument("--module_data_dir", required=True) + p.add_argument("--biomod_data_dir", "--module_data_dir", required=True, dest='module_data_dir') p.add_argument("--gene_list_path", required=True) p.add_argument("--gene_mapping_path", required=True) p.add_argument("--gene2vec_data_path", required=True) @@ -290,8 +290,8 @@ def main(): setattr(args, attr, str(Path(val).resolve())) logger.info("Loading model...") - model, module_data, label_classes, head_type = load_finetuned_model(args, device) - num_modules = len(module_data) + model, biomod_data, label_classes, head_type = load_finetuned_model(args, device) + num_modules = len(biomod_data) logger.info("Loading test data...") X_test, y_test, le, preprocessor, reindex_state = load_test_data( diff --git a/BioMod/interpretability/module_importance/pub_utils.py b/BioMod/interpretability/biomod_importance/pub_utils.py similarity index 94% rename from BioMod/interpretability/module_importance/pub_utils.py rename to BioMod/interpretability/biomod_importance/pub_utils.py index cd6234d7399f265758ec226d49885039c36237ee..b5b784d637590d5d11310f6920fc401b07216d85 100644 --- a/BioMod/interpretability/module_importance/pub_utils.py +++ b/BioMod/interpretability/biomod_importance/pub_utils.py @@ -140,15 +140,15 @@ def load_importance_results(results_dir) -> dict: return data -def load_module_gene_mapping(module_data_dir) -> Tuple[List[str], Dict[str, List[str]]]: - """Load merged_modules.json and return (module_names, module_gene_symbols). +def load_biomod_gene_mapping(module_data_dir) -> Tuple[List[str], Dict[str, List[str]]]: + """Load merged_biomods.json and return (module_names, module_gene_symbols). Returns: module_names: ordered list of module names module_gene_symbols: dict mapping module_name -> list of gene symbols """ module_data_dir = Path(module_data_dir) - with open(module_data_dir / "merged_modules.json") as f: + with open(module_data_dir / "merged_biomods.json") as f: modules = json.load(f) module_names = list(modules.keys()) module_gene_symbols = {} @@ -160,7 +160,7 @@ def load_module_gene_mapping(module_data_dir) -> Tuple[List[str], Dict[str, List return module_names, module_gene_symbols -def get_module_family(module_name: str) -> str: +def get_biomod_family(module_name: str) -> str: """Determine which family a module belongs to based on its name prefix.""" if module_name.startswith("module1_hallmarks"): return "Hallmarks" @@ -173,14 +173,14 @@ def get_module_family(module_name: str) -> str: return "Unknown" -def get_module_families(module_names: List[str]) -> List[str]: +def get_biomod_families(module_names: List[str]) -> List[str]: """Get family labels for a list of module names.""" - return [get_module_family(p) for p in module_names] + return [get_biomod_family(p) for p in module_names] # ─── Analysis Helpers ──────────────────────────────────────────────────────── -def find_gene_modules( +def find_gene_biomods( gene_symbol: str, module_gene_symbols: Dict[str, List[str]] ) -> List[str]: """Find all modules containing a given gene symbol.""" diff --git a/BioMod/interpretability/module_importance/random_baseline.py b/BioMod/interpretability/biomod_importance/random_baseline.py similarity index 96% rename from BioMod/interpretability/module_importance/random_baseline.py rename to BioMod/interpretability/biomod_importance/random_baseline.py index 18397f0fb20bff8406b7df18147914b811c80b68..5cca5bde4f765d48ff3a3b9f65933874d3bae175 100644 --- a/BioMod/interpretability/module_importance/random_baseline.py +++ b/BioMod/interpretability/biomod_importance/random_baseline.py @@ -27,14 +27,14 @@ SCRIPT_DIR = Path(__file__).resolve().parent INTERP_DIR = SCRIPT_DIR.parent -PERFORMER_MODULE_ROOT = INTERP_DIR.parent -SCLONG_ROOT = PERFORMER_MODULE_ROOT.parent +BIOMOD_ROOT = INTERP_DIR.parent +SCLONG_ROOT = BIOMOD_ROOT.parent -for p in (str(SCRIPT_DIR), str(PERFORMER_MODULE_ROOT), str(SCLONG_ROOT)): +for p in (str(SCRIPT_DIR), str(BIOMOD_ROOT), str(SCLONG_ROOT)): if p not in sys.path: sys.path.insert(0, p) -from module_importance import ( +from biomod_importance import ( CellDataset, _classify_from_tokens, load_finetuned_model, @@ -48,7 +48,7 @@ # ─── Mode A: Shuffle Modules (GPU) ────────────────────────────────────────── @torch.no_grad() -def compute_importance_shuffled_modules( +def compute_importance_shuffled_biomods( model, dataloader, preprocessor, reindex_state, num_modules, num_classes, device, seed, ): @@ -77,7 +77,7 @@ def compute_importance_shuffled_modules( if model.head_type == "cls": module_tokens = latent[:, 1:, :] else: - module_tokens = model._restore_module_order(latent, ids_restore) + module_tokens = model._restore_biomod_order(latent, ids_restore) # Permute module token order (breaks module identity) module_tokens_shuffled = module_tokens[:, perm, :] @@ -206,7 +206,7 @@ def parse_args(): # Model args (for shuffle_modules mode) p.add_argument("--finetune_checkpoint") p.add_argument("--config_pkl") - p.add_argument("--module_data_dir") + p.add_argument("--biomod_data_dir", "--module_data_dir", dest='module_data_dir') p.add_argument("--gene_list_path") p.add_argument("--gene_mapping_path") p.add_argument("--gene2vec_data_path") @@ -256,7 +256,7 @@ def main(): setattr(args, attr, str(Path(val).resolve())) logger.info("Loading model for shuffle_modules mode...") - model, module_data, label_classes, _ = load_finetuned_model(args, device) + model, biomod_data, label_classes, _ = load_finetuned_model(args, device) logger.info("Loading test data...") X_test, y_test, le, preprocessor, reindex_state = load_test_data( @@ -273,7 +273,7 @@ def main(): ) for pi in range(n_perms_a): logger.info(" Permutation %d/%d...", pi + 1, n_perms_a) - null_module_shuffle[pi] = compute_importance_shuffled_modules( + null_module_shuffle[pi] = compute_importance_shuffled_biomods( model, dataloader, preprocessor, reindex_state, num_modules, num_classes, device, seed=args.seed + pi + 1000, diff --git a/BioMod/interpretability/module_importance/tcell_reactome_analysis.py b/BioMod/interpretability/biomod_importance/tcell_reactome_analysis.py similarity index 100% rename from BioMod/interpretability/module_importance/tcell_reactome_analysis.py rename to BioMod/interpretability/biomod_importance/tcell_reactome_analysis.py diff --git a/BioMod/interpretability/module_text_alignment/DESIGN.md b/BioMod/interpretability/biomod_text_alignment/DESIGN.md similarity index 80% rename from BioMod/interpretability/module_text_alignment/DESIGN.md rename to BioMod/interpretability/biomod_text_alignment/DESIGN.md index b55785f0f41844cd695be3b5afea6ab0af477d7d..3715b16034958a4558e5b58f4234fb14f5154a90 100644 --- a/BioMod/interpretability/module_text_alignment/DESIGN.md +++ b/BioMod/interpretability/biomod_text_alignment/DESIGN.md @@ -11,15 +11,15 @@ gene names do not carry phrase-level semantics. | Script | Function | | --- | --- | -| `module_text_alignment.py` | Main script: RSA/Mantel test, CKA, and leave-one-out Ridge retrieval | -| `module_text_alignment_extended.py` | Extended analysis: Reactome broad-category RSA, Reactome description-based RSA, and Hallmark deep dive | -| `module_text_alignment_publication.py` | Publication-level analysis: six main figures, bootstrap CIs, multi-model comparison, cross-dataset validation, and deep analysis | +| `biomod_text_alignment.py` | Main script: RSA/Mantel test, CKA, and leave-one-out Ridge retrieval | +| `biomod_text_alignment_extended.py` | Extended analysis: Reactome broad-category RSA, Reactome description-based RSA, and Hallmark deep dive | +| `biomod_text_alignment_publication.py` | Publication-level analysis: six main figures, bootstrap CIs, multi-model comparison, cross-dataset validation, and deep analysis | | `fetch_reactome_descriptions.py` | Fetch `summaryText` for 491 pathways from the Reactome API | ## Commands Run from the BioMod repository root. Generate each `module_embeddings.npy` with -`BioMod/interpretability/module_geometry.py` before using it here. The historical +`BioMod/interpretability/biomod_geometry.py` before using it here. The historical `pretrain_subset` output requires a separately supplied pretraining dataset; the `pancreas_scib` example uses the bundled pancreas h5ad file. Existing result tables below describe historical runs; their raw output directories are not @@ -41,24 +41,24 @@ pip install sentence-transformers -i https://pypi.tuna.tsinghua.edu.cn/simple ```bash # pretrain_subset is recommended because averaging over 50k cells is more stable. -python BioMod/interpretability/module_text_alignment/module_text_alignment.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --n_permutations 1000 \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_v2 # pancreas_scib validates cross-dataset consistency. -python BioMod/interpretability/module_text_alignment/module_text_alignment.py \ - --module_embedding_path outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py \ + --biomod_embedding_path outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --n_permutations 1000 \ --output_dir outputs/interpretability/text_alignment/pancreas_scib_v2 # Quick mode: skip LOO retrieval and run only RSA + CKA. -python BioMod/interpretability/module_text_alignment/module_text_alignment.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --skip_loo \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_quick @@ -68,24 +68,24 @@ python BioMod/interpretability/module_text_alignment/module_text_alignment.py \ ```bash # Re-fetch if needed. Requires network access to reactome.org and takes about 2 minutes. -python BioMod/interpretability/module_text_alignment/fetch_reactome_descriptions.py \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/fetch_reactome_descriptions.py \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --output reactome_descriptions.json --delay 0.05 ``` The command writes refreshed descriptions to `reactome_descriptions.json` in the repository root. The analysis examples below use the bundled -`BioMod/interpretability/module_text_alignment/reactome_descriptions.json` instead; +`BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json` instead; set `--descriptions_file reactome_descriptions.json` to use the refreshed output. ### 4. Extended Analysis: Reactome Categories, Description-Based RSA, Hallmark Deep Dive ```bash -python BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ - --descriptions_file BioMod/interpretability/module_text_alignment/reactome_descriptions.json \ + --descriptions_file BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json \ --n_permutations 1000 \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_v2_extended ``` @@ -94,23 +94,23 @@ python BioMod/interpretability/module_text_alignment/module_text_alignment_exten ```bash # Basic run: one dataset and one model. -python BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ - --descriptions_file BioMod/interpretability/module_text_alignment/reactome_descriptions.json \ + --descriptions_file BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json \ --n_permutations 1000 \ --n_bootstrap 2000 \ --output_dir outputs/interpretability/text_alignment/publication # Full run: cross-dataset validation and multi-model comparison. -python BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_embedding_path2 outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ +python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_embedding_path2 outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --sentence_models_compare "sentence-transformers/all-mpnet-base-v2,sentence-transformers/all-MiniLM-L6-v2" \ - --descriptions_file BioMod/interpretability/module_text_alignment/reactome_descriptions.json \ + --descriptions_file BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json \ --n_permutations 1000 \ --n_bootstrap 2000 \ --output_dir outputs/interpretability/text_alignment/publication_full @@ -120,8 +120,8 @@ python BioMod/interpretability/module_text_alignment/module_text_alignment_publi | Argument | Description | | --- | --- | -| `--module_embedding_path` | `module_embeddings.npy` for the main dataset, from Experiment 1 | -| `--module_embedding_path2` | Optional second dataset for cross-dataset validation | +| `--biomod_embedding_path` | `module_embeddings.npy` for the main dataset, from Experiment 1 | +| `--biomod_embedding_path2` | Optional second dataset for cross-dataset validation | | `--sentence_models_compare` | Comma-separated model names for comparison, downloaded automatically from HuggingFace | | `--n_permutations` | Mantel-test permutations; 1000 is standard and 10000 gives more precise p-values | | `--n_bootstrap` | Bootstrap resampling iterations; 2000 is standard for 95% CIs | @@ -135,7 +135,7 @@ pip install sentence-transformers -i https://pypi.tuna.tsinghua.edu.cn/simple ## Output Files -### Main Experiment Outputs (`module_text_alignment.py`) +### Main Experiment Outputs (`biomod_text_alignment.py`) | File | Content | | --- | --- | @@ -149,7 +149,7 @@ pip install sentence-transformers -i https://pypi.tuna.tsinghua.edu.cn/simple | `retrieval_examples.csv` | Top-5 retrieval results for representative pathways | | `run.log` | Full log | -### Extended Analysis Outputs (`module_text_alignment_extended.py`) +### Extended Analysis Outputs (`biomod_text_alignment_extended.py`) | File | Content | | --- | --- | @@ -159,7 +159,7 @@ pip install sentence-transformers -i https://pypi.tuna.tsinghua.edu.cn/simple | `hallmarks_per_module_alignment.csv` | Per-module alignment ranking for 50 Hallmarks | | `run_extended.log` | Extended-analysis log | -### Publication-Level Outputs (`module_text_alignment_publication.py`) +### Publication-Level Outputs (`biomod_text_alignment_publication.py`) | File | Content | | --- | --- | diff --git a/BioMod/interpretability/module_text_alignment/MANUSCRIPT_SECTION.md b/BioMod/interpretability/biomod_text_alignment/MANUSCRIPT_SECTION.md similarity index 100% rename from BioMod/interpretability/module_text_alignment/MANUSCRIPT_SECTION.md rename to BioMod/interpretability/biomod_text_alignment/MANUSCRIPT_SECTION.md diff --git a/BioMod/interpretability/module_text_alignment/__init__.py b/BioMod/interpretability/biomod_text_alignment/__init__.py similarity index 100% rename from BioMod/interpretability/module_text_alignment/__init__.py rename to BioMod/interpretability/biomod_text_alignment/__init__.py diff --git a/BioMod/interpretability/module_text_alignment/module_text_alignment.py b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py similarity index 94% rename from BioMod/interpretability/module_text_alignment/module_text_alignment.py rename to BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py index f17d369217f4480b22e8f42cee72aec8eae24f25..bb18840a0bc96d11372ce811a9969671fa19dbeb 100644 --- a/BioMod/interpretability/module_text_alignment/module_text_alignment.py +++ b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py @@ -10,7 +10,7 @@ Pipeline -------- -1. Parse pathway names from merged_modules.json keys for Hallmarks (50) and +1. Parse pathway names from merged_biomods.json keys for Hallmarks (50) and Reactome (491) modules. Hotspot/Processed are excluded (no semantic names). 2. Encode pathway names with a biomedical sentence encoder. 3. Load cell-averaged module embeddings from experiment 1 output. @@ -58,16 +58,16 @@ HERE = Path(__file__).resolve().parent INTERP_DIR = HERE.parent -PERFORMER_MODULE = INTERP_DIR.parent -REPO_ROOT = PERFORMER_MODULE.parent -for p in (str(INTERP_DIR), str(PERFORMER_MODULE), str(REPO_ROOT)): +BIOMOD_SOURCE = INTERP_DIR.parent +REPO_ROOT = BIOMOD_SOURCE.parent +for p in (str(INTERP_DIR), str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) from interpretability.utils import ( MODULE_FAMILY_MAP, - load_module_names, - parse_module_family, + load_biomod_names, + parse_biomod_family, ) logger = logging.getLogger(__name__) @@ -79,7 +79,7 @@ def parse_pathway_text(module_name: str) -> Optional[str]: """Extract a human-readable pathway name from a module key.""" - family = parse_module_family(module_name) + family = parse_biomod_family(module_name) if family == "Hallmarks": raw = module_name.split("__", 1)[1] raw = raw.replace("HALLMARK_", "", 1) @@ -218,7 +218,7 @@ def compute_rsa_by_family( ) -> Dict[str, Dict[str, float]]: """RSA computed separately for each module family (Hallmarks vs Reactome).""" results = {} - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] for fam in ["Hallmarks", "Reactome"]: mask = np.array([f == fam for f in families]) if mask.sum() < 10: @@ -447,11 +447,11 @@ def parse_args() -> argparse.Namespace: description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) - parser.add_argument("--module_embedding_path", type=str, required=True, - help="Path to module_embeddings.npy from experiment 1.") - parser.add_argument("--module_json", type=str, - default=str(REPO_ROOT / "model_dependencies" / "module_data_all" / "merged_modules.json"), - help="Path to merged_modules.json.") + parser.add_argument("--biomod_embedding_path", "--module_embedding_path", type=str, required=True, + help="Path to module_embeddings.npy from experiment 1.", dest='module_embedding_path') + parser.add_argument("--biomod_json", "--module_json", type=str, + default=str(REPO_ROOT / "model_dependencies" / "biomod_data_all" / "merged_biomods.json"), + help="Path to merged_biomods.json.", dest='module_json') parser.add_argument("--descriptions_file", type=str, default=None, help="Optional JSON with extended pathway descriptions.") parser.add_argument("--sentence_model", type=str, @@ -487,7 +487,7 @@ def main(): # --- Load module names and embeddings --- logger.info("Loading module names from %s", args.module_json) - module_names = load_module_names(args.module_json) + module_names = load_biomod_names(args.module_json) logger.info("Total modules: %d", len(module_names)) logger.info("Loading module embeddings from %s", args.module_embedding_path) diff --git a/BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py similarity index 93% rename from BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py rename to BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py index 3ec84212bd4c5524e32f7e34cbb080f4c3abfc21..afe9bd920eb2e16bfb60244e2ad70a964a8eb351 100644 --- a/BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py +++ b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py @@ -6,9 +6,9 @@ 3. Hallmarks deep dive (per-module alignment scores, heatmap) Usage (run from the BioMod repository root): - python BioMod/interpretability/module_text_alignment/module_text_alignment_extended.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ + python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_extended.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --output_dir outputs/interpretability/text_alignment/pretrain_subset_v2_extended """ @@ -36,14 +36,14 @@ HERE = Path(__file__).resolve().parent INTERP_DIR = HERE.parent -PERFORMER_MODULE = INTERP_DIR.parent -REPO_ROOT = PERFORMER_MODULE.parent -for p in (str(INTERP_DIR), str(PERFORMER_MODULE), str(REPO_ROOT)): +BIOMOD_SOURCE = INTERP_DIR.parent +REPO_ROOT = BIOMOD_SOURCE.parent +for p in (str(INTERP_DIR), str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) -from interpretability.utils import load_module_names, parse_module_family -from module_text_alignment.module_text_alignment import ( +from interpretability.utils import load_biomod_names, parse_biomod_family +from biomod_text_alignment.biomod_text_alignment import ( build_text_descriptions, compute_cka, compute_rsa, @@ -150,7 +150,7 @@ def reactome_category_rsa( logger.info("Analysis 1: Reactome category-level RSA") logger.info("=" * 50) - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] reactome_mask = np.array([f == "Reactome" for f in families]) reactome_idx = np.where(reactome_mask)[0] @@ -259,7 +259,7 @@ def reactome_with_descriptions( with open(descriptions_file, "r", encoding="utf-8") as f: descriptions = json.load(f) - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] reactome_mask = np.array([f == "Reactome" for f in families]) reactome_idx = np.where(reactome_mask)[0] @@ -317,7 +317,7 @@ def hallmarks_deep_dive( logger.info("Analysis 3: Hallmarks deep dive") logger.info("=" * 50) - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -445,9 +445,9 @@ def hallmarks_deep_dive( def parse_args(): parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--module_embedding_path", type=str, required=True) - parser.add_argument("--module_json", type=str, - default=str(REPO_ROOT / "model_dependencies" / "module_data_all" / "merged_modules.json")) + parser.add_argument("--biomod_embedding_path", "--module_embedding_path", type=str, required=True, dest='module_embedding_path') + parser.add_argument("--biomod_json", "--module_json", type=str, + default=str(REPO_ROOT / "model_dependencies" / "biomod_data_all" / "merged_biomods.json"), dest='module_json') parser.add_argument("--descriptions_file", type=str, default=None, help="JSON with Reactome summaryText descriptions.") parser.add_argument("--sentence_model", type=str, @@ -474,7 +474,7 @@ def main(): ) # Load data - module_names = load_module_names(args.module_json) + module_names = load_biomod_names(args.module_json) all_embeddings = np.load(args.module_embedding_path) indices, names, texts = build_text_descriptions(module_names) module_emb = all_embeddings[indices] diff --git a/BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py similarity index 93% rename from BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py rename to BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py index 470064c2f4a744245810a0ef0bb539a48470ca39..1bd6acd012649a4a286fa24a82663e80ecea5048 100644 --- a/BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py +++ b/BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py @@ -4,13 +4,13 @@ Supports multi-model comparison and cross-dataset validation. Usage (run from the BioMod repository root): - python BioMod/interpretability/module_text_alignment/module_text_alignment_publication.py \ - --module_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ - --module_embedding_path2 outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ - --module_json model_dependencies/module_data_all/merged_modules.json \ + python BioMod/interpretability/biomod_text_alignment/biomod_text_alignment_publication.py \ + --biomod_embedding_path outputs/interpretability/geometry/pretrain_subset/module_embeddings.npy \ + --biomod_embedding_path2 outputs/interpretability/geometry/pancreas_scib/module_embeddings.npy \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --sentence_model pritamdeka/S-PubMedBert-MS-MARCO \ --sentence_models_compare "sentence-transformers/all-mpnet-base-v2" \ - --descriptions_file BioMod/interpretability/module_text_alignment/reactome_descriptions.json \ + --descriptions_file BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json \ --n_permutations 1000 \ --n_bootstrap 2000 \ --output_dir outputs/interpretability/text_alignment/publication @@ -40,19 +40,19 @@ HERE = Path(__file__).resolve().parent INTERP_DIR = HERE.parent -PERFORMER_MODULE = INTERP_DIR.parent -REPO_ROOT = PERFORMER_MODULE.parent -for p in (str(HERE), str(INTERP_DIR), str(PERFORMER_MODULE), str(REPO_ROOT)): +BIOMOD_SOURCE = INTERP_DIR.parent +REPO_ROOT = BIOMOD_SOURCE.parent +for p in (str(HERE), str(INTERP_DIR), str(BIOMOD_SOURCE), str(REPO_ROOT)): if p not in sys.path: sys.path.insert(0, p) from interpretability.utils import ( HALLMARK_PROCESS, - load_module_names, + load_biomod_names, parse_hallmark_process, - parse_module_family, + parse_biomod_family, ) -from module_text_alignment.module_text_alignment import ( +from biomod_text_alignment.biomod_text_alignment import ( build_text_descriptions, compute_cka, compute_rsa, @@ -60,7 +60,7 @@ mantel_test, permutation_test_cka, ) -from module_text_alignment.module_text_alignment_extended import ( +from biomod_text_alignment.biomod_text_alignment_extended import ( REACTOME_CATEGORIES, categorize_reactome, ) @@ -166,8 +166,8 @@ def bootstrap_rsa( float(np.percentile(boot_rs, 97.5)) -def get_module_sizes(module_json: str) -> Dict[str, int]: - """Get number of genes per module from merged_modules.json.""" +def get_biomod_sizes(module_json: str) -> Dict[str, int]: + """Get number of genes per module from merged_biomods.json.""" with open(module_json, "r", encoding="utf-8") as f: modules = json.load(f) return {name: len(genes) for name, genes in modules.items()} @@ -182,12 +182,12 @@ def parse_args() -> argparse.Namespace: description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) - parser.add_argument("--module_embedding_path", type=str, required=True, - help="Primary module_embeddings.npy (e.g. pretrain_subset)") - parser.add_argument("--module_embedding_path2", type=str, default=None, - help="Second dataset module_embeddings.npy for cross-dataset comparison") - parser.add_argument("--module_json", type=str, - default=str(REPO_ROOT / "model_dependencies" / "module_data_all" / "merged_modules.json")) + parser.add_argument("--biomod_embedding_path", "--module_embedding_path", type=str, required=True, + help="Primary module_embeddings.npy (e.g. pretrain_subset)", dest='module_embedding_path') + parser.add_argument("--biomod_embedding_path2", "--module_embedding_path2", type=str, default=None, + help="Second dataset module_embeddings.npy for cross-dataset comparison", dest='module_embedding_path2') + parser.add_argument("--biomod_json", "--module_json", type=str, + default=str(REPO_ROOT / "model_dependencies" / "biomod_data_all" / "merged_biomods.json"), dest='module_json') parser.add_argument("--descriptions_file", type=str, default=None) parser.add_argument("--sentence_model", type=str, default="pritamdeka/S-PubMedBert-MS-MARCO") @@ -314,7 +314,7 @@ def fig2_hallmarks_heatmaps( output_dir: Path, ) -> Dict: """Hallmarks 50x50 similarity matrices with rank-transform and clustering.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -433,7 +433,7 @@ def fig3_per_hallmark_alignment( seed: int = 42, ) -> Dict: """Lollipop chart of per-hallmark alignment scores, colored by process.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -531,7 +531,7 @@ def fig4_category_alignment( output_dir: Path, ) -> Dict: """Violin/box plot of alignment scores grouped by MSigDB process category.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -634,7 +634,7 @@ def fig5_granularity_effect( output_dir: Path = None, ) -> Dict: """RSA at multiple granularity levels: fine → category → super-category.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] reactome_mask = np.array([f == "Reactome" for f in families]) hallmark_mask = np.array([f == "Hallmarks" for f in families]) reactome_idx = np.where(reactome_mask)[0] @@ -761,7 +761,7 @@ def fig6_model_comparison( output_dir: Path = None, ) -> Dict: """Grouped bar chart comparing RSA across models and datasets.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -861,7 +861,7 @@ def fig6_model_comparison( # Deep Analysis: Alignment vs Module Size # ============================================================ -def analysis_alignment_vs_module_size( +def analysis_alignment_vs_biomod_size( module_emb: np.ndarray, text_emb: np.ndarray, names: List[str], @@ -870,7 +870,7 @@ def analysis_alignment_vs_module_size( output_dir: Path, ) -> Dict: """Scatter: n_genes vs alignment score. Tests if larger modules align better.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -891,7 +891,7 @@ def analysis_alignment_vs_module_size( per_module_corr[i] = r # Module sizes - module_sizes = get_module_sizes(module_json) + module_sizes = get_biomod_sizes(module_json) h_sizes = np.array([module_sizes.get(n, 0) for n in h_names]) # Correlation @@ -945,7 +945,7 @@ def analysis_negative_results( n_show: int = 5, ) -> Dict: """For poorly-aligned modules, show neighbor divergence.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -1022,7 +1022,7 @@ def analysis_knn_overlap( output_dir: Path = None, ) -> Dict: """kNN overlap with bootstrap CI and per-category breakdown.""" - families = [parse_module_family(n) for n in names] + families = [parse_biomod_family(n) for n in names] hallmark_mask = np.array([f == "Hallmarks" for f in families]) hallmark_idx = np.where(hallmark_mask)[0] @@ -1103,13 +1103,13 @@ def main(): logger.info("Args: %s", vars(args)) # ---- Load data ---- - module_names = load_module_names(args.module_json) + module_names = load_biomod_names(args.module_json) all_module_emb = np.load(args.module_embedding_path) logger.info("Loaded module embeddings: %s from %s", all_module_emb.shape, args.module_embedding_path) # Filter to semantic modules (Hallmarks + Reactome) - families = [parse_module_family(n) for n in module_names] + families = [parse_biomod_family(n) for n in module_names] semantic_mask = np.array([f in ("Hallmarks", "Reactome") for f in families]) semantic_idx = np.where(semantic_mask)[0] module_emb = all_module_emb[semantic_idx] @@ -1205,7 +1205,7 @@ def main(): # ---- Deep Analysis ---- logger.info("Running deep analysis: alignment vs module size...") - metrics_size = analysis_alignment_vs_module_size( + metrics_size = analysis_alignment_vs_biomod_size( module_emb, text_emb, names, texts, args.module_json, output_dir, ) diff --git a/BioMod/interpretability/module_text_alignment/fetch_reactome_descriptions.py b/BioMod/interpretability/biomod_text_alignment/fetch_reactome_descriptions.py similarity index 80% rename from BioMod/interpretability/module_text_alignment/fetch_reactome_descriptions.py rename to BioMod/interpretability/biomod_text_alignment/fetch_reactome_descriptions.py index f0af9684cfe173a83a9b56c27dd75141cb2924bc..41efcd22d52efe30b697850c24d31d99d3408307 100644 --- a/BioMod/interpretability/module_text_alignment/fetch_reactome_descriptions.py +++ b/BioMod/interpretability/biomod_text_alignment/fetch_reactome_descriptions.py @@ -1,11 +1,11 @@ """Fetch Reactome pathway descriptions via the Content Service REST API. Produces a JSON file mapping module_name -> summaryText that can be passed -to module_text_alignment_extended.py via --descriptions_file. +to biomod_text_alignment_extended.py via --descriptions_file. Usage (run from the BioMod repository root): - python BioMod/interpretability/module_text_alignment/fetch_reactome_descriptions.py \ - --module_json model_dependencies/module_data_all/merged_modules.json \ + python BioMod/interpretability/biomod_text_alignment/fetch_reactome_descriptions.py \ + --biomod_json model_dependencies/biomod_data_all/merged_biomods.json \ --output reactome_descriptions.json Requires internet access to https://reactome.org/ContentService/ @@ -24,12 +24,12 @@ HERE = Path(__file__).resolve().parent INTERP_DIR = HERE.parent -PERFORMER_MODULE = INTERP_DIR.parent -for p in (str(INTERP_DIR), str(PERFORMER_MODULE)): +BIOMOD_SOURCE = INTERP_DIR.parent +for p in (str(INTERP_DIR), str(BIOMOD_SOURCE)): if p not in sys.path: sys.path.insert(0, p) -from interpretability.utils import load_module_names, parse_module_family +from interpretability.utils import load_biomod_names, parse_biomod_family REACTOME_API = "https://reactome.org/ContentService/data/query" @@ -56,14 +56,14 @@ def fetch_summary(rhs_id: str, retries: int = 3) -> str: def main(): parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--module_json", type=str, required=True) + parser.add_argument("--biomod_json", "--module_json", type=str, required=True, dest='module_json') parser.add_argument("--output", type=str, default="reactome_descriptions.json") parser.add_argument("--delay", type=float, default=0.1, help="Delay between API calls (seconds)") args = parser.parse_args() - module_names = load_module_names(args.module_json) - reactome_modules = [n for n in module_names if parse_module_family(n) == "Reactome"] + module_names = load_biomod_names(args.module_json) + reactome_modules = [n for n in module_names if parse_biomod_family(n) == "Reactome"] print(f"Reactome modules to fetch: {len(reactome_modules)}") descriptions = {} diff --git a/BioMod/interpretability/module_text_alignment/reactome_descriptions.json b/BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json similarity index 100% rename from BioMod/interpretability/module_text_alignment/reactome_descriptions.json rename to BioMod/interpretability/biomod_text_alignment/reactome_descriptions.json diff --git a/BioMod/interpretability/utils.py b/BioMod/interpretability/utils.py index 0ad1ba60fb11f6545b854ee6f263e8058ffc2691..c1eab06100490240b7700265b73f2d1b97729a5a 100644 --- a/BioMod/interpretability/utils.py +++ b/BioMod/interpretability/utils.py @@ -1,7 +1,7 @@ """Shared helpers for module interpretability experiments. Provides: - * module family / hallmark-process parsing from merged_modules.json keys + * module family / hallmark-process parsing from merged_biomods.json keys * a generic in-memory dataset that yields raw [G_input] expression rows, compatible with DataPreprocessor.reindex_tensor_to_model_space """ @@ -86,7 +86,7 @@ } -def parse_module_family(module_name: str) -> str: +def parse_biomod_family(module_name: str) -> str: head = module_name.split("__", 1)[0] return MODULE_FAMILY_MAP.get(head, "Other") @@ -98,18 +98,18 @@ def parse_hallmark_process(module_name: str) -> Optional[str]: return HALLMARK_PROCESS.get(short) -def build_module_metadata(module_names: Sequence[str]): +def build_biomod_metadata(module_names: Sequence[str]): rows = [] for name in module_names: rows.append({ "module_name": name, - "family": parse_module_family(name), + "family": parse_biomod_family(name), "hallmark_process": parse_hallmark_process(name), }) return rows -def load_module_names(module_json_path: str) -> List[str]: +def load_biomod_names(module_json_path: str) -> List[str]: with open(module_json_path, "r", encoding="utf-8") as handle: return list(json.load(handle).keys()) diff --git a/BioMod/models_performer_module.py b/BioMod/models_biomod.py similarity index 95% rename from BioMod/models_performer_module.py rename to BioMod/models_biomod.py index 7752932147bad6968924fc75001e89fb503d73ef..365012e9d4401f780d830ecec8e0c6361b8bd701 100644 --- a/BioMod/models_performer_module.py +++ b/BioMod/models_biomod.py @@ -1,4 +1,4 @@ -"""Module-based Performer encoder with MAE-style masking and configurable +"""BioMod encoder for gene groups with MAE-style masking and configurable reconstruction decoders or direct prediction heads.""" import sys @@ -16,21 +16,21 @@ sys.path.insert(0, model_dependencies_dir) try: - from performer_pytorch_cont.ding_models import ( - Performer, + from pytorch_cont.ding_models import ( + Performer as BioModEncoder, Always, cast_tuple, ) except ImportError: try: - from performer_pytorch_cont.performer_pytorch import ( - Performer, + from pytorch_cont.pytorch import ( + Performer as BioModEncoder, Always, cast_tuple, ) except ImportError: from performer_pytorch.performer_pytorch import ( - Performer, + Performer as BioModEncoder, Always, cast_tuple, ) @@ -41,7 +41,7 @@ import numpy as np -def _migrate_legacy_module_name(value: str) -> str: +def _migrate_legacy_biomod_name(value: str) -> str: """Translate serialized pre-rename names without changing their semantics.""" replacements = ( ("PATHCH", "MODULE"), @@ -59,13 +59,13 @@ def _migrate_legacy_module_name(value: str) -> str: return value -def migrate_legacy_module_config(config: Dict[str, object]) -> Dict[str, object]: +def migrate_legacy_biomod_config(config: Dict[str, object]) -> Dict[str, object]: """Return a module-named copy of an older serialized configuration.""" def translate(value): if isinstance(value, dict): migrated = {} for key, item in value.items(): - new_key = _migrate_legacy_module_name(key) if isinstance(key, str) else key + new_key = _migrate_legacy_biomod_name(key) if isinstance(key, str) else key if new_key in migrated: raise ValueError( f"Legacy configuration migration would duplicate key {new_key!r}" @@ -77,13 +77,13 @@ def translate(value): if isinstance(value, tuple): return tuple(translate(item) for item in value) if isinstance(value, str): - return _migrate_legacy_module_name(value) + return _migrate_legacy_biomod_name(value) return value return translate(dict(config)) -def migrate_legacy_module_state_dict(state_dict): +def migrate_legacy_biomod_state_dict(state_dict): """Translate old state-dict key names before PyTorch performs strict loading.""" try: migrated = state_dict.__class__() @@ -91,7 +91,7 @@ def migrate_legacy_module_state_dict(state_dict): migrated = {} for key, value in state_dict.items(): - new_key = _migrate_legacy_module_name(key) if isinstance(key, str) else key + new_key = _migrate_legacy_biomod_name(key) if isinstance(key, str) else key if new_key in migrated: # If both spellings are supplied, prefer the explicit new spelling. if key == new_key: @@ -101,7 +101,7 @@ def migrate_legacy_module_state_dict(state_dict): if hasattr(state_dict, "_metadata"): migrated._metadata = { - _migrate_legacy_module_name(key): value + _migrate_legacy_biomod_name(key): value for key, value in state_dict._metadata.items() } return migrated @@ -135,13 +135,13 @@ def normalize_fusion_method(fusion_method: str) -> str: ) -def sanitize_performer_module_config( +def sanitize_biomod_config( config: Dict[str, object], *, source: str = "config", log_fn: Optional[Callable[[str], None]] = None, ) -> Dict[str, object]: - cleaned = migrate_legacy_module_config(config) + cleaned = migrate_legacy_biomod_config(config) legacy_mode = str(cleaned.get("extra_gene_mode", "none")).lower() legacy_num = int(cleaned.get("extra_gene_num", 0) or 0) if legacy_mode != "none" or legacy_num > 0: @@ -160,7 +160,7 @@ def sanitize_performer_module_config( return cleaned -# ModuleEmbedding is implemented here to support three expression-fusion methods. +# BioModEmbedding is implemented here to support three expression-fusion methods. # from sclong_module.models_module import ModuleToGeneDecoder @@ -220,7 +220,7 @@ def forward(self, query: torch.Tensor, memory: torch.Tensor) -> torch.Tensor: return query -class ModuleEmbedding(nn.Module): +class BioModEmbedding(nn.Module): """Gene embedding, MLP, and attention pooling produce a module token. Expression fusion supports three methods: @@ -823,8 +823,8 @@ def forward( return self._forward_bucketed(expr, return_module_targets) -class PerformerEncoderOnly_Module(nn.Module): - """Performer encoder for module inputs with configurable masking and decoding. +class BioMod(nn.Module): + """BioMod encoder for gene-group inputs with configurable masking and decoding. use_gene2vec=True initializes from supplied pretrained gene embeddings; False uses random gene embeddings for ablation experiments.""" @@ -996,7 +996,7 @@ def __init__( print(f"[INFO] use_gene2vec=False: Using randomly initialized gene embeddings (dim={gene_embed_dim})") # Module embedding layer. - self.module_embed = ModuleEmbedding( + self.module_embed = BioModEmbedding( module_sets=module_sets, num_genes=num_genes, gene_embed_dim=gene_embed_dim, @@ -1054,7 +1054,7 @@ def __init__( self.dropout = nn.Dropout(emb_dropout) # Performer encoder. - self.encoder = Performer( + self.encoder = BioModEncoder( base_dim, enc_depth, enc_heads, enc_dim_head, local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, @@ -1090,7 +1090,7 @@ def __init__( if self.decoder_type == "performer": self.decoder_pos_emb = nn.Parameter(torch.zeros(1, pos_tokens, self.dec_embed_dim)) self.decoder_dropout = nn.Dropout(emb_dropout) - self.decoder = Performer( + self.decoder = BioModEncoder( self.dec_embed_dim, dec_depth, dec_heads, dec_dim_head, local_attn_heads=cast_tuple(0), local_window_size=256, causal=False, ff_mult=dec_ff_mult, nb_features=None, feature_redraw_interval=feature_redraw_interval, @@ -1203,7 +1203,7 @@ def _init_weights(self) -> None: nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) - def _sample_module_mask( + def _sample_biomod_mask( self, batch_size: int, seq_len: int, @@ -1237,7 +1237,7 @@ def random_masking(self, x: torch.Tensor, mask_ratio: float) -> Tuple[torch.Tens ids_restore = [3, 9, 1, 7, 2, 8, 4, 6, 5, 0] is the inverse permutation. For example, original module 0 occupies position 3 after shuffling.""" b, l, d = x.shape - mask, ids_shuffle, ids_restore = self._sample_module_mask( + mask, ids_shuffle, ids_restore = self._sample_biomod_mask( batch_size=b, seq_len=l, device=x.device, @@ -1260,7 +1260,7 @@ def replace_masking( raise RuntimeError("replace_masking requires encoder_mask_token to be initialized.") b, l, _ = x.shape - mask, _, _ = self._sample_module_mask( + mask, _, _ = self._sample_biomod_mask( batch_size=b, seq_len=l, device=x.device, @@ -1271,7 +1271,7 @@ def replace_masking( x_masked = torch.where(mask.bool().unsqueeze(-1), mask_tokens, x) return x_masked, mask, ids_restore - def _add_module_position_embedding(self, x: torch.Tensor) -> torch.Tensor: + def _add_biomod_position_embedding(self, x: torch.Tensor) -> torch.Tensor: pos = self.pos_emb[:, 1:, :] if self.use_cls else self.pos_emb if pos.shape[1] != x.shape[1]: raise RuntimeError( @@ -1306,10 +1306,10 @@ def forward_encoder( if self.mask_style == "replace": # Replace token contents, then add module-ID embeddings while retaining all positions. x, mask, ids_restore = self.replace_masking(x, mask_ratio) - x = self._add_module_position_embedding(x) + x = self._add_biomod_position_embedding(x) else: # MAE path: add module-ID embeddings, then drop masked modules. - x = self._add_module_position_embedding(x) + x = self._add_biomod_position_embedding(x) x, mask, ids_restore = self.random_masking(x, mask_ratio) # Sparse module gating (1B): suppress unimportant modules @@ -1361,7 +1361,7 @@ def _build_full_tokens(self, x: torch.Tensor, ids_restore: torch.Tensor) -> torc return torch.cat([cls_token, x_], dim=1) return x_ - def _aggregate_module_to_gene( + def _aggregate_biomod_to_gene( self, module_feats: torch.Tensor, ) -> torch.Tensor: @@ -1383,13 +1383,13 @@ def _aggregate_module_to_gene( gene_counts = gene_counts.clamp(min=1.0).unsqueeze(-1) # (B, G, 1) return gene_feats / gene_counts - def _predict_from_module_features( + def _predict_from_biomod_features( self, module_feats: torch.Tensor, ) -> Union[List[torch.Tensor], torch.Tensor]: """module_feats: (B, L, D)""" if self.recon_target_level == "gene" and self.decoder_gene_agg: - gene_feats = self._aggregate_module_to_gene(module_feats) # (B, G, D) + gene_feats = self._aggregate_biomod_to_gene(module_feats) # (B, G, D) assert self.gene_w is not None and self.gene_b is not None # Apply a separate linear projection to each gene. preds = torch.einsum("bgd,gdk->bgk", gene_feats, self.gene_w).squeeze(-1) @@ -1445,9 +1445,9 @@ def forward_decoder_simple( Returns per-module predictions, or a (B, G) tensor for decoder_gene_agg.""" x_full = self._build_full_tokens(x, ids_restore) module_feats = x_full[:, 1:, :] if self.use_cls else x_full - return self._predict_from_module_features(module_feats) + return self._predict_from_biomod_features(module_feats) - def forward_decoder_performer( + def forward_decoder_biomod( self, x: torch.Tensor, ids_restore: torch.Tensor, @@ -1459,7 +1459,7 @@ def forward_decoder_performer( x_full = self.decoder(x_full, pos_emb=self.layer_pos_emb(x_full)) x_full = self.decoder_norm(x_full) module_feats = x_full[:, 1:, :] if self.use_cls else x_full - return self._predict_from_module_features(module_feats) + return self._predict_from_biomod_features(module_feats) def forward_decoder_cross_attn( self, @@ -1508,14 +1508,14 @@ def forward_decoder_cross_attn( masked_queries = self.decoder_norm(masked_queries).to(dtype=module_feats.dtype) module_feats[mask_bool] = masked_queries.reshape(-1, dim) - return self._predict_from_module_features(module_feats) + return self._predict_from_biomod_features(module_feats) def forward_predictor_from_encoder( self, x: torch.Tensor, ) -> Union[List[torch.Tensor], torch.Tensor]: module_feats = x[:, 1:, :] if self.use_cls else x - return self._predict_from_module_features(module_feats) + return self._predict_from_biomod_features(module_feats) def compute_loss( self, @@ -1618,7 +1618,7 @@ def forward( return out if self.decoder_type == "performer": - preds = self.forward_decoder_performer(latent, ids_restore) + preds = self.forward_decoder_biomod(latent, ids_restore) elif self.decoder_type == "cross_attn": preds = self.forward_decoder_cross_attn(latent, mask, ids_restore) elif self.decoder_type == "simple": @@ -1643,7 +1643,7 @@ def get_last_loss_breakdown(self) -> dict: def load_state_dict(self, state_dict, strict: bool = True, assign: bool = False): """Load current or pre-rename weights under the module-named interface.""" return super().load_state_dict( - migrate_legacy_module_state_dict(state_dict), + migrate_legacy_biomod_state_dict(state_dict), strict=strict, assign=assign, ) diff --git a/BioMod/requirements.txt b/BioMod/requirements.txt index 9267c890a74abcc5a801607214b36706851925d3..5d9e6a8e92d532f071bcde35d9fd3f72a67c7fcf 100644 --- a/BioMod/requirements.txt +++ b/BioMod/requirements.txt @@ -1,4 +1,4 @@ -# PerformerModule Python dependencies. +# BioMod Python dependencies. # # Recommended use: # 1. Prefer `conda env create -f environment.yml` for a clean GPU setup. @@ -37,7 +37,7 @@ torch-sparse==0.6.18+pt26cu124 torch-cluster==1.6.3+pt26cu124 torch-spline-conv==1.2.2+pt26cu124 -# PerformerModule model utilities +# BioMod model utilities einops==0.8.1 local-attention==1.11.2 hickle>=5.0.0 diff --git a/BioMod/toturials/cell_annotation.ipynb b/BioMod/toturials/cell_annotation.ipynb index 208f64edba66e5fa8027833318c77f30369379a8..bee929aa4a80f71fa2f5e0029e387cdc1e4c48c4 100644 --- a/BioMod/toturials/cell_annotation.ipynb +++ b/BioMod/toturials/cell_annotation.ipynb @@ -24,9 +24,9 @@ "source": [ "## Inputs and workflow\n", "\n", - "- Entry point: `BioMod/cell_annotation/performer_module_finetune.py`.\n", + "- Entry point: `BioMod/cell_annotation/biomod_finetune.py`.\n", "- Dataset: `data/downstream/final_data/Zheng68K.h5ad`.\n", - "- Model weights and matching configuration: the `.pth` / `.pkl` pair under `weight/0810model/`.\n", + "- Model weights and matching configuration: the `.pth` / `.pkl` pair under `weight/`.\n", "- Gene vocabulary, mapping, embeddings, and module definitions: `model_dependencies/`.\n", "- Results: `tutorial_runs/cell_annotation_zheng68k_biomod/`.\n", "\n", @@ -54,7 +54,7 @@ " print(obj)\n", "\n", "PROJECT_ROOT = Path('../..').resolve()\n", - "if not (PROJECT_ROOT / 'BioMod' / 'models_performer_module.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", + "if not (PROJECT_ROOT / 'BioMod' / 'models_biomod.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", " raise FileNotFoundError(\n", " 'Project root not found. Start Jupyter from BioMod/toturials, or set '\n", " 'PROJECT_ROOT to your repository root (use Path(\".\").resolve() if '\n", @@ -64,14 +64,14 @@ "\n", "SOURCE_DIR = PROJECT_ROOT / 'BioMod'\n", "ASSET_ROOT = PROJECT_ROOT / 'model_dependencies'\n", - "SCRIPT = SOURCE_DIR / 'cell_annotation' / 'performer_module_finetune.py'\n", + "SCRIPT = SOURCE_DIR / 'cell_annotation' / 'biomod_finetune.py'\n", "DATA_PATH = PROJECT_ROOT / 'data' / 'downstream' / 'final_data' / 'Zheng68K.h5ad'\n", "\n", "# Keep the BioMod model weights and their matching configuration together.\n", - "WEIGHT_DIR = PROJECT_ROOT / 'weight' / '0810model'\n", - "CONFIG_PKL = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2.pkl'\n", - "CKPT_PATH = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2_epoch0.pth'\n", - "MODULE_DATA_DIR = ASSET_ROOT / 'module_data_all'\n", + "WEIGHT_DIR = PROJECT_ROOT / 'weight'\n", + "CONFIG_PKL = WEIGHT_DIR / 'BioMod.pkl'\n", + "CKPT_PATH = WEIGHT_DIR / 'BioMod.pth'\n", + "MODULE_DATA_DIR = ASSET_ROOT / 'biomod_data_all'\n", "GENE_LIST = ASSET_ROOT / 'selected_genes_27k.txt'\n", "GENE_MAP = ASSET_ROOT / 'gene2ens.csv'\n", "GENE2VEC = ASSET_ROOT / 'selected_gene2vec_27k.npy'\n", @@ -95,8 +95,8 @@ "source": [ "required_files = [\n", " SCRIPT, DATA_PATH, CONFIG_PKL, CKPT_PATH, GENE_LIST, GENE_MAP, GENE2VEC,\n", - " SOURCE_DIR / 'models_performer_module.py',\n", - " MODULE_DATA_DIR / 'merged_modules.json',\n", + " SOURCE_DIR / 'models_biomod.py',\n", + " MODULE_DATA_DIR / 'merged_biomods.json',\n", "]\n", "required_dirs = [SOURCE_DIR, ASSET_ROOT]\n", "missing = [str(p) for p in required_files if not p.is_file()]\n", @@ -184,8 +184,8 @@ " '--min_genes', '10',\n", " '--min_cells', '10',\n", " '--config_pkl', str(CONFIG_PKL),\n", - " '--performer_checkpoint', str(CKPT_PATH),\n", - " '--module_data_dir', str(MODULE_DATA_DIR),\n", + " '--biomod_checkpoint', str(CKPT_PATH),\n", + " '--biomod_data_dir', str(MODULE_DATA_DIR),\n", " '--gene_list_path', str(GENE_LIST),\n", " '--gene_mapping_path', str(GENE_MAP),\n", " '--gene2vec_data_path', str(GENE2VEC),\n", diff --git a/BioMod/toturials/drug_combination.ipynb b/BioMod/toturials/drug_combination.ipynb index 1923b901353dfc8d3ddd911da9f70e35ff0aae35..f9d4fa5f5c027d3ce60df6270592a75853778710 100644 --- a/BioMod/toturials/drug_combination.ipynb +++ b/BioMod/toturials/drug_combination.ipynb @@ -7,7 +7,7 @@ "# Drug combination synergy prediction with BioMod\n", "\n", "This notebook checks the bundled drug-combination inputs, prepares a command for\n", - "`BioMod/drug_combination/train_drug_combination_performer_module.py`, and plots\n", + "`BioMod/drug_combination/train_drug_combination_biomod.py`, and plots\n", "available training outputs. It uses the BioMod model weights and configuration.\n", "\n", "`RUN_TRAINING = False` by default. Set it to `True` only when you are ready to train\n", @@ -69,7 +69,7 @@ "# If Jupyter starts at the repository root, change this line to Path('.').resolve().\n", "PROJECT_ROOT = Path('../..').resolve()\n", "SOURCE_DIR = PROJECT_ROOT / 'BioMod'\n", - "if not (SOURCE_DIR / 'models_performer_module.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", + "if not (SOURCE_DIR / 'models_biomod.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", " raise FileNotFoundError(\n", " 'Start Jupyter from BioMod/toturials, or set PROJECT_ROOT to the repository root '\n", " \"(use Path('.').resolve() if Jupyter was launched at that root).\"\n", @@ -77,8 +77,8 @@ "REPO_ROOT = PROJECT_ROOT\n", "\n", "DEPENDENCY_ROOT = REPO_ROOT / 'model_dependencies'\n", - "WEIGHT_DIR = REPO_ROOT / 'weight' / '0810model'\n", - "MODULE_DATA_DIR = DEPENDENCY_ROOT / 'module_data_all'\n", + "WEIGHT_DIR = REPO_ROOT / 'weight'\n", + "MODULE_DATA_DIR = DEPENDENCY_ROOT / 'biomod_data_all'\n", "TRAIN_PYTHON = Path(sys.executable).resolve()\n", "PYTHONPATH = os.pathsep.join(\n", " [str(SOURCE_DIR), str(DEPENDENCY_ROOT)]\n", @@ -86,14 +86,14 @@ ")\n", "\n", "TASK_ROOT = SOURCE_DIR / 'drug_combination'\n", - "TRAIN_SCRIPT = TASK_ROOT / 'train_drug_combination_performer_module.py'\n", - "CHECKPOINT_PATH = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2_epoch0.pth'\n", - "HYPER_PARAMS_PATH = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2.pkl'\n", + "TRAIN_SCRIPT = TASK_ROOT / 'train_drug_combination_biomod.py'\n", + "CHECKPOINT_PATH = WEIGHT_DIR / 'BioMod.pth'\n", + "HYPER_PARAMS_PATH = WEIGHT_DIR / 'BioMod.pkl'\n", "GENE_LIST_PATH = DEPENDENCY_ROOT / 'selected_genes_27k.txt'\n", "GENE_MAPPING_PATH = DEPENDENCY_ROOT / 'gene2ens.csv'\n", "GENE2VEC_PATH = DEPENDENCY_ROOT / 'selected_gene2vec_27k.npy'\n", "SYMBOL2ENS_PATH = GENE_MAPPING_PATH\n", - "PERFORMER_CONT_ROOT = DEPENDENCY_ROOT / 'performer_pytorch_cont'\n", + "ATTENTION_ROOT = DEPENDENCY_ROOT / 'pytorch_cont'\n", "DATA_DIR = REPO_ROOT / 'data/downstream/final_data/drug_combination_synergy/data_summary'\n", "RUN_ROOT = REPO_ROOT / 'tutorial_runs/drug_combination'\n", "OUTPUT_DIR = RUN_ROOT / 'results'\n", @@ -120,7 +120,7 @@ " 'gene_mapping_path': GENE_MAPPING_PATH,\n", " 'gene2vec_path': GENE2VEC_PATH,\n", " 'symbol2ens_path': SYMBOL2ENS_PATH,\n", - " 'performer_pytorch_cont': PERFORMER_CONT_ROOT,\n", + " 'pytorch_cont': ATTENTION_ROOT,\n", "}\n", "\n", "expected_data_files = {\n", @@ -221,7 +221,7 @@ " str(TRAIN_PYTHON), str(TRAIN_SCRIPT),\n", " '--checkpoint_path', str(CHECKPOINT_PATH),\n", " '--hyper_params_path', str(HYPER_PARAMS_PATH),\n", - " '--module_data_dir', str(MODULE_DATA_DIR),\n", + " '--biomod_data_dir', str(MODULE_DATA_DIR),\n", " '--gene_list_path', str(GENE_LIST_PATH),\n", " '--gene_mapping_path', str(GENE_MAPPING_PATH),\n", " '--gene2vec_path', str(GENE2VEC_PATH),\n", diff --git a/BioMod/toturials/drug_response_prediction.ipynb b/BioMod/toturials/drug_response_prediction.ipynb index 86a2c62eec111a113f53d3e41deba8edf11935ef..f054e85c28523e44a669b0fbf1012edea8a42f5e 100644 --- a/BioMod/toturials/drug_response_prediction.ipynb +++ b/BioMod/toturials/drug_response_prediction.ipynb @@ -7,7 +7,7 @@ "# Drug response prediction with BioMod\n", "\n", "This notebook uses the BioMod model weights, drug graph features, and the bundled\n", - "GDSC/CCLE response data with `train_drug_performer_module.py`.\n", + "GDSC/CCLE response data with `train_drug_biomod.py`.\n", "\n", "By default, `RUN_TRAINING = False`: the notebook checks inputs and prepares the\n", "training command. Result and plotting cells run only when `predictions.npz`\n", @@ -35,9 +35,9 @@ "source": [ "## Inputs and outputs\n", "\n", - "- Training script: `BioMod/drug_response/train_drug_performer_module.py`\n", + "- Training script: `BioMod/drug_response/train_drug_biomod.py`\n", "- Dataset: `data/downstream/final_data/single_drug_response/data/`\n", - "- BioMod model weights and configuration: `weight/0810model/`\n", + "- BioMod model weights and configuration: `weight/`\n", "- Shared model assets: `model_dependencies/`\n", "- Tutorial output: `tutorial_runs/drug_response_prediction/`\n", "\n", @@ -76,7 +76,7 @@ "# If Jupyter starts at the repository root, change this line to Path('.').resolve().\n", "PROJECT_ROOT = Path('../..').resolve()\n", "SOURCE_DIR = PROJECT_ROOT / 'BioMod'\n", - "if not (SOURCE_DIR / 'models_performer_module.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", + "if not (SOURCE_DIR / 'models_biomod.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", " raise FileNotFoundError(\n", " 'Start Jupyter from BioMod/toturials, or set PROJECT_ROOT to the repository root '\n", " \"(use Path('.').resolve() if Jupyter was launched at that root).\"\n", @@ -84,8 +84,8 @@ "REPO_ROOT = PROJECT_ROOT\n", "\n", "DEPENDENCY_ROOT = REPO_ROOT / 'model_dependencies'\n", - "WEIGHT_DIR = REPO_ROOT / 'weight' / '0810model'\n", - "MODULE_DATA_DIR = DEPENDENCY_ROOT / 'module_data_all'\n", + "WEIGHT_DIR = REPO_ROOT / 'weight'\n", + "MODULE_DATA_DIR = DEPENDENCY_ROOT / 'biomod_data_all'\n", "TRAIN_PYTHON = Path(sys.executable).resolve()\n", "PYTHONPATH = os.pathsep.join(\n", " [str(SOURCE_DIR), str(DEPENDENCY_ROOT)]\n", @@ -93,10 +93,10 @@ ")\n", "\n", "TASK_ROOT = SOURCE_DIR / 'drug_response'\n", - "SCRIPT = TASK_ROOT / 'train_drug_performer_module.py'\n", + "SCRIPT = TASK_ROOT / 'train_drug_biomod.py'\n", "DATA_ROOT = REPO_ROOT / 'data/downstream/final_data/single_drug_response/data'\n", - "CKPT_PATH = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2_epoch0.pth'\n", - "CONFIG_PKL = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2.pkl'\n", + "CKPT_PATH = WEIGHT_DIR / 'BioMod.pth'\n", + "CONFIG_PKL = WEIGHT_DIR / 'BioMod.pkl'\n", "GENE2VEC = DEPENDENCY_ROOT / 'selected_gene2vec_27k.npy'\n", "GENE_MAP = DEPENDENCY_ROOT / 'gene2ens.csv'\n", "GENE_LIST = DEPENDENCY_ROOT / 'selected_genes_27k.txt'\n", @@ -129,7 +129,7 @@ " DATA_ROOT / 'GDSC/1.Drug_listMon Jun 24 09_00_55 2019.csv',\n", "]\n", "required_dirs = [PROJECT_ROOT, TASK_ROOT, MODULE_DATA_DIR,\n", - " DEPENDENCY_ROOT / 'performer_pytorch_cont', DATA_ROOT / 'GDSC/drug_graph_feat']\n", + " DEPENDENCY_ROOT / 'pytorch_cont', DATA_ROOT / 'GDSC/drug_graph_feat']\n", "missing = [str(path) for path in required_files if not path.is_file()]\n", "missing += [str(path) for path in required_dirs if not path.is_dir()]\n", "if missing:\n", @@ -236,7 +236,7 @@ " '--hyper_params_path', str(CONFIG_PKL),\n", " '--epochs', str(EPOCHS),\n", " '--batch_size', str(BATCH_SIZE),\n", - " '--module_data_dir', str(MODULE_DATA_DIR),\n", + " '--biomod_data_dir', str(MODULE_DATA_DIR),\n", " '--gene_list_path', str(GENE_LIST),\n", " '--gene_mapping_path', str(GENE_MAP),\n", " '--symbol2ens_path', str(GENE_MAP),\n", diff --git a/BioMod/toturials/gene_perturbation.ipynb b/BioMod/toturials/gene_perturbation.ipynb index b2c3bfaf3b5d59299be5027f5a7d854096148c23..01db049c969342ff716a9d871c25408c46b08f11 100644 --- a/BioMod/toturials/gene_perturbation.ipynb +++ b/BioMod/toturials/gene_perturbation.ipynb @@ -24,11 +24,11 @@ "source": [ "## Inputs and workflow\n", "\n", - "- Entry point: `BioMod/gene_perturbation/train_module.py`.\n", + "- Entry point: `BioMod/gene_perturbation/train_biomod.py`.\n", "- Dataset parent: `data/downstream/final_data/`; the loader appends `norman/`.\n", "- Processed expression: `norman/perturb_processed.h5ad` under that parent.\n", "- Split: `simulation`, seed `1`, training-gene fraction `0.75`.\n", - "- Model weights and matching configuration: the `.pth` / `.pkl` pair under `weight/0810model/`.\n", + "- Model weights and matching configuration: the `.pth` / `.pkl` pair under `weight/`.\n", "- Gene vocabulary, mapping, embeddings, and module definitions: `model_dependencies/`.\n", "- Results: `tutorial_runs/gene_perturbation_norman_biomod/`.\n", "\n", @@ -56,7 +56,7 @@ " print(obj)\n", "\n", "PROJECT_ROOT = Path('../..').resolve()\n", - "if not (PROJECT_ROOT / 'BioMod' / 'models_performer_module.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", + "if not (PROJECT_ROOT / 'BioMod' / 'models_biomod.py').is_file() or not (PROJECT_ROOT / 'model_dependencies').is_dir():\n", " raise FileNotFoundError(\n", " 'Project root not found. Start Jupyter from BioMod/toturials, or set '\n", " 'PROJECT_ROOT to your repository root (use Path(\".\").resolve() if '\n", @@ -67,7 +67,7 @@ "SOURCE_DIR = PROJECT_ROOT / 'BioMod'\n", "ASSET_ROOT = PROJECT_ROOT / 'model_dependencies'\n", "TASK_ROOT = SOURCE_DIR / 'gene_perturbation'\n", - "TRAIN_SCRIPT = TASK_ROOT / 'train_module.py'\n", + "TRAIN_SCRIPT = TASK_ROOT / 'train_biomod.py'\n", "DATA_DIR = PROJECT_ROOT / 'data' / 'downstream' / 'final_data'\n", "DATA_NAME = 'norman'\n", "SPLIT = 'simulation'\n", @@ -75,13 +75,13 @@ "TRAIN_GENE_SET_SIZE = 0.75\n", "\n", "# Keep the BioMod model weights and their matching configuration together.\n", - "WEIGHT_DIR = PROJECT_ROOT / 'weight' / '0810model'\n", - "SCFM_HYPER = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2.pkl'\n", - "SCFM_CKPT = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2_epoch0.pth'\n", + "WEIGHT_DIR = PROJECT_ROOT / 'weight'\n", + "SCFM_HYPER = WEIGHT_DIR / 'BioMod.pkl'\n", + "SCFM_CKPT = WEIGHT_DIR / 'BioMod.pth'\n", "SCFM_GENES = ASSET_ROOT / 'selected_genes_27k.txt'\n", "SCFM_GENE2VEC = ASSET_ROOT / 'selected_gene2vec_27k.npy'\n", "GENE_MAP = ASSET_ROOT / 'gene2ens.csv'\n", - "MODULE_DATA_DIR = ASSET_ROOT / 'module_data_all'\n", + "MODULE_DATA_DIR = ASSET_ROOT / 'biomod_data_all'\n", "\n", "OUTPUT_PARENT = PROJECT_ROOT / 'tutorial_runs'\n", "RUN_NAME = 'gene_perturbation_norman_biomod'\n", @@ -105,8 +105,8 @@ "required_files = [\n", " TRAIN_SCRIPT, TARGET_H5AD, SPLIT_FILE, SCFM_HYPER, SCFM_CKPT,\n", " SCFM_GENES, SCFM_GENE2VEC, GENE_MAP,\n", - " SOURCE_DIR / 'models_performer_module.py',\n", - " MODULE_DATA_DIR / 'merged_modules.json',\n", + " SOURCE_DIR / 'models_biomod.py',\n", + " MODULE_DATA_DIR / 'merged_biomods.json',\n", "]\n", "required_dirs = [TASK_ROOT, ASSET_ROOT, DATA_DIR / DATA_NAME / 'data_pyg']\n", "missing = [str(p) for p in required_files if not p.is_file()]\n", @@ -123,7 +123,7 @@ "source": [ "## Runtime setup\n", "\n", - "The current source imports `BioMod.models_performer_module` directly. The child process uses the repository and bundled dependency directories on `PYTHONPATH`; no temporary package or symlink is needed. The notebook leaves the kernel environment unchanged." + "The current source imports `BioMod.models_biomod` directly. The child process uses the repository and bundled dependency directories on `PYTHONPATH`; no temporary package or symlink is needed. The notebook leaves the kernel environment unchanged." ] }, { @@ -158,8 +158,8 @@ "outputs": [], "source": [ "# File checks do not load the model weights or deserialize the configuration.\n", - "print('Backbone source:', SOURCE_DIR / 'models_performer_module.py')\n", - "print('Bundled Performer:', ASSET_ROOT / 'performer_pytorch_cont')\n", + "print('Backbone source:', SOURCE_DIR / 'models_biomod.py')\n", + "print('Bundled attention components:', ASSET_ROOT / 'pytorch_cont')\n", "print('Training enabled:', RUN_TRAINING)" ] }, @@ -169,7 +169,7 @@ "source": [ "## Training command\n", "\n", - "`train_module.py` initializes distributed training even for one GPU. The command therefore uses `python -m torch.distributed.run --standalone --nproc_per_node=1`. Review the settings below, then set `RUN_TRAINING = True` in the setup cell to launch. The training script selects a checkpoint using validation MSE on the top 20 differentially expressed genes, then evaluates the selected checkpoint on the test split. Batch sizes, epoch count, and learning rate are configurable examples, not a verified benchmark recipe." + "`train_biomod.py` initializes distributed training even for one GPU. The command therefore uses `python -m torch.distributed.run --standalone --nproc_per_node=1`. Review the settings below, then set `RUN_TRAINING = True` in the setup cell to launch. The training script selects a checkpoint using validation MSE on the top 20 differentially expressed genes, then evaluates the selected checkpoint on the test split. Batch sizes, epoch count, and learning rate are configurable examples, not a verified benchmark recipe." ] }, { @@ -206,7 +206,7 @@ " '--gene_mapping_path', str(GENE_MAP),\n", " '--scfm_gene2vec_file', str(SCFM_GENE2VEC),\n", " '--accumulation_steps', str(ACCUMULATION_STEPS),\n", - " '--module_data_dir', str(MODULE_DATA_DIR),\n", + " '--biomod_data_dir', str(MODULE_DATA_DIR),\n", " '--lr', LR,\n", "]\n", "print('Command:')\n", diff --git a/BioMod/toturials/zero_shot_batch_integration.ipynb b/BioMod/toturials/zero_shot_batch_integration.ipynb index 48f83d1ac808669ae7a2b15acc862223b63311bf..d09c8bb2c4455db3fbaac029a75d3a3c80f37c61 100644 --- a/BioMod/toturials/zero_shot_batch_integration.ipynb +++ b/BioMod/toturials/zero_shot_batch_integration.ipynb @@ -18,9 +18,9 @@ "Start Jupyter with `BioMod/toturials` as its working directory and select the environment described in [the project README](../../README.md). Paths below are relative to this notebook directory, following the project-relative layout used in scGPT tutorials. `Path.resolve()` turns them into absolute paths for subprocesses. If Jupyter was started at the repository root, set `PROJECT_ROOT = Path('.').resolve()` instead.\n", "\n", "- Data: `../../data/downstream/final_data/Kim2020_Lung_noMalignant.h5ad`\n", - "- BioMod model weights and configuration: `../../weight/0810model/`\n", + "- BioMod model weights and configuration: `../../weight/`\n", "- Shared assets: `../../model_dependencies/`\n", - "- Runner: `../zero_shot_batch/performer_module_zero_shot.py`\n", + "- Runner: `../zero_shot_batch/biomod_zero_shot.py`\n", "- Results: `../../tutorial_runs/zero_shot_kim_lung_no_malignant/`\n", "\n", "Review the configuration, then set `RUN_EVALUATION = True` to run inference. The runner consumes `adata.X` without normalization or log transformation; check that its expression preprocessing is appropriate for the checkpoint and your experiment. It loads the input dataset into memory before batching inference.\n" @@ -46,15 +46,15 @@ " )\n", "SOURCE_DIR = PROJECT_ROOT / 'BioMod'\n", "DEPENDENCIES_DIR = PROJECT_ROOT / 'model_dependencies'\n", - "SCRIPT = SOURCE_DIR / 'zero_shot_batch' / 'performer_module_zero_shot.py'\n", + "SCRIPT = SOURCE_DIR / 'zero_shot_batch' / 'biomod_zero_shot.py'\n", "DATA_PATH = PROJECT_ROOT / 'data' / 'downstream' / 'final_data' / 'Kim2020_Lung_noMalignant.h5ad'\n", - "WEIGHT_DIR = PROJECT_ROOT / 'weight' / '0810model'\n", - "CONFIG_PKL = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2.pkl'\n", - "CKPT_PATH = WEIGHT_DIR / 'mae_tost_70M_75mask_ec2_epoch0.pth'\n", + "WEIGHT_DIR = PROJECT_ROOT / 'weight'\n", + "CONFIG_PKL = WEIGHT_DIR / 'BioMod.pkl'\n", + "CKPT_PATH = WEIGHT_DIR / 'BioMod.pth'\n", "GENE2VEC = DEPENDENCIES_DIR / 'selected_gene2vec_27k.npy'\n", "GENE_MAP = DEPENDENCIES_DIR / 'gene2ens.csv'\n", "GENE_LIST = DEPENDENCIES_DIR / 'selected_genes_27k.txt'\n", - "MODULE_DATA_DIR = DEPENDENCIES_DIR / 'module_data_all'\n", + "MODULE_DATA_DIR = DEPENDENCIES_DIR / 'biomod_data_all'\n", "OUT_DIR = PROJECT_ROOT / 'tutorial_runs' / 'zero_shot_kim_lung_no_malignant'\n", "\n", "RUN_EVALUATION = False\n", @@ -77,8 +77,8 @@ "outputs": [], "source": [ "required_files = [SCRIPT, DATA_PATH, CONFIG_PKL, CKPT_PATH, GENE2VEC, GENE_MAP, GENE_LIST,\n", - " MODULE_DATA_DIR / 'merged_modules.json']\n", - "required_dirs = [DEPENDENCIES_DIR / 'performer_pytorch_cont']\n", + " MODULE_DATA_DIR / 'merged_biomods.json']\n", + "required_dirs = [DEPENDENCIES_DIR / 'pytorch_cont']\n", "missing = [str(p) for p in required_files if not p.is_file()]\n", "missing += [str(p) for p in required_dirs if not p.is_dir()]\n", "if missing:\n", @@ -135,7 +135,7 @@ " '--gene2vec_path', str(GENE2VEC),\n", " '--scfm_hyper_params_path', str(CONFIG_PKL),\n", " '--scfm_ckpt_path', str(CKPT_PATH),\n", - " '--module_data_dir', str(MODULE_DATA_DIR),\n", + " '--biomod_data_dir', str(MODULE_DATA_DIR),\n", " '--gene_mapping_path', str(GENE_MAP),\n", " '--scfm_genes_file', str(GENE_LIST),\n", " '--output_dir', str(OUT_DIR),\n", diff --git a/BioMod/zero_shot_batch/README.md b/BioMod/zero_shot_batch/README.md index 456c6719ae819ff7c7b2745780d795484c1ba62c..569ae25bcfbb0d80a52579ab71e1af42bfc8dcbe 100644 --- a/BioMod/zero_shot_batch/README.md +++ b/BioMod/zero_shot_batch/README.md @@ -17,10 +17,10 @@ Run these commands from the repository root. Built-in resource defaults resolve ### Basic Usage ```bash -python BioMod/zero_shot_batch/performer_module_zero_shot.py \ - --scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --module_data_path model_dependencies/module_data_all/merged_modules.json \ +python BioMod/zero_shot_batch/biomod_zero_shot.py \ + --scfm_hyper_params_path weight/BioMod.pkl \ + --scfm_ckpt_path weight/BioMod.pth \ + --biomod_data_path model_dependencies/biomod_data_all/merged_biomods.json \ --data_path data/downstream/final_data/Kim2020_Lung.h5ad \ --dataset_preset lung ``` @@ -28,11 +28,11 @@ python BioMod/zero_shot_batch/performer_module_zero_shot.py \ ### Full Argument Example ```bash -python BioMod/zero_shot_batch/performer_module_zero_shot.py \ +python BioMod/zero_shot_batch/biomod_zero_shot.py \ --gene2vec_path model_dependencies/selected_gene2vec_27k.npy \ - --scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --module_data_dir model_dependencies/module_data_all \ + --scfm_hyper_params_path weight/BioMod.pkl \ + --scfm_ckpt_path weight/BioMod.pth \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_mapping_path model_dependencies/gene2ens.csv \ --scfm_genes_file model_dependencies/selected_genes_27k.txt \ --data_path data/downstream/final_data/Kim2020_Lung.h5ad \ @@ -46,11 +46,11 @@ python BioMod/zero_shot_batch/performer_module_zero_shot.py \ ``` ```bash -python BioMod/zero_shot_batch/performer_module_zero_shot.py \ +python BioMod/zero_shot_batch/biomod_zero_shot.py \ --gene2vec_path model_dependencies/selected_gene2vec_27k.npy \ - --scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ - --scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ - --module_data_dir model_dependencies/module_data_all \ + --scfm_hyper_params_path weight/BioMod.pkl \ + --scfm_ckpt_path weight/BioMod.pth \ + --biomod_data_dir model_dependencies/biomod_data_all \ --gene_mapping_path model_dependencies/gene2ens.csv \ --scfm_genes_file model_dependencies/selected_genes_27k.txt \ --dataset_preset lung \ @@ -68,8 +68,8 @@ python BioMod/zero_shot_batch/performer_module_zero_shot.py \ | --- | --- | --- | | `--scfm_hyper_params_path` | Path to the matching BioMod model configuration; required | - | | `--scfm_ckpt_path` | Path to the BioMod model weights; required | - | -| `--module_data_path` | Path to one module JSON file | None | -| `--module_data_dir` | Directory containing multiple module JSON files | None | +| `--biomod_data_path` | Path to one module JSON file | None | +| `--biomod_data_dir` | Directory containing multiple module JSON files | None | | `--gene2vec_path` | Path to the Gene2Vec file | model_dependencies/selected_gene2vec_27k.npy | | `--gene_mapping_path` | CSV mapping gene symbols to Ensembl IDs | model_dependencies/gene2ens.csv | | `--scfm_genes_file` | BioMod gene list file | model_dependencies/selected_genes_27k.txt | @@ -123,7 +123,7 @@ The current script does not fall back to decoder outputs when module encodings a ## Notes -1. **Module data**: make sure the correct module data path is provided with `--module_data_path` or `--module_data_dir`. +1. **Module data**: make sure the correct module data path is provided with `--biomod_data_path` or `--biomod_data_dir`. 2. **Gene mapping**: make sure the mapping file (`gene2ens.csv`) contains the required gene-symbol to Ensembl-ID mappings. 3. **Memory usage**: for large datasets, set `--n_cells` to limit the number of processed cells. 4. **Batch size**: adjust `--batch_size` according to GPU memory. @@ -134,7 +134,7 @@ The current script does not fall back to decoder outputs when module encodings a **Error**: `KeyError: 'module_sets'` -**Fix**: make sure `--module_data_path` or `--module_data_dir` is provided. +**Fix**: make sure `--biomod_data_path` or `--biomod_data_dir` is provided. ### Issue 2: Gene Mapping Failed diff --git a/BioMod/zero_shot_batch/performer_module_zero_shot.py b/BioMod/zero_shot_batch/biomod_zero_shot.py similarity index 90% rename from BioMod/zero_shot_batch/performer_module_zero_shot.py rename to BioMod/zero_shot_batch/biomod_zero_shot.py index 5bdf7e33642a6d01c502ebf3b6e0a6b69799482b..a96ba9464e379955014d053f4757e052a05876c3 100644 --- a/BioMod/zero_shot_batch/performer_module_zero_shot.py +++ b/BioMod/zero_shot_batch/biomod_zero_shot.py @@ -1,8 +1,8 @@ """ -Adaptation code of the PerformerEncoderOnly_Module model on the zero-shot batch correction task. +Adaptation code of the BioMod model on the zero-shot batch correction task. The current script mainly supports: -1. Use the PerformerEncoderOnly_Module model (encoder-only) to extract cell embeddings. +1. Use the BioMod model (encoder-only) to extract cell embeddings. 2. Use mean pooling of module encodings as cell embedding. 3. Supports evaluation based on scIB indicators consistent with the scGPT tutorial. 4. Supports Lung data set preset and retains all genes by default without going through the HVG 3000 process. @@ -19,7 +19,7 @@ REPO_ROOT = Path(__file__).resolve().parents[2] ASSET_DIR = REPO_ROOT / "model_dependencies" DOWNSTREAM_DATA_DIR = REPO_ROOT / "data" / "downstream" / "final_data" -WEIGHTS_DIR = REPO_ROOT / "weight" / "0810model" +WEIGHTS_DIR = REPO_ROOT / "weight" from copy import deepcopy from typing import Dict, List, Optional, Sequence, Set, Union @@ -377,27 +377,27 @@ def reindex_tensor_universal( sys.path.insert(0, project_root) sys.path.insert(0, os.path.dirname(project_root)) -performer_module_dir = project_root +biomod_dir = project_root -def load_module_modules(): - models_path = os.path.join(performer_module_dir, "models_performer_module.py") +def load_biomod_components(): + models_path = os.path.join(biomod_dir, "models_biomod.py") if not os.path.exists(models_path): raise FileNotFoundError(f"Model file not found: {models_path}") - models_spec = importlib.util.spec_from_file_location("models_performer_module", models_path) - models_performer_module = importlib.util.module_from_spec(models_spec) - models_spec.loader.exec_module(models_performer_module) + models_spec = importlib.util.spec_from_file_location("models_biomod", models_path) + models_biomod = importlib.util.module_from_spec(models_spec) + models_spec.loader.exec_module(models_biomod) - dataset_path = os.path.join(performer_module_dir, "module_dataset.py") + dataset_path = os.path.join(biomod_dir, "biomod_dataset.py") if not os.path.exists(dataset_path): raise FileNotFoundError(f"Dataset file not found: {dataset_path}") - dataset_spec = importlib.util.spec_from_file_location("module_dataset", dataset_path) - module_dataset = importlib.util.module_from_spec(dataset_spec) - dataset_spec.loader.exec_module(module_dataset) + dataset_spec = importlib.util.spec_from_file_location("biomod_dataset", dataset_path) + biomod_dataset = importlib.util.module_from_spec(dataset_spec) + dataset_spec.loader.exec_module(biomod_dataset) return ( - models_performer_module.PerformerEncoderOnly_Module, - models_performer_module.sanitize_performer_module_config, - module_dataset.load_module_data, + models_biomod.BioMod, + models_biomod.sanitize_biomod_config, + biomod_dataset.load_biomod_data, ) OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "res") @@ -410,10 +410,10 @@ def load_module_modules(): help="Path to the matching BioMod model configuration pkl file") parser.add_argument("--scfm_ckpt_path", type=str, required=True, help="Path to the BioMod model weights") -parser.add_argument("--module_data_path", type=str, default=None, - help="Path to module JSON file (single file)") -parser.add_argument("--module_data_dir", type=str, default=None, - help="Path to directory containing module JSON files") +parser.add_argument("--biomod_data_path", "--module_data_path", type=str, default=None, + help="Path to module JSON file (single file)", dest='module_data_path') +parser.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=None, + help="Path to directory containing module JSON files", dest='module_data_dir') parser.add_argument("--gene_mapping_path", type=str, default=str(ASSET_DIR / "gene2ens.csv"), help="Path to gene symbol to Ensembl ID mapping CSV") parser.add_argument("--scfm_genes_file", type=str, default=str(ASSET_DIR / "selected_genes_27k.txt"), @@ -446,13 +446,13 @@ def load_module_modules(): help="Optional Scanpy cell filter. Default 0 keeps all cells.") parser.add_argument("--min_cells_per_gene", type=int, default=0, help="Optional Scanpy gene filter. Default 0 keeps all genes.") -parser.add_argument("--shuffle_module_genes", action="store_true", +parser.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", help="Optional downstream-only extra reshuffle on top of module_sets restored from PKL. " - "Do not pass this when reproducing the pretrained backbone as-is.") + "Do not pass this when reproducing the pretrained backbone as-is.", dest='shuffle_module_genes') parser.add_argument("--shuffle_seed", type=int, default=2021, - help="Random seed for the downstream extra reshuffle when --shuffle_module_genes is enabled.") -parser.add_argument("--save_raw_module_emb", action="store_true", - help="Save raw module_encodings with shape (num_cells, num_modules, embed_dim)") + help="Random seed for the downstream extra reshuffle when --shuffle_biomod_genes is enabled.") +parser.add_argument("--save_raw_biomod_emb", "--save_raw_module_emb", action="store_true", + help="Save raw module_encodings with shape (num_cells, num_modules, embed_dim)", dest='save_raw_module_emb') args = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) @@ -472,13 +472,13 @@ def load_module_modules(): f"pool_method={args.pool_method}" ) -PerformerEncoderOnly_Module, sanitize_performer_module_config, load_module_data = load_module_modules() +BioMod, sanitize_biomod_config, load_biomod_data = load_biomod_components() print_sys(f"Loading hyper parameters from {args.scfm_hyper_params_path}") with open(args.scfm_hyper_params_path, "rb") as handle: scfm_hyper_params = pickle.load(handle) scfm_hyper_params.pop("module_embed_chunk_padded_gene_budget", None) -scfm_hyper_params = sanitize_performer_module_config( +scfm_hyper_params = sanitize_biomod_config( scfm_hyper_params, source=args.scfm_hyper_params_path, log_fn=print_sys, @@ -492,7 +492,7 @@ def load_module_modules(): if "module_sets" not in scfm_hyper_params or scfm_hyper_params.get("module_sets") is None: if args.module_data_path or args.module_data_dir: print_sys("Loading module data...") - module_sets = load_module_data( + module_sets = load_biomod_data( module_data_path=args.module_data_path, module_data_dir=args.module_data_dir, gene_list_path=args.scfm_genes_file, @@ -501,7 +501,7 @@ def load_module_modules(): ) scfm_hyper_params["module_sets"] = module_sets else: - raise ValueError("Model requires module_sets, but neither --module_data_path nor --module_data_dir was provided") + raise ValueError("Model requires module_sets, but neither --biomod_data_path nor --biomod_data_dir was provided") else: module_sets = scfm_hyper_params["module_sets"] print_sys(f"Using module_sets from hyper parameters ({len(module_sets)} modules)") @@ -537,8 +537,8 @@ def load_module_modules(): scfm_hyper_params["use_gene2vec"] = pkl_use_gene2vec print_sys(f"use_gene2vec={pkl_use_gene2vec} (from PKL)") -print_sys("Creating PerformerEncoderOnly_Module model...") -model = PerformerEncoderOnly_Module(**scfm_hyper_params).to(device) +print_sys("Creating BioMod model...") +model = BioMod(**scfm_hyper_params).to(device) print_sys(f"Loading checkpoint from {args.scfm_ckpt_path}") ckpt = torch.load(args.scfm_ckpt_path, map_location=device) diff --git a/BioMod/zero_shot_batch/sequencing_depth/cmd.txt b/BioMod/zero_shot_batch/sequencing_depth/cmd.txt index 6ada3ddefa64d53fceafa27a068e55bd1fc11d4b..a52c8360a7db20992b29ee9c423eb68ef28912d3 100644 --- a/BioMod/zero_shot_batch/sequencing_depth/cmd.txt +++ b/BioMod/zero_shot_batch/sequencing_depth/cmd.txt @@ -9,12 +9,12 @@ python BioMod/zero_shot_batch/sequencing_depth/run_zero_shot_low_depth.py \ --data_paths \ data/downstream/final_data/Kim2020_Lung.h5ad \ tutorial_runs/sequencing_depth/inputs/*.h5ad \ ---scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ ---scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ +--scfm_ckpt_path weight/BioMod.pth \ +--scfm_hyper_params_path weight/BioMod.pkl \ --output_root tutorial_runs/sequencing_depth/results \ --gene_mapping_path model_dependencies/gene2ens.csv \ --scfm_genes_file model_dependencies/selected_genes_27k.txt \ ---module_data_dir model_dependencies/module_data_all \ +--biomod_data_dir model_dependencies/biomod_data_all \ --dataset_preset lung \ --output_key X_sclong_module \ --batch_size 256 \ @@ -29,12 +29,12 @@ tutorial_runs/sequencing_depth/inputs/Kim2020_Lung_ld0p5_seed0.h5ad \ tutorial_runs/sequencing_depth/inputs/Kim2020_Lung_ld0p25_seed0.h5ad \ tutorial_runs/sequencing_depth/inputs/Kim2020_Lung_ld0p1_seed0.h5ad \ tutorial_runs/sequencing_depth/inputs/Kim2020_Lung_ld0p05_seed0.h5ad \ ---scfm_ckpt_path weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth \ ---scfm_hyper_params_path weight/0810model/mae_tost_70M_75mask_ec2.pkl \ +--scfm_ckpt_path weight/BioMod.pth \ +--scfm_hyper_params_path weight/BioMod.pkl \ --output_root tutorial_runs/sequencing_depth/results \ --gene_mapping_path model_dependencies/gene2ens.csv \ --scfm_genes_file model_dependencies/selected_genes_27k.txt \ ---module_data_dir model_dependencies/module_data_all \ +--biomod_data_dir model_dependencies/biomod_data_all \ --dataset_preset lung \ --output_key X_sclong_module \ --batch_size 256 \ diff --git a/BioMod/zero_shot_batch/sequencing_depth/run_zero_shot_low_depth.py b/BioMod/zero_shot_batch/sequencing_depth/run_zero_shot_low_depth.py index 0ce82f0343fb30030bf11bcc1c284ab0b5488bfd..d5e3e37c432fad75c204b23c342ef73216159fe8 100644 --- a/BioMod/zero_shot_batch/sequencing_depth/run_zero_shot_low_depth.py +++ b/BioMod/zero_shot_batch/sequencing_depth/run_zero_shot_low_depth.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -"""Batch-run performer_module zero-shot on full/low-depth h5ad files and aggregate metrics.""" +"""Batch-run biomod zero-shot on full/low-depth h5ad files and aggregate metrics.""" import argparse import re @@ -15,15 +15,15 @@ def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( - description="Run performer_module zero-shot on multiple full/low-depth h5ad files and aggregate metrics." + description="Run biomod zero-shot on multiple full/low-depth h5ad files and aggregate metrics." ) - parser.add_argument("--script_path", type=str, default=None, help="Path to performer_module_zero_shot.py") + parser.add_argument("--script_path", type=str, default=None, help="Path to biomod_zero_shot.py") parser.add_argument("--data_paths", nargs="+", required=True, help="One or more full/low-depth h5ad files") parser.add_argument("--output_root", type=str, required=True, help="Root directory for per-run outputs") parser.add_argument("--scfm_hyper_params_path", type=str, required=True) parser.add_argument("--scfm_ckpt_path", type=str, required=True) - parser.add_argument("--module_data_path", type=str, default=None) - parser.add_argument("--module_data_dir", type=str, default=None) + parser.add_argument("--biomod_data_path", "--module_data_path", type=str, default=None, dest='module_data_path') + parser.add_argument("--biomod_data_dir", "--module_data_dir", type=str, default=None, dest='module_data_dir') parser.add_argument("--gene_mapping_path", type=str, required=True) parser.add_argument("--scfm_genes_file", type=str, required=True) parser.add_argument("--gene2vec_path", type=str, default=str(Path(__file__).resolve().parents[3] / "model_dependencies" / "selected_gene2vec_27k.npy")) @@ -38,9 +38,9 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--metrics_mode", type=str, default="scgpt") parser.add_argument("--min_genes_per_cell", type=int, default=0) parser.add_argument("--min_cells_per_gene", type=int, default=0) - parser.add_argument("--shuffle_module_genes", action="store_true") + parser.add_argument("--shuffle_biomod_genes", "--shuffle_module_genes", action="store_true", dest='shuffle_module_genes') parser.add_argument("--shuffle_seed", type=int, default=2021) - parser.add_argument("--save_raw_module_emb", action="store_true") + parser.add_argument("--save_raw_biomod_emb", "--save_raw_module_emb", action="store_true", dest='save_raw_module_emb') parser.add_argument("--python_bin", type=str, default=sys.executable) parser.add_argument("--overwrite", action="store_true", help="Re-run even if result CSV already exists") return parser.parse_args() @@ -49,7 +49,7 @@ def parse_args() -> argparse.Namespace: def resolve_script_path(script_path: Optional[str]) -> Path: if script_path is not None: return Path(script_path).resolve() - return (Path(__file__).resolve().parent.parent / "performer_module_zero_shot.py").resolve() + return (Path(__file__).resolve().parent.parent / "biomod_zero_shot.py").resolve() def slugify_path(path: Path) -> str: @@ -93,9 +93,9 @@ def build_command(args: argparse.Namespace, script_path: Path, data_path: Path, ] if args.module_data_path: - cmd.extend(["--module_data_path", args.module_data_path]) + cmd.extend(["--biomod_data_path", args.module_data_path]) if args.module_data_dir: - cmd.extend(["--module_data_dir", args.module_data_dir]) + cmd.extend(["--biomod_data_dir", args.module_data_dir]) if args.gene_col: cmd.extend(["--gene_col", args.gene_col]) if args.batch_key: @@ -103,10 +103,10 @@ def build_command(args: argparse.Namespace, script_path: Path, data_path: Path, if args.label_key: cmd.extend(["--label_key", *args.label_key]) if args.shuffle_module_genes: - cmd.append("--shuffle_module_genes") + cmd.append("--shuffle_biomod_genes") cmd.extend(["--shuffle_seed", str(args.shuffle_seed)]) if args.save_raw_module_emb: - cmd.append("--save_raw_module_emb") + cmd.append("--save_raw_biomod_emb") return cmd diff --git a/README.md b/README.md index d2f9b721711f662f32c67a1c7e2c39ea68229993..44a5b83f01f0c2eec0bf8d223c80c505b6f8083e 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ library_name: pytorch # BioMod BioMod is a single-cell transcriptomics foundation model built around gene modules -and a Performer-based encoder. This repository provides pretrained model assets, +and an attention-based encoder. This repository provides pretrained model assets, pretraining and fine-tuning code, downstream task tutorials, and interpretability tools. @@ -42,7 +42,7 @@ Create and activate the supplied Conda environment: ```bash conda env create -f BioMod/environment.yml -conda activate performer_module +conda activate biomod ``` The environment specification uses Python 3.9, PyTorch 2.6, CUDA 12.4, and PyTorch @@ -62,15 +62,15 @@ architecture and gene-module definitions. | Asset | File | | --- | --- | -| BioMod model weights | [mae_tost_70M_75mask_ec2_epoch0.pth](weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth) | -| Matching model configuration | [mae_tost_70M_75mask_ec2.pkl](weight/0810model/mae_tost_70M_75mask_ec2.pkl) | +| BioMod model weights | [BioMod.pth](weight/BioMod.pth) | +| Matching model configuration | [BioMod.pkl](weight/BioMod.pkl) | | Model gene order | [selected_genes_27k.txt](model_dependencies/selected_genes_27k.txt) | | Gene identifier mapping | [gene2ens.csv](model_dependencies/gene2ens.csv) | | Gene2Vec embeddings | [selected_gene2vec_27k.npy](model_dependencies/selected_gene2vec_27k.npy) | -| Shared module definitions | [merged_modules.json](model_dependencies/module_data_all/merged_modules.json) | +| Shared module definitions | [merged_biomods.json](model_dependencies/biomod_data_all/merged_biomods.json) | The model components are bundled under -[`model_dependencies/performer_pytorch_cont/`](model_dependencies/performer_pytorch_cont/). +[`model_dependencies/pytorch_cont/`](model_dependencies/pytorch_cont/). Task-specific checkpoint arguments are listed under [Usage notes](#checkpoint-arguments). @@ -82,11 +82,11 @@ to end with the bundled checkpoint. | Task | Implementation | Notebook | | --- | --- | --- | -| Cell-type annotation | [Fine-tuning script](BioMod/cell_annotation/performer_module_finetune.py) | [Notebook](BioMod/toturials/cell_annotation.ipynb) | -| Zero-shot cell embeddings and batch-effect evaluation | [Embedding and evaluation script](BioMod/zero_shot_batch/performer_module_zero_shot.py) | [Notebook](BioMod/toturials/zero_shot_batch_integration.ipynb) | -| Single-drug response regression (IC50) | [Training script](BioMod/drug_response/train_drug_performer_module.py) | [Notebook](BioMod/toturials/drug_response_prediction.ipynb) | -| Drug-pair synergy classification | [Training script](BioMod/drug_combination/train_drug_combination_performer_module.py) | [Notebook](BioMod/toturials/drug_combination.ipynb) | -| Gene perturbation response prediction | [Training script](BioMod/gene_perturbation/train_module.py) | [Notebook](BioMod/toturials/gene_perturbation.ipynb) | +| Cell-type annotation | [Fine-tuning script](BioMod/cell_annotation/biomod_finetune.py) | [Notebook](BioMod/toturials/cell_annotation.ipynb) | +| Zero-shot cell embeddings and batch-effect evaluation | [Embedding and evaluation script](BioMod/zero_shot_batch/biomod_zero_shot.py) | [Notebook](BioMod/toturials/zero_shot_batch_integration.ipynb) | +| Single-drug response regression (IC50) | [Training script](BioMod/drug_response/train_drug_biomod.py) | [Notebook](BioMod/toturials/drug_response_prediction.ipynb) | +| Drug-pair synergy classification | [Training script](BioMod/drug_combination/train_drug_combination_biomod.py) | [Notebook](BioMod/toturials/drug_combination.ipynb) | +| Gene perturbation response prediction | [Training script](BioMod/gene_perturbation/train_biomod.py) | [Notebook](BioMod/toturials/gene_perturbation.ipynb) | ### Pretraining and analysis scripts @@ -94,8 +94,8 @@ The repository also includes the following supporting workflows. | Workflow | Implementation | Documentation | | --- | --- | --- | -| Pretraining and continued pretraining | [Pretraining script](BioMod/performer_module.py) | [Source guide](BioMod/README.md), [launcher reference](BioMod/command.txt) | -| Module interpretability analysis | [Module geometry](BioMod/interpretability/module_geometry.py), [module importance](BioMod/interpretability/module_importance/module_importance.py), [text alignment](BioMod/interpretability/module_text_alignment/module_text_alignment.py) | [Overview](BioMod/interpretability/README.md), [attention](BioMod/interpretability/attention/README.md), [module importance guide](BioMod/interpretability/module_importance/README.md) | +| Pretraining and continued pretraining | [Pretraining script](BioMod/biomod.py) | [Source guide](BioMod/README.md), [launcher reference](BioMod/command.txt) | +| Module interpretability analysis | [Module geometry](BioMod/interpretability/biomod_geometry.py), [module importance](BioMod/interpretability/biomod_importance/biomod_importance.py), [text alignment](BioMod/interpretability/biomod_text_alignment/biomod_text_alignment.py) | [Overview](BioMod/interpretability/README.md), [attention](BioMod/interpretability/attention/README.md), [module importance guide](BioMod/interpretability/biomod_importance/README.md) | Bundled input defaults resolve from each script's location. Command examples state their working directory; explicit relative command-line paths are resolved @@ -122,7 +122,7 @@ the outer repository root. If Jupyter starts at the repository root, use | `SOURCE_DIR` | `PROJECT_ROOT / 'BioMod'` | | Asset or dependency directory | `PROJECT_ROOT / 'model_dependencies'` | | Weight and configuration paths | The BioMod model weights and matching configuration listed above | -| `MODULE_DATA_DIR` | `model_dependencies/module_data_all/` under the project root | +| `MODULE_DATA_DIR` | `model_dependencies/biomod_data_all/` under the project root | | Data and output paths | Your task inputs and a writable output directory | Checkpoint variable names differ by notebook: `CKPT_PATH` / `CONFIG_PKL` @@ -139,7 +139,7 @@ Output directories are created when the corresponding operation is enabled. | --- | --- | --- | | Drug response / drug combination | `--checkpoint_path` | `--hyper_params_path` | | Zero-shot / gene perturbation | `--scfm_ckpt_path` | `--scfm_hyper_params_path` | -| Cell annotation / backbone interpretation | `--performer_checkpoint` | `--config_pkl` | +| Cell annotation / backbone interpretation | `--biomod_checkpoint` | `--config_pkl` | | Continued pretraining | `--model_path` | `--model_config_path` or paired-file discovery | ### Data and evaluation @@ -175,67 +175,63 @@ readability. Folder descriptions explain where to find each component. BioMod/ # Project root: source code, model dependencies and weights ├── BioMod/ # Task code: training, evaluation, interpretability and tutorials │ ├── cell_annotation/ # Cell-type annotation and model fine-tuning -│ │ ├── module_data/ # Hallmark and Reactome modules with loading utilities -│ │ │ ├── load_module.py -│ │ │ ├── module1_hallmarks.json -│ │ │ └── module2_reactome_2022_filtered.json +│ │ ├── biomod_data/ # Hallmark and Reactome modules with loading utilities +│ │ │ ├── load_biomod.py +│ │ │ ├── biomod1_hallmarks.json +│ │ │ └── biomod2_reactome_2022_filtered.json │ │ ├── cmd_finetune.md -│ │ └── performer_module_finetune.py +│ │ └── biomod_finetune.py │ ├── drug_combination/ # Drug-combination synergy prediction │ │ ├── README.md -│ │ └── train_drug_combination_performer_module.py +│ │ └── train_drug_combination_biomod.py │ ├── drug_response/ # Single-drug response prediction and training │ │ ├── cmd.txt -│ │ └── train_drug_performer_module.py +│ │ └── train_drug_biomod.py │ ├── gene_perturbation/ # Perturbation response prediction: processing, training and inference │ │ ├── data_utils_local.py │ │ ├── gears_reduce_local.py │ │ ├── inference_local.py -│ │ ├── model_module.py +│ │ ├── model_biomod.py │ │ ├── pertdata_local.py │ │ ├── README.md │ │ ├── scfm_utils_local.py -│ │ ├── train_module.py +│ │ ├── train_biomod.py │ │ └── utils.py │ ├── interpretability/ # Module representations and biological interpretability │ │ ├── attention/ # Module crosstalk and within-module gene attention │ │ │ ├── __init__.py -│ │ │ ├── module_crosstalk.py -│ │ │ ├── module_gene_attention.py +│ │ │ ├── biomod_crosstalk.py +│ │ │ ├── biomod_gene_attention.py │ │ │ ├── README.md │ │ │ └── README_gene_attention.md -│ │ ├── module_importance/ # Module importance, ablation studies and marker-gene validation +│ │ ├── biomod_importance/ # Module importance, ablation studies and marker-gene validation │ │ │ ├── __init__.py │ │ │ ├── cross_class_similarity.py │ │ │ ├── family_ablation.py │ │ │ ├── family_importance_analysis.py │ │ │ ├── marker_gene_verification.py │ │ │ ├── masking_ablation.py -│ │ │ ├── module_importance.py -│ │ │ ├── module_importance_publication.py +│ │ │ ├── biomod_importance.py +│ │ │ ├── biomod_importance_publication.py │ │ │ ├── pub_utils.py │ │ │ ├── random_baseline.py │ │ │ ├── README.md │ │ │ ├── RESULTS.md │ │ │ ├── tcell_reactome_analysis.py │ │ │ └── TODO.md -│ │ ├── module_text_alignment/ # Semantic alignment of module representations and pathway text +│ │ ├── biomod_text_alignment/ # Semantic alignment of module representations and pathway text │ │ │ ├── __init__.py │ │ │ ├── DESIGN.md │ │ │ ├── fetch_reactome_descriptions.py │ │ │ ├── MANUSCRIPT_SECTION.md -│ │ │ ├── module_text_alignment.py -│ │ │ ├── module_text_alignment_extended.py -│ │ │ ├── module_text_alignment_publication.py +│ │ │ ├── biomod_text_alignment.py +│ │ │ ├── biomod_text_alignment_extended.py +│ │ │ ├── biomod_text_alignment_publication.py │ │ │ └── reactome_descriptions.json │ │ ├── __init__.py -│ │ ├── module_geometry.py +│ │ ├── biomod_geometry.py │ │ ├── README.md │ │ └── utils.py -│ ├── module_data_all/ # Module definitions, merging and uncovered-gene tools -│ │ ├── create_uncovered_modules.py -│ │ ├── merge_modules.py -│ │ └── merged_modules.json │ ├── tests/ # CPU tests for shared training utilities │ │ └── test_training_utils.py │ ├── toturials/ # Jupyter tutorials for five downstream tasks @@ -249,7 +245,7 @@ BioMod/ # Project root: source │ │ │ ├── cmd.txt │ │ │ ├── make_low_depth_h5ad.py │ │ │ └── run_zero_shot_low_depth.py -│ │ ├── performer_module_zero_shot.py +│ │ ├── biomod_zero_shot.py │ │ ├── README.md │ │ └── visualize_results.py │ ├── .env.example @@ -257,36 +253,29 @@ BioMod/ # Project root: source │ ├── .gitignore │ ├── command.txt │ ├── environment.yml -│ ├── models_performer_module.py -│ ├── module_dataset.py -│ ├── performer_module.py +│ ├── models_biomod.py +│ ├── biomod_dataset.py +│ ├── biomod.py │ ├── README.md │ ├── requirements.txt │ └── training_utils.py ├── model_dependencies/ # Shared gene mappings, gene lists, embeddings and model components -│ ├── module_data_all/ # Shared module definitions and construction tools -│ │ ├── create_uncovered_modules.py -│ │ ├── merge_modules.py -│ │ └── merged_modules.json -│ ├── performer_pytorch_cont/ # Performer attention and model components -│ │ ├── performer_pytorch_cont/ # Nested package containing copies of the model component source files -│ │ │ ├── __init__.py -│ │ │ ├── ding_models.py -│ │ │ ├── performer_pytorch.py -│ │ │ ├── performer_pytorch_old.py -│ │ │ └── reversible.py +│ ├── biomod_data_all/ # Shared module definitions and construction tools +│ │ ├── create_uncovered_biomods.py +│ │ ├── merge_biomods.py +│ │ └── merged_biomods.json +│ ├── pytorch_cont/ # Attention and model components │ │ ├── __init__.py │ │ ├── ding_models.py -│ │ ├── performer_pytorch.py -│ │ ├── performer_pytorch_old.py +│ │ ├── pytorch.py +│ │ ├── pytorch_old.py │ │ └── reversible.py │ ├── gene2ens.csv │ ├── selected_gene2vec_27k.npy │ └── selected_genes_27k.txt ├── weight/ # Pretrained weights and matching model configurations -│ └── 0810model/ # BioMod model weights and matching configuration -│ ├── mae_tost_70M_75mask_ec2.pkl -│ └── mae_tost_70M_75mask_ec2_epoch0.pth +│ ├── BioMod.pkl +│ └── BioMod.pth ├── .gitattributes ├── .gitignore └── README.md diff --git a/data/downstream/final_data/drug_combination_synergy/README.md b/data/downstream/final_data/drug_combination_synergy/README.md index f7ce3ed36240f72a9426ba352f573aad70b297bb..cfeb98163db6304ad1e8df10c799724cb7915bc1 100644 --- a/data/downstream/final_data/drug_combination_synergy/README.md +++ b/data/downstream/final_data/drug_combination_synergy/README.md @@ -9,7 +9,7 @@ data/downstream/final_data/drug_response_pytorch/drug_combination ## Runnable data path -From the repository root, use this directory as `--data_dir` for `BioMod/drug_combination/train_drug_combination_performer_module.py`: +From the repository root, use this directory as `--data_dir` for `BioMod/drug_combination/train_drug_combination_biomod.py`: ```text data/downstream/final_data/drug_combination_synergy/data_summary diff --git a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug.py b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug.py index 6701fe1f138ea7c68ab91d8cd13779a9c07ca8c4..20df48753bceddf5019a9ad020e35c2190541216 100644 --- a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug.py +++ b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug.py @@ -37,7 +37,7 @@ import pickle import sys sys.path.insert(0, str(ASSET_DIR)) -from performer_pytorch_cont.ding_models import DualEncoderSCFM +from pytorch_cont.ding_models import DualEncoderSCFM def print_sys(*args, **kwargs): diff --git a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug_GCN_linear.py b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug_GCN_linear.py index f27fa787b346874097fc8b8a110efa157105e8b2..4a241321a8bcd8d29d5243b99c83aaa1ca8f1938 100644 --- a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug_GCN_linear.py +++ b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_sclong_drug_GCN_linear.py @@ -37,7 +37,7 @@ import pickle import sys sys.path.insert(0, str(ASSET_DIR)) -from performer_pytorch_cont.ding_models import DualEncoderSCFM +from pytorch_cont.ding_models import DualEncoderSCFM def print_sys(*args, **kwargs): diff --git a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_tost_multisource.py b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_tost_multisource.py index 89cb6be183d8e0d0dee22a8b0cc2eee1bde6b8e8..a0cc241ad2c172a91e247413bae1857f5525b1c5 100644 --- a/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_tost_multisource.py +++ b/data/downstream/final_data/drug_response_pytorch/drug_response_pytorch/train_tost_multisource.py @@ -35,7 +35,7 @@ from torch.utils.data import DataLoader, Dataset # Make the bundled DualEncoderSCFM implementation importable. sys.path.insert(0, str(ASSET_DIR)) -from performer_pytorch_cont.ding_models import DualEncoderSCFM +from pytorch_cont.ding_models import DualEncoderSCFM # ---------------------- Utilities and logging ---------------------- diff --git a/data/downstream/final_data/single_drug_response/README.md b/data/downstream/final_data/single_drug_response/README.md index b7920f85a255b255724e7f4cf3279a4da2b55e63..9a5d59a2147c234c9b5b410952a3d54f2cabb727 100644 --- a/data/downstream/final_data/single_drug_response/README.md +++ b/data/downstream/final_data/single_drug_response/README.md @@ -9,7 +9,7 @@ data/downstream/final_data/drug_response_pytorch/drug_response_pytorch ## Runnable data path -From the repository root, use this directory as `--data_path` for `BioMod/drug_response/train_drug_performer_module.py`: +From the repository root, use this directory as `--data_path` for `BioMod/drug_response/train_drug_biomod.py`: ```text data/downstream/final_data/single_drug_response/data diff --git a/BioMod/module_data_all/create_uncovered_modules.py b/model_dependencies/biomod_data_all/create_uncovered_biomods.py similarity index 86% rename from BioMod/module_data_all/create_uncovered_modules.py rename to model_dependencies/biomod_data_all/create_uncovered_biomods.py index 06b3139c99d2ffbac95360c171cd880df9a45cb8..0ae3b5acc61fd8733e5ecae188e639539bfaf7b1 100644 --- a/BioMod/module_data_all/create_uncovered_modules.py +++ b/model_dependencies/biomod_data_all/create_uncovered_biomods.py @@ -5,10 +5,10 @@ Ensure that all model genes can participate in training Run from the repository root: - python BioMod/module_data_all/create_uncovered_modules.py \ - --merged_modules model_dependencies/module_data_all/merged_modules.json \ - --output tutorial_runs/module_data/merged_modules_full.json \ - --module_size 50 + python model_dependencies/biomod_data_all/create_uncovered_biomods.py \ + --merged_biomods model_dependencies/biomod_data_all/merged_biomods.json \ + --output tutorial_runs/biomod_data/merged_modules_full.json \ + --biomod_size 50 """ import json @@ -21,7 +21,7 @@ ASSET_ROOT = PROJECT_ROOT / 'model_dependencies' MODULE_DIR = Path(__file__).resolve().parent -def create_uncovered_modules( +def create_uncovered_biomods( merged_modules_path, gene_list_path, gene_mapping_path, @@ -122,9 +122,9 @@ def create_uncovered_modules( def main(): parser = argparse.ArgumentParser(description='Create modules for uncovered genes') - parser.add_argument('--merged_modules', type=str, - default=str(MODULE_DIR / 'merged_modules.json'), - help='Path to merged modules JSON file') + parser.add_argument('--merged_biomods', '--merged_modules', type=str, + default=str(MODULE_DIR / 'merged_biomods.json'), + help='Path to merged modules JSON file', dest='merged_modules') parser.add_argument('--gene_list', type=str, default=str(ASSET_ROOT / 'selected_genes_27k.txt'), help='Path to model gene list file') @@ -134,12 +134,12 @@ def main(): parser.add_argument('--output', '-o', type=str, default=str(MODULE_DIR / 'merged_modules_full.json'), help='Output file path') - parser.add_argument('--module_size', type=int, default=50, - help='Size of new modules for uncovered genes') + parser.add_argument('--biomod_size', '--module_size', type=int, default=50, + help='Size of new modules for uncovered genes', dest='module_size') args = parser.parse_args() - create_uncovered_modules( + create_uncovered_biomods( merged_modules_path=args.merged_modules, gene_list_path=args.gene_list, gene_mapping_path=args.gene_mapping, diff --git a/BioMod/module_data_all/merge_modules.py b/model_dependencies/biomod_data_all/merge_biomods.py similarity index 81% rename from BioMod/module_data_all/merge_modules.py rename to model_dependencies/biomod_data_all/merge_biomods.py index bae26048dcaf97610cb5613b019e8fca673c60e5..eba252a209c41b6a6aff51d78f0de114663a5496 100644 --- a/BioMod/module_data_all/merge_modules.py +++ b/model_dependencies/biomod_data_all/merge_biomods.py @@ -3,8 +3,8 @@ """Merge explicitly selected module JSON files. Run from the repository root, choosing the module sources for your experiment: - python BioMod/module_data_all/merge_modules.py --input first.json second.json \ - --output tutorial_runs/module_data/merged_modules.json + python model_dependencies/biomod_data_all/merge_biomods.py --input first.json second.json \ + --output tutorial_runs/biomod_data/merged_biomods.json The example input filenames are placeholders for your module definitions. """ @@ -18,7 +18,7 @@ ASSET_ROOT = PROJECT_ROOT / 'model_dependencies' MODULE_DIR = Path(__file__).resolve().parent -def merge_module_files(input_files, output_file, verbose=True): +def merge_biomod_files(input_files, output_file, verbose=True): """ Merge multiple module JSON files @@ -44,6 +44,8 @@ def merge_module_files(input_files, output_file, verbose=True): # Get the file name as a prefix to avoid key conflicts basename = os.path.splitext(os.path.basename(input_file))[0] + # Preserve original module IDs when renamed source files are merged. + basename = {'biomod1_hallmarks': 'module1_hallmarks', 'biomod2_reactome_2022_filtered': 'module2_reactome_2022_filtered'}.get(basename, basename) for key, genes in data.items(): if isinstance(genes, (list, tuple)): @@ -79,7 +81,7 @@ def main(): parser.add_argument('--input', '-i', nargs='+', required=True, help='Explicit input module JSON files; historical source collections are not all bundled.') parser.add_argument('--output', '-o', - default=str(MODULE_DIR / 'merged_modules.json'), + default=str(MODULE_DIR / 'merged_biomods.json'), help='Output merged JSON file') parser.add_argument('--add-external', type=str, default=None, help='Additional external module file to include') @@ -90,7 +92,7 @@ def main(): if args.add_external: input_files.append(args.add_external) - merge_module_files(input_files, args.output) + merge_biomod_files(input_files, args.output) if __name__ == '__main__': main() diff --git a/BioMod/module_data_all/merged_modules.json b/model_dependencies/biomod_data_all/merged_biomods.json similarity index 100% rename from BioMod/module_data_all/merged_modules.json rename to model_dependencies/biomod_data_all/merged_biomods.json diff --git a/model_dependencies/module_data_all/create_uncovered_modules.py b/model_dependencies/module_data_all/create_uncovered_modules.py deleted file mode 100644 index 06b3139c99d2ffbac95360c171cd880df9a45cb8..0000000000000000000000000000000000000000 --- a/model_dependencies/module_data_all/create_uncovered_modules.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -Create additional modules for genes not covered by existing modules -Ensure that all model genes can participate in training - -Run from the repository root: - python BioMod/module_data_all/create_uncovered_modules.py \ - --merged_modules model_dependencies/module_data_all/merged_modules.json \ - --output tutorial_runs/module_data/merged_modules_full.json \ - --module_size 50 -""" - -import json -import os -import csv -import argparse -from pathlib import Path - -PROJECT_ROOT = Path(__file__).resolve().parents[2] -ASSET_ROOT = PROJECT_ROOT / 'model_dependencies' -MODULE_DIR = Path(__file__).resolve().parent - -def create_uncovered_modules( - merged_modules_path, - gene_list_path, - gene_mapping_path, - output_path, - module_size=50, - verbose=True -): - """ - Create additional modules for uncovered genes - - Args: - merged_modules_path: merged module file path - gene_list_path: model gene list path - gene_mapping_path: Gene mapping file path - output_path: output file path - module_size: the size of the new module - verbose: whether to print detailed information - """ - # Load model gene list - with open(gene_list_path, 'r') as f: - model_genes = set(line.strip() for line in f) - - # Load existing modules - with open(merged_modules_path, 'r') as f: - modules = json.load(f) - - # Load gene map (symbol -> ensembl) - symbol_to_ens = {} - ens_to_symbol = {} - with open(gene_mapping_path, 'r') as f: - reader = csv.DictReader(f) - for row in reader: - symbol = row.get('Approved symbol', '').strip() - ens_id = row.get('Ensembl gene ID', '').strip() - if symbol and ens_id: - symbol_to_ens[symbol] = ens_id - ens_to_symbol[ens_id] = symbol - - # Find genes that are covered - covered_genes_ens = set() - for module_name, genes in modules.items(): - for g in genes: - ens_id = symbol_to_ens.get(g) - if ens_id and ens_id in model_genes: - covered_genes_ens.add(ens_id) - elif g in model_genes: - covered_genes_ens.add(g) - - # Find uncovered genes - uncovered_genes = model_genes - covered_genes_ens - uncovered_genes = sorted(list(uncovered_genes)) - - if verbose: - print(f"Total number of model genes: {len(model_genes)}") - print(f"Genes covered: {len(covered_genes_ens)} ({len(covered_genes_ens)/len(model_genes)*100:.1f}%)") - print(f"Genes not covered: {len(uncovered_genes)} ({len(uncovered_genes)/len(model_genes)*100:.1f}%)") - - # Create new modules for uncovered genes - # Use Ensembl ID (because the model uses Ensembl ID) - new_modules = {} - num_new_modules = (len(uncovered_genes) + module_size - 1) // module_size - - for i in range(num_new_modules): - start_idx = i * module_size - end_idx = min((i + 1) * module_size, len(uncovered_genes)) - module_genes = uncovered_genes[start_idx:end_idx] - - # Try converting to symbol if there is a mapping, otherwise keep the Ensembl ID - module_genes_final = [] - for g in module_genes: - symbol = ens_to_symbol.get(g) - if symbol: - module_genes_final.append(symbol) - else: - module_genes_final.append(g) - - new_modules[f"uncovered_module_{i+1:04d}"] = module_genes_final - - if verbose: - print(f"\nCreated {len(new_modules)} new modules (each approx. {module_size} genes)") - - # Merge existing modules and new modules - merged_modules = {**modules, **new_modules} - - # save - os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else '.', exist_ok=True) - with open(output_path, 'w', encoding='utf-8') as f: - json.dump(merged_modules, f, indent=2) - - if verbose: - print(f"\nFinal result:") - print(f" Original modules: {len(modules)}") - print(f" New modules: {len(new_modules)}") - print(f" Total modules: {len(merged_modules)}") - print(f" Save to: {output_path}") - - return merged_modules - -def main(): - parser = argparse.ArgumentParser(description='Create modules for uncovered genes') - parser.add_argument('--merged_modules', type=str, - default=str(MODULE_DIR / 'merged_modules.json'), - help='Path to merged modules JSON file') - parser.add_argument('--gene_list', type=str, - default=str(ASSET_ROOT / 'selected_genes_27k.txt'), - help='Path to model gene list file') - parser.add_argument('--gene_mapping', type=str, - default=str(ASSET_ROOT / 'gene2ens.csv'), - help='Path to gene mapping CSV file') - parser.add_argument('--output', '-o', type=str, - default=str(MODULE_DIR / 'merged_modules_full.json'), - help='Output file path') - parser.add_argument('--module_size', type=int, default=50, - help='Size of new modules for uncovered genes') - - args = parser.parse_args() - - create_uncovered_modules( - merged_modules_path=args.merged_modules, - gene_list_path=args.gene_list, - gene_mapping_path=args.gene_mapping, - output_path=args.output, - module_size=args.module_size - ) - -if __name__ == '__main__': - main() diff --git a/model_dependencies/module_data_all/merge_modules.py b/model_dependencies/module_data_all/merge_modules.py deleted file mode 100644 index bae26048dcaf97610cb5613b019e8fca673c60e5..0000000000000000000000000000000000000000 --- a/model_dependencies/module_data_all/merge_modules.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -"""Merge explicitly selected module JSON files. - -Run from the repository root, choosing the module sources for your experiment: - python BioMod/module_data_all/merge_modules.py --input first.json second.json \ - --output tutorial_runs/module_data/merged_modules.json - -The example input filenames are placeholders for your module definitions. -""" - -import json -import os -import argparse -from pathlib import Path - -PROJECT_ROOT = Path(__file__).resolve().parents[2] -ASSET_ROOT = PROJECT_ROOT / 'model_dependencies' -MODULE_DIR = Path(__file__).resolve().parent - -def merge_module_files(input_files, output_file, verbose=True): - """ - Merge multiple module JSON files - - Args: - input_files: list of input file paths - output_file: output file path - verbose: whether to print detailed information - """ - merged_modules = {} - total_modules = 0 - all_genes = set() - - for input_file in input_files: - if not os.path.exists(input_file): - print(f"Warning: File not found: {input_file}") - continue - - with open(input_file, 'r', encoding='utf-8') as f: - data = json.load(f) - - file_modules = 0 - file_genes = set() - - # Get the file name as a prefix to avoid key conflicts - basename = os.path.splitext(os.path.basename(input_file))[0] - - for key, genes in data.items(): - if isinstance(genes, (list, tuple)): - # Add file name prefix to avoid key conflicts - new_key = f"{basename}__{key}" - merged_modules[new_key] = list(genes) - file_modules += 1 - file_genes.update(genes) - - total_modules += file_modules - all_genes.update(file_genes) - - if verbose: - print(f"{os.path.basename(input_file)}:") - print(f" Modules: {file_modules}") - print(f" Unique genes: {len(file_genes)}") - - # Save the merged file - os.makedirs(os.path.dirname(output_file) if os.path.dirname(output_file) else '.', exist_ok=True) - with open(output_file, 'w', encoding='utf-8') as f: - json.dump(merged_modules, f, indent=2) - - if verbose: - print("=" * 50) - print(f"Total modules: {total_modules}") - print(f"Total unique genes: {len(all_genes)}") - print(f"Saved to: {output_file}") - - return merged_modules - -def main(): - parser = argparse.ArgumentParser(description='Merge multiple module JSON files') - parser.add_argument('--input', '-i', nargs='+', required=True, - help='Explicit input module JSON files; historical source collections are not all bundled.') - parser.add_argument('--output', '-o', - default=str(MODULE_DIR / 'merged_modules.json'), - help='Output merged JSON file') - parser.add_argument('--add-external', type=str, default=None, - help='Additional external module file to include') - - args = parser.parse_args() - - input_files = args.input - if args.add_external: - input_files.append(args.add_external) - - merge_module_files(input_files, args.output) - -if __name__ == '__main__': - main() diff --git a/model_dependencies/module_data_all/merged_modules.json b/model_dependencies/module_data_all/merged_modules.json deleted file mode 100644 index d244057f2c917c059ac5d7a6e6468ae442eee2c1..0000000000000000000000000000000000000000 --- a/model_dependencies/module_data_all/merged_modules.json +++ /dev/null @@ -1,70937 +0,0 @@ -{ - "module1_hallmarks__HALLMARK_ADIPOGENESIS": [ - "ABCA1", - "ABCB8", - "ACAA2", - "ACADL", - "ACADM", - "ACADS", - "ACLY", - "ACO2", - "ACOX1", - "ADCY6", - "ADIG", - "ADIPOQ", - "ADIPOR2", - "AGPAT3", - "AIFM1", - "AK2", - "ALDH2", - "ALDOA", - "ANGPT1", - "ANGPTL4", - "APLP2", - "APOE", - "ARAF", - "ARL4A", - "ATL2", - "ATP1B3", - "ATP5PO", - "BAZ2A", - "BCKDHA", - "BCL2L13", - "BCL6", - "C3", - "CAT", - "CAVIN1", - "CAVIN2", - "CCNG2", - "CD151", - "CD302", - "CD36", - "CDKN2C", - "CHCHD10", - "CHUK", - "CIDEA", - "CMBL", - "CMPK1", - "COL15A1", - "COL4A1", - "COQ3", - "COQ5", - "COQ9", - "COX6A1", - "COX7B", - "COX8A", - "CPT2", - "CRAT", - "CS", - "CYC1", - "CYP4B1", - "DBT", - "DDT", - "DECR1", - "DGAT1", - "DHCR7", - "DHRS7", - "DHRS7B", - "DLAT", - "DLD", - "DNAJB9", - "DNAJC15", - "DRAM2", - "ECH1", - "ECHS1", - "ELMOD3", - "ELOVL6", - "ENPP2", - "EPHX2", - "ESRRA", - "ESYT1", - "ETFB", - "FABP4", - "FAH", - "FZD4", - "G3BP2", - "GADD45A", - "GBE1", - "GHITM", - "GPAM", - "GPAT4", - "GPD2", - "GPHN", - "GPX3", - "GPX4", - "GRPEL1", - "HADH", - "HIBCH", - "HSPB8", - "IDH1", - "IDH3A", - "IDH3G", - "IFNGR1", - "IMMT", - "ITGA7", - "ITIH5", - "ITSN1", - "JAGN1", - "LAMA4", - "LEP", - "LIFR", - "LIPE", - "LPCAT3", - "LPL", - "LTC4S", - "MAP4K3", - "MCCC1", - "MDH2", - "ME1", - "MGLL", - "MGST3", - "MIGA2", - "MRAP", - "MRPL15", - "MTARC2", - "MTCH2", - "MYLK", - "NABP1", - "NDUFA5", - "NDUFAB1", - "NDUFB7", - "NDUFS3", - "NKIRAS1", - "NMT1", - "OMD", - "ORM1", - "PDCD4", - "PEMT", - "PEX14", - "PFKFB3", - "PFKL", - "PGM1", - "PHLDB1", - "PHYH", - "PIM3", - "PLIN2", - "POR", - "PPARG", - "PPM1B", - "PPP1R15B", - "PRDX3", - "PREB", - "PTCD3", - "PTGER3", - "QDPR", - "RAB34", - "REEP5", - "REEP6", - "RETN", - "RETSAT", - "RIOK3", - "RMDN3", - "RNF11", - "RREB1", - "RTN3", - "SAMM50", - "SCARB1", - "SCP2", - "SDHB", - "SDHC", - "SLC19A1", - "SLC1A5", - "SLC25A1", - "SLC25A10", - "SLC27A1", - "SLC5A6", - "SLC66A3", - "SNCG", - "SOD1", - "SORBS1", - "SOWAHC", - "SPARCL1", - "SQOR", - "SSPN", - "STAT5A", - "STOM", - "SUCLG1", - "SULT1A1", - "TALDO1", - "TANK", - "TKT", - "TOB1", - "TST", - "UBC", - "UBQLN1", - "UCK1", - "UCP2", - "UQCR10", - "UQCR11", - "UQCRC1", - "UQCRQ", - "VEGFB", - "YWHAG" - ], - "module1_hallmarks__HALLMARK_ALLOGRAFT_REJECTION": [ - "AARS1", - "ABCE1", - "ABI1", - "ACHE", - "ACVR2A", - "AKT1", - "APBB1", - "B2M", - "BCAT1", - "BCL10", - "BCL3", - "BRCA1", - "C2", - "CAPG", - "CARTPT", - "CCL11", - "CCL13", - "CCL19", - "CCL2", - "CCL22", - "CCL4", - "CCL5", - "CCL7", - "CCND2", - "CCND3", - "CCR1", - "CCR2", - "CCR5", - "CD1D", - "CD2", - "CD247", - "CD28", - "CD3D", - "CD3E", - "CD3G", - "CD4", - "CD40", - "CD40LG", - "CD47", - "CD7", - "CD74", - "CD79A", - "CD80", - "CD86", - "CD8A", - "CD8B", - "CD96", - "CDKN2A", - "CFP", - "CRTAM", - "CSF1", - "CSK", - "CTSS", - "CXCL13", - "CXCL9", - "CXCR3", - "DARS1", - "DEGS1", - "DYRK3", - "EGFR", - "EIF3A", - "EIF3D", - "EIF3J", - "EIF4G3", - "EIF5A", - "ELANE", - "ELF4", - "EREG", - "ETS1", - "F2", - "F2R", - "FAS", - "FASLG", - "FCGR2B", - "FGR", - "FLNA", - "FYB1", - "GALNT1", - "GBP2", - "GCNT1", - "GLMN", - "GPR65", - "GZMA", - "GZMB", - "HCLS1", - "HDAC9", - "HIF1A", - "HLA-A", - "HLA-DMA", - "HLA-DMB", - "HLA-DOA", - "HLA-DOB", - "HLA-DQA1", - "HLA-DRA", - "HLA-E", - "HLA-G", - "ICAM1", - "ICOSLG", - "IFNAR2", - "IFNG", - "IFNGR1", - "IFNGR2", - "IGSF6", - "IKBKB", - "IL10", - "IL11", - "IL12A", - "IL12B", - "IL12RB1", - "IL13", - "IL15", - "IL16", - "IL18", - "IL18RAP", - "IL1B", - "IL2", - "IL27RA", - "IL2RA", - "IL2RB", - "IL2RG", - "IL4", - "IL4R", - "IL6", - "IL7", - "IL9", - "INHBA", - "INHBB", - "IRF4", - "IRF7", - "IRF8", - "ITGAL", - "ITGB2", - "ITK", - "JAK2", - "KLRD1", - "KRT1", - "LCK", - "LCP2", - "LIF", - "LTB", - "LY75", - "LY86", - "LYN", - "MAP3K7", - "MAP4K1", - "MBL2", - "MMP9", - "MRPL3", - "MTIF2", - "NCF4", - "NCK1", - "NCR1", - "NLRP3", - "NME1", - "NOS2", - "NPM1", - "PF4", - "PRF1", - "PRKCB", - "PRKCG", - "PSMB10", - "PTPN6", - "PTPRC", - "RARS1", - "RIPK2", - "RPL39", - "RPL3L", - "RPL9", - "RPS19", - "RPS3A", - "RPS9", - "SIT1", - "SOCS1", - "SOCS5", - "SPI1", - "SRGN", - "ST8SIA4", - "STAB1", - "STAT1", - "STAT4", - "TAP1", - "TAP2", - "TAPBP", - "TGFB1", - "TGFB2", - "THY1", - "TIMP1", - "TLR1", - "TLR2", - "TLR3", - "TLR6", - "TNF", - "TPD52", - "TRAF2", - "TRAT1", - "UBE2D1", - "UBE2N", - "WARS1", - "WAS", - "ZAP70" - ], - "module1_hallmarks__HALLMARK_ANDROGEN_RESPONSE": [ - "ABCC4", - "ABHD2", - "ACSL3", - "ACTN1", - "ADAMTS1", - "ADRM1", - "AKAP12", - "AKT1", - "ALDH1A3", - "ANKH", - "APPBP2", - "ARID5B", - "AZGP1", - "B2M", - "B4GALT1", - "BMPR1B", - "CAMKK2", - "CCND1", - "CCND3", - "CDC14B", - "CDK6", - "CENPN", - "DBI", - "DHCR24", - "DNAJB9", - "ELK4", - "ELL2", - "ELOVL5", - "FADS1", - "FKBP5", - "GNAI3", - "GPD1L", - "GSR", - "GUCY1A1", - "H1-0", - "HERC3", - "HMGCR", - "HMGCS1", - "HOMER2", - "HPGD", - "HSD17B14", - "IDI1", - "INPP4B", - "INSIG1", - "IQGAP2", - "ITGAV", - "KLK2", - "KLK3", - "KRT19", - "KRT8", - "LIFR", - "LMAN1", - "MAF", - "MAK", - "MAP7", - "MERTK", - "MYL12A", - "NCOA4", - "NDRG1", - "NGLY1", - "NKX3-1", - "PA2G4", - "PDLIM5", - "PGM3", - "PIAS1", - "PLPP1", - "PMEPA1", - "PTK2B", - "PTPN21", - "RAB4A", - "RPS6KA3", - "RRP12", - "SAT1", - "SCD", - "SEC24D", - "SELENOP", - "SGK1", - "SLC26A2", - "SLC38A2", - "SMS", - "SORD", - "SPCS3", - "SPDEF", - "SRF", - "SRP19", - "STEAP4", - "STK39", - "TARP", - "TMEM50A", - "TMPRSS2", - "TNFAIP8", - "TPD52", - "TSC22D1", - "UAP1", - "UBE2I", - "UBE2J1", - "VAPA", - "XRCC5", - "XRCC6", - "ZBTB10", - "ZMIZ1" - ], - "module1_hallmarks__HALLMARK_ANGIOGENESIS": [ - "APOH", - "APP", - "CCND2", - "COL3A1", - "COL5A2", - "CXCL6", - "FGFR1", - "FSTL1", - "ITGAV", - "JAG1", - "JAG2", - "KCNJ8", - "LPL", - "LRPAP1", - "LUM", - "MSX1", - "NRP1", - "OLR1", - "PDGFA", - "PF4", - "PGLYRP1", - "POSTN", - "PRG2", - "PTK2", - "S100A4", - "SERPINA5", - "SLCO2A1", - "SPP1", - "STC1", - "THBD", - "TIMP1", - "TNFRSF21", - "VAV2", - "VCAN", - "VEGFA", - "VTN" - ], - "module1_hallmarks__HALLMARK_APICAL_JUNCTION": [ - "ACTA1", - "ACTB", - "ACTC1", - "ACTG1", - "ACTG2", - "ACTN1", - "ACTN2", - "ACTN3", - "ACTN4", - "ADAM15", - "ADAM23", - "ADAM9", - "ADAMTS5", - "ADRA1B", - "AKT2", - "AKT3", - "ALOX15B", - "AMH", - "AMIGO1", - "AMIGO2", - "ARHGEF6", - "ARPC2", - "ATP1A3", - "B4GALT1", - "BAIAP2", - "BMP1", - "CADM2", - "CADM3", - "CALB2", - "CAP1", - "CD209", - "CD274", - "CD276", - "CD34", - "CD86", - "CD99", - "CDH1", - "CDH11", - "CDH15", - "CDH3", - "CDH4", - "CDH6", - "CDH8", - "CDK8", - "CDSN", - "CERCAM", - "CLDN11", - "CLDN14", - "CLDN15", - "CLDN18", - "CLDN19", - "CLDN4", - "CLDN5", - "CLDN6", - "CLDN7", - "CLDN8", - "CLDN9", - "CNN2", - "CNTN1", - "COL16A1", - "COL17A1", - "COL9A1", - "CRAT", - "CRB3", - "CTNNA1", - "CTNND1", - "CX3CL1", - "DHX16", - "DLG1", - "DMP1", - "DSC1", - "DSC3", - "EGFR", - "EPB41L2", - "EVL", - "EXOC4", - "FBN1", - "FLNC", - "FSCN1", - "FYB1", - "GAMT", - "GNAI1", - "GNAI2", - "GRB7", - "GTF2F1", - "HADH", - "HRAS", - "ICAM1", - "ICAM2", - "ICAM4", - "ICAM5", - "IKBKG", - "INPPL1", - "INSIG1", - "IRS1", - "ITGA10", - "ITGA2", - "ITGA3", - "ITGA9", - "ITGB1", - "ITGB4", - "JAM3", - "JUP", - "KCNH2", - "KRT31", - "LAMA3", - "LAMB3", - "LAMC2", - "LAYN", - "LDLRAP1", - "LIMA1", - "MADCAM1", - "MAP3K20", - "MAP4K2", - "MAPK11", - "MAPK13", - "MAPK14", - "MDK", - "MMP2", - "MMP9", - "MPZL1", - "MPZL2", - "MSN", - "MVD", - "MYH10", - "MYH9", - "MYL12B", - "MYL9", - "NECTIN1", - "NECTIN2", - "NECTIN3", - "NECTIN4", - "NEGR1", - "NEXN", - "NF1", - "NF2", - "NFASC", - "NHERF4", - "NLGN2", - "NLGN3", - "NRAP", - "NRTN", - "NRXN2", - "PALS1", - "PARD6G", - "PARVA", - "PBX2", - "PCDH1", - "PECAM1", - "PFN1", - "PIK3CB", - "PIK3R3", - "PKD1", - "PLCG1", - "PPP2R2C", - "PTEN", - "PTK2", - "PTPRC", - "RAC2", - "RASA1", - "RHOF", - "RRAS", - "RSU1", - "SDC3", - "SGCE", - "SHC1", - "SHROOM2", - "SIRPA", - "SKAP2", - "SLC30A3", - "SLIT2", - "SORBS3", - "SPEG", - "SRC", - "STX4", - "SYK", - "SYMPK", - "TAOK2", - "TGFBI", - "THBS3", - "THY1", - "TIAL1", - "TJP1", - "TMEM8B", - "TNFRSF11B", - "TRAF1", - "TRO", - "TSC1", - "TSPAN4", - "TUBG1", - "VASP", - "VAV2", - "VCAM1", - "VCAN", - "VCL", - "VWF", - "WASL", - "WNK4", - "YWHAH", - "ZYX" - ], - "module1_hallmarks__HALLMARK_APICAL_SURFACE": [ - "ADAM10", - "ADIPOR2", - "AFAP1L2", - "AKAP7", - "APP", - "ATP6V0A4", - "ATP8B1", - "B4GALT1", - "BRCA1", - "CD160", - "CROCC", - "CRYBG1", - "CX3CL1", - "DCBLD2", - "EFNA5", - "EPHB4", - "FLOT2", - "GAS1", - "GATA3", - "GHRL", - "GSTM3", - "HSPB1", - "IL2RB", - "IL2RG", - "LYN", - "LYPD3", - "MAL", - "MDGA1", - "NCOA6", - "NTNG1", - "PCSK9", - "PKHD1", - "PLAUR", - "RHCG", - "RTN4RL1", - "SCUBE1", - "SHROOM2", - "SLC22A12", - "SLC2A4", - "SLC34A3", - "SRPX", - "SULF2", - "THY1", - "TMEM8B" - ], - "module1_hallmarks__HALLMARK_APOPTOSIS": [ - "ADD1", - "AIFM3", - "ANKH", - "ANXA1", - "APP", - "ATF3", - "AVPR1A", - "BAX", - "BCAP31", - "BCL10", - "BCL2L1", - "BCL2L10", - "BCL2L11", - "BCL2L2", - "BGN", - "BID", - "BIK", - "BIRC3", - "BMF", - "BMP2", - "BNIP3L", - "BRCA1", - "BTG2", - "BTG3", - "CASP1", - "CASP2", - "CASP3", - "CASP4", - "CASP6", - "CASP7", - "CASP8", - "CASP9", - "CAV1", - "CCNA1", - "CCND1", - "CCND2", - "CD14", - "CD2", - "CD38", - "CD44", - "CD69", - "CDC25B", - "CDK2", - "CDKN1A", - "CDKN1B", - "CFLAR", - "CLU", - "CREBBP", - "CTH", - "CTNNB1", - "CYLD", - "DAP", - "DAP3", - "DCN", - "DDIT3", - "DFFA", - "DIABLO", - "DNAJA1", - "DNAJC3", - "DNM1L", - "DPYD", - "EBP", - "EGR3", - "EMP1", - "ENO2", - "ERBB2", - "ERBB3", - "EREG", - "ETF1", - "F2", - "F2R", - "FAS", - "FASLG", - "FDXR", - "FEZ1", - "GADD45A", - "GADD45B", - "GCH1", - "GNA15", - "GPX1", - "GPX3", - "GPX4", - "GSN", - "GSR", - "GSTM1", - "GUCY2D", - "H1-0", - "HGF", - "HMGB2", - "HMOX1", - "HSPB1", - "IER3", - "IFITM3", - "IFNB1", - "IFNGR1", - "IGF2R", - "IGFBP6", - "IL18", - "IL1A", - "IL1B", - "IL6", - "IRF1", - "ISG20", - "JUN", - "KRT18", - "LEF1", - "LGALS3", - "LMNA", - "LUM", - "MADD", - "MCL1", - "MGMT", - "MMP2", - "NEDD9", - "NEFH", - "PAK1", - "PDCD4", - "PDGFRB", - "PEA15", - "PLAT", - "PLCB2", - "PLPPR4", - "PMAIP1", - "PPP2R5B", - "PPP3R1", - "PPT1", - "PRF1", - "PSEN1", - "PSEN2", - "PTK2", - "RARA", - "RELA", - "RETSAT", - "RHOB", - "RHOT2", - "RNASEL", - "ROCK1", - "SAT1", - "SATB1", - "SC5D", - "SLC20A1", - "SMAD7", - "SOD1", - "SOD2", - "SPTAN1", - "SQSTM1", - "TAP1", - "TGFB2", - "TGFBR3", - "TIMP1", - "TIMP2", - "TIMP3", - "TNF", - "TNFRSF12A", - "TNFSF10", - "TOP2A", - "TSPO", - "TXNIP", - "VDAC2", - "WEE1", - "XIAP" - ], - "module1_hallmarks__HALLMARK_BILE_ACID_METABOLISM": [ - "ABCA1", - "ABCA2", - "ABCA3", - "ABCA4", - "ABCA5", - "ABCA6", - "ABCA8", - "ABCA9", - "ABCD1", - "ABCD2", - "ABCD3", - "ABCG4", - "ABCG8", - "ACSL1", - "ACSL5", - "AGXT", - "AKR1D1", - "ALDH1A1", - "ALDH8A1", - "ALDH9A1", - "AMACR", - "APOA1", - "AQP9", - "AR", - "ATXN1", - "BBOX1", - "BCAR3", - "BMP6", - "CAT", - "CH25H", - "CROT", - "CYP27A1", - "CYP39A1", - "CYP46A1", - "CYP7A1", - "CYP7B1", - "CYP8B1", - "DHCR24", - "DIO1", - "DIO2", - "EFHC1", - "EPHX2", - "FADS1", - "FADS2", - "FDXR", - "GC", - "GCLM", - "GNMT", - "GNPAT", - "GSTK1", - "HACL1", - "HAO1", - "HSD17B11", - "HSD17B4", - "HSD17B6", - "HSD3B1", - "HSD3B7", - "IDH1", - "IDH2", - "IDI1", - "ISOC1", - "KLF1", - "LCK", - "LIPE", - "LONP2", - "MLYCD", - "NEDD4", - "NPC1", - "NR0B2", - "NR1H4", - "NR1I2", - "NR3C2", - "NUDT12", - "OPTN", - "PAOX", - "PECR", - "PEX1", - "PEX11A", - "PEX11G", - "PEX12", - "PEX13", - "PEX16", - "PEX19", - "PEX26", - "PEX6", - "PEX7", - "PFKM", - "PHYH", - "PIPOX", - "PNPLA8", - "PRDX5", - "PXMP2", - "RBP1", - "RETSAT", - "RXRA", - "RXRG", - "SCP2", - "SERPINA6", - "SLC22A18", - "SLC23A1", - "SLC23A2", - "SLC27A2", - "SLC27A5", - "SLC29A1", - "SLC35B2", - "SLCO1A2", - "SOAT2", - "SOD1", - "SULT1B1", - "SULT2B1", - "TFCP2L1", - "TTR" - ], - "module1_hallmarks__HALLMARK_CHOLESTEROL_HOMEOSTASIS": [ - "ABCA2", - "ACAT2", - "ACSS2", - "ACTG1", - "ADH4", - "ALCAM", - "ALDOC", - "ANTXR2", - "ANXA13", - "ANXA5", - "ATF3", - "ATF5", - "ATXN2", - "AVPR1A", - "CBS", - "CD9", - "CHKA", - "CLU", - "CPEB2", - "CTNNB1", - "CXCL16", - "CYP51A1", - "DHCR7", - "EBP", - "ECH1", - "ERRFI1", - "ETHE1", - "FABP5", - "FADS2", - "FASN", - "FBXO6", - "FDFT1", - "FDPS", - "GLDC", - "GNAI1", - "GPX8", - "GSTM2", - "GUSB", - "HMGCR", - "HMGCS1", - "HSD17B7", - "IDI1", - "JAG1", - "LDLR", - "LGALS3", - "LGMN", - "LPL", - "LSS", - "MAL2", - "MVD", - "MVK", - "NFIL3", - "NIBAN1", - "NSDHL", - "PCYT2", - "PDK3", - "PLAUR", - "PLSCR1", - "PMVK", - "PNRC1", - "PPARG", - "S100A11", - "SC5D", - "SCD", - "SEMA3B", - "SQLE", - "SREBF2", - "STARD4", - "STX5", - "TM7SF2", - "TMEM97", - "TNFRSF12A", - "TP53INP1", - "TRIB3" - ], - "module1_hallmarks__HALLMARK_COAGULATION": [ - "A2M", - "ACOX2", - "ADAM9", - "ANG", - "ANXA1", - "APOA1", - "APOC1", - "APOC2", - "APOC3", - "ARF4", - "BMP1", - "C1QA", - "C1R", - "C1S", - "C2", - "C3", - "C8A", - "C8B", - "C8G", - "C9", - "CAPN2", - "CAPN5", - "CASP9", - "CD9", - "CFB", - "CFD", - "CFH", - "CFI", - "CLU", - "COMP", - "CPB2", - "CPN1", - "CPQ", - "CRIP2", - "CSRP1", - "CTSB", - "CTSE", - "CTSH", - "CTSK", - "CTSO", - "CTSV", - "DCT", - "DPP4", - "DUSP14", - "DUSP6", - "F10", - "F11", - "F12", - "F13B", - "F2", - "F2RL2", - "F3", - "F8", - "F9", - "FBN1", - "FGA", - "FGG", - "FN1", - "FURIN", - "FYN", - "GDA", - "GNB2", - "GNG12", - "GP1BA", - "GP9", - "GSN", - "HMGCS2", - "HNF4A", - "HPN", - "HRG", - "HTRA1", - "ISCU", - "ITGA2", - "ITGB3", - "ITIH1", - "KLF7", - "KLK8", - "KLKB1", - "LAMP2", - "LEFTY2", - "LGMN", - "LRP1", - "LTA4H", - "MAFF", - "MASP2", - "MBL2", - "MEP1A", - "MMP1", - "MMP10", - "MMP11", - "MMP14", - "MMP15", - "MMP2", - "MMP3", - "MMP7", - "MMP8", - "MMP9", - "MSRB2", - "MST1", - "OLR1", - "P2RY1", - "PDGFB", - "PECAM1", - "PEF1", - "PF4", - "PLAT", - "PLAU", - "PLEK", - "PLG", - "PREP", - "PROC", - "PROS1", - "PROZ", - "PRSS23", - "RABIF", - "RAC1", - "RAPGEF3", - "RGN", - "S100A1", - "S100A13", - "SERPINA1", - "SERPINB2", - "SERPINC1", - "SERPINE1", - "SERPING1", - "SH2B2", - "SIRT2", - "SPARC", - "TF", - "TFPI2", - "THBD", - "THBS1", - "TIMP1", - "TIMP3", - "TMPRSS6", - "USP11", - "VWF", - "WDR1" - ], - "module1_hallmarks__HALLMARK_COMPLEMENT": [ - "ACTN2", - "ADAM9", - "ADRA2B", - "AKAP10", - "ANG", - "ANXA5", - "APOA4", - "APOBEC3F", - "APOBEC3G", - "APOC1", - "ATOX1", - "BRPF3", - "C1QA", - "C1QC", - "C1R", - "C1S", - "C2", - "C3", - "C4BPB", - "C9", - "CA2", - "CALM1", - "CALM3", - "CASP1", - "CASP10", - "CASP3", - "CASP4", - "CASP5", - "CASP7", - "CASP9", - "CBLB", - "CCL5", - "CD36", - "CD40LG", - "CD46", - "CD55", - "CD59", - "CDA", - "CDH13", - "CDK5R1", - "CEBPB", - "CFB", - "CFH", - "CLU", - "COL4A2", - "CP", - "CPM", - "CPQ", - "CR1", - "CR2", - "CSRP1", - "CTSB", - "CTSC", - "CTSD", - "CTSH", - "CTSL", - "CTSO", - "CTSS", - "CTSV", - "CXCL1", - "DGKG", - "DGKH", - "DOCK10", - "DOCK4", - "DOCK9", - "DPP4", - "DUSP5", - "DUSP6", - "DYRK2", - "EHD1", - "ERAP2", - "F10", - "F2", - "F3", - "F5", - "F7", - "F8", - "FCER1G", - "FCN1", - "FDX1", - "FN1", - "FYN", - "GATA3", - "GCA", - "GMFB", - "GNAI2", - "GNAI3", - "GNB2", - "GNB4", - "GNG2", - "GNGT2", - "GP1BA", - "GP9", - "GPD2", - "GRB2", - "GZMA", - "GZMB", - "GZMK", - "HNF4A", - "HPCAL4", - "HSPA1A", - "HSPA5", - "IL6", - "IRF1", - "IRF2", - "IRF7", - "ITGAM", - "ITIH1", - "JAK2", - "KCNIP2", - "KCNIP3", - "KIF2A", - "KLK1", - "KLKB1", - "KYNU", - "L3MBTL4", - "LAMP2", - "LAP3", - "LCK", - "LCP2", - "LGALS3", - "LGMN", - "LIPA", - "LRP1", - "LTA4H", - "LTF", - "LYN", - "MAFF", - "ME1", - "MMP12", - "MMP13", - "MMP14", - "MMP15", - "MMP8", - "MSRB1", - "MT3", - "NOTCH4", - "OLR1", - "PCLO", - "PCSK9", - "PDGFB", - "PDP1", - "PFN1", - "PHEX", - "PIK3CA", - "PIK3CG", - "PIK3R5", - "PIM1", - "PLA2G4A", - "PLA2G7", - "PLAT", - "PLAUR", - "PLEK", - "PLG", - "PLSCR1", - "PPP2CB", - "PPP4C", - "PRCP", - "PRDM4", - "PREP", - "PRKCD", - "PRSS3", - "PRSS36", - "PSEN1", - "PSMB9", - "RABIF", - "RAF1", - "RASGRP1", - "RBSN", - "RCE1", - "RHOG", - "RNF4", - "S100A12", - "S100A13", - "S100A9", - "SCG3", - "SERPINA1", - "SERPINB2", - "SERPINC1", - "SERPINE1", - "SERPING1", - "SH2B3", - "SIRT6", - "SPOCK2", - "SRC", - "STX4", - "TFPI2", - "TIMP1", - "TIMP2", - "TMPRSS6", - "TNFAIP3", - "USP14", - "USP15", - "USP16", - "USP8", - "VCPIP1", - "WAS", - "XPNPEP1", - "ZEB1", - "ZFPM2" - ], - "module1_hallmarks__HALLMARK_DNA_REPAIR": [ - "AAAS", - "ADA", - "ADCY6", - "ADRM1", - "AGO4", - "AK1", - "AK3", - "ALYREF", - "APRT", - "ARL6IP1", - "BCAM", - "BCAP31", - "BOLA2", - "BRF2", - "CANT1", - "CCNO", - "CDA", - "CETN2", - "CLP1", - "CMPK2", - "COX17", - "CSTF3", - "DAD1", - "DCTN4", - "DDB1", - "DDB2", - "DGCR8", - "DGUOK", - "DUT", - "EDF1", - "EIF1B", - "ELL", - "ELOA", - "ERCC1", - "ERCC2", - "ERCC3", - "ERCC4", - "ERCC5", - "ERCC8", - "FEN1", - "GMPR2", - "GPX4", - "GSDME", - "GTF2A2", - "GTF2B", - "GTF2F1", - "GTF2H1", - "GTF2H3", - "GTF2H5", - "GTF3C5", - "GUK1", - "HCLS1", - "HPRT1", - "IMPDH2", - "ITPA", - "LIG1", - "MPC2", - "MPG", - "MRPL40", - "NCBP2", - "NELFB", - "NELFCD", - "NELFE", - "NFX1", - "NME1", - "NME3", - "NME4", - "NPR2", - "NT5C", - "NT5C3A", - "NUDT21", - "NUDT9", - "PCNA", - "PDE4B", - "PDE6G", - "PNP", - "POLA1", - "POLA2", - "POLB", - "POLD1", - "POLD3", - "POLD4", - "POLE4", - "POLH", - "POLL", - "POLR1C", - "POLR1D", - "POLR1H", - "POLR2A", - "POLR2C", - "POLR2D", - "POLR2E", - "POLR2F", - "POLR2G", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR3C", - "POLR3GL", - "POM121", - "PRIM1", - "RAD51", - "RAD52", - "RAE1", - "RALA", - "RBX1", - "REV3L", - "RFC2", - "RFC3", - "RFC4", - "RFC5", - "RNMT", - "RPA2", - "RPA3", - "RRM2B", - "SAC3D1", - "SDCBP", - "SEC61A1", - "SF3A3", - "SMAD5", - "SNAPC4", - "SNAPC5", - "SRSF6", - "SSRP1", - "STX3", - "SUPT4H1", - "SUPT5H", - "SURF1", - "TAF10", - "TAF12", - "TAF13", - "TAF1C", - "TAF6", - "TAF9", - "TARBP2", - "TK2", - "TMED2", - "TP53", - "TSG101", - "TYMS", - "UMPS", - "UPF3B", - "USP11", - "VPS28", - "VPS37B", - "VPS37D", - "XPC", - "ZNF707", - "ZWINT" - ], - "module1_hallmarks__HALLMARK_E2F_TARGETS": [ - "AK2", - "ANP32E", - "ASF1A", - "ASF1B", - "ATAD2", - "AURKA", - "AURKB", - "BARD1", - "BIRC5", - "BRCA1", - "BRCA2", - "BRMS1L", - "BUB1B", - "CBX5", - "CCNB2", - "CCNE1", - "CCP110", - "CDC20", - "CDC25A", - "CDC25B", - "CDCA3", - "CDCA8", - "CDK1", - "CDK4", - "CDKN1A", - "CDKN1B", - "CDKN2A", - "CDKN2C", - "CDKN3", - "CENPE", - "CENPM", - "CHEK1", - "CHEK2", - "CIT", - "CKS1B", - "CKS2", - "CNOT9", - "CSE1L", - "CTCF", - "CTPS1", - "DCK", - "DCLRE1B", - "DCTPP1", - "DDX39A", - "DEK", - "DEPDC1", - "DIAPH3", - "DLGAP5", - "DNMT1", - "DONSON", - "DSCC1", - "DUT", - "E2F8", - "EED", - "EIF2S1", - "ESPL1", - "EXOSC8", - "EZH2", - "GINS1", - "GINS3", - "GINS4", - "GSPT1", - "H2AX", - "H2AZ1", - "HELLS", - "HMGA1", - "HMGB2", - "HMGB3", - "HMMR", - "HNRNPD", - "HUS1", - "ILF3", - "ING3", - "IPO7", - "JPT1", - "KIF18B", - "KIF22", - "KIF2C", - "KIF4A", - "KPNA2", - "LBR", - "LIG1", - "LMNB1", - "LUC7L3", - "LYAR", - "MAD2L1", - "MCM2", - "MCM3", - "MCM4", - "MCM5", - "MCM6", - "MCM7", - "MELK", - "MKI67", - "MLH1", - "MMS22L", - "MRE11", - "MSH2", - "MTHFD2", - "MXD3", - "MYBL2", - "MYC", - "NAA38", - "NAP1L1", - "NASP", - "NBN", - "NCAPD2", - "NME1", - "NOLC1", - "NOP56", - "NUDT21", - "NUP107", - "NUP153", - "NUP205", - "ORC2", - "ORC6", - "PA2G4", - "PAICS", - "PAN2", - "PCNA", - "PDS5B", - "PHF5A", - "PLK1", - "PLK4", - "PMS2", - "PNN", - "POLA2", - "POLD1", - "POLD2", - "POLD3", - "POLE", - "POLE4", - "POP7", - "PPM1D", - "PPP1R8", - "PRDX4", - "PRIM2", - "PRKDC", - "PRPS1", - "PSIP1", - "PSMC3IP", - "PTTG1", - "RACGAP1", - "RAD1", - "RAD21", - "RAD50", - "RAD51AP1", - "RAD51C", - "RAN", - "RANBP1", - "RBBP7", - "RFC1", - "RFC2", - "RFC3", - "RNASEH2A", - "RPA1", - "RPA2", - "RPA3", - "RRM2", - "SHMT1", - "SLBP", - "SMC1A", - "SMC3", - "SMC4", - "SMC6", - "SNRPB", - "SPAG5", - "SPC24", - "SPC25", - "SRSF1", - "SRSF2", - "SSRP1", - "STAG1", - "STMN1", - "SUV39H1", - "SYNCRIP", - "TACC3", - "TBRG4", - "TCF19", - "TFRC", - "TIMELESS", - "TIPIN", - "TK1", - "TMPO", - "TOP2A", - "TP53", - "TRA2B", - "TRIP13", - "TUBB", - "TUBG1", - "UBE2S", - "UBE2T", - "UBR7", - "UNG", - "USP1", - "WDR90", - "WEE1", - "XPO1", - "XRCC6", - "ZW10" - ], - "module1_hallmarks__HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION": [ - "ABI3BP", - "ACTA2", - "ADAM12", - "ANPEP", - "APLP1", - "AREG", - "BASP1", - "BDNF", - "BGN", - "BMP1", - "CADM1", - "CALD1", - "CALU", - "CAP2", - "CAPG", - "CCN1", - "CCN2", - "CD44", - "CD59", - "CDH11", - "CDH2", - "CDH6", - "COL11A1", - "COL12A1", - "COL16A1", - "COL1A1", - "COL1A2", - "COL3A1", - "COL4A1", - "COL4A2", - "COL5A1", - "COL5A2", - "COL5A3", - "COL6A2", - "COL6A3", - "COL7A1", - "COL8A2", - "COLGALT1", - "COMP", - "COPA", - "CRLF1", - "CTHRC1", - "CXCL1", - "CXCL12", - "CXCL6", - "CXCL8", - "DAB2", - "DCN", - "DKK1", - "DPYSL3", - "DST", - "ECM1", - "ECM2", - "EDIL3", - "EFEMP2", - "ELN", - "EMP3", - "ENO2", - "FAP", - "FAS", - "FBLN1", - "FBLN2", - "FBLN5", - "FBN1", - "FBN2", - "FERMT2", - "FGF2", - "FLNA", - "FMOD", - "FN1", - "FOXC2", - "FSTL1", - "FSTL3", - "FUCA1", - "FZD8", - "GADD45A", - "GADD45B", - "GAS1", - "GEM", - "GJA1", - "GLIPR1", - "GPC1", - "GPX7", - "GREM1", - "HTRA1", - "ID2", - "IGFBP2", - "IGFBP3", - "IGFBP4", - "IL15", - "IL32", - "IL6", - "INHBA", - "ITGA2", - "ITGA5", - "ITGAV", - "ITGB1", - "ITGB3", - "ITGB5", - "JUN", - "LAMA1", - "LAMA2", - "LAMA3", - "LAMC1", - "LAMC2", - "LGALS1", - "LOX", - "LOXL1", - "LOXL2", - "LRP1", - "LRRC15", - "LUM", - "MAGEE1", - "MATN2", - "MATN3", - "MCM7", - "MEST", - "MFAP5", - "MGP", - "MMP1", - "MMP14", - "MMP2", - "MMP3", - "MSX1", - "MXRA5", - "MYL9", - "MYLK", - "NID2", - "NNMT", - "NOTCH2", - "NT5E", - "NTM", - "OXTR", - "P3H1", - "PCOLCE", - "PCOLCE2", - "PDGFRB", - "PDLIM4", - "PFN2", - "PLAUR", - "PLOD1", - "PLOD2", - "PLOD3", - "PMEPA1", - "PMP22", - "POSTN", - "PPIB", - "PRRX1", - "PRSS2", - "PTHLH", - "PTX3", - "PVR", - "QSOX1", - "RGS4", - "RHOB", - "SAT1", - "SCG2", - "SDC1", - "SDC4", - "SERPINE1", - "SERPINE2", - "SERPINH1", - "SFRP1", - "SFRP4", - "SGCB", - "SGCD", - "SGCG", - "SLC6A8", - "SLIT2", - "SLIT3", - "SNAI2", - "SNTB1", - "SPARC", - "SPOCK1", - "SPP1", - "TAGLN", - "TFPI2", - "TGFB1", - "TGFBI", - "TGFBR3", - "TGM2", - "THBS1", - "THBS2", - "THY1", - "TIMP1", - "TIMP3", - "TNC", - "TNFAIP3", - "TNFRSF11B", - "TNFRSF12A", - "TPM1", - "TPM2", - "TPM4", - "VCAM1", - "VCAN", - "VEGFA", - "VEGFC", - "VIM", - "WIPF1", - "WNT5A" - ], - "module1_hallmarks__HALLMARK_ESTROGEN_RESPONSE_EARLY": [ - "ABAT", - "ABCA3", - "ABHD2", - "ABLIM1", - "ADCY1", - "ADCY9", - "ADD3", - "AFF1", - "AKAP1", - "ALDH3B1", - "AMFR", - "ANXA9", - "AQP3", - "AR", - "AREG", - "ARL3", - "ASB13", - "B4GALT1", - "BAG1", - "BCL11B", - "BCL2", - "BHLHE40", - "BLVRB", - "CA12", - "CALB2", - "CALCR", - "CANT1", - "CBFA2T3", - "CCN5", - "CCND1", - "CD44", - "CELSR1", - "CELSR2", - "CHPT1", - "CISH", - "CLDN7", - "CLIC3", - "CXCL12", - "CYP26B1", - "DEPTOR", - "DHCR7", - "DHRS2", - "DHRS3", - "DLC1", - "DYNLT3", - "EEIG1", - "EGR3", - "ELF1", - "ELF3", - "ELOVL2", - "ELOVL5", - "ENDOD1", - "ESRP2", - "FARP1", - "FASN", - "FCMR", - "FDFT1", - "FHL2", - "FKBP4", - "FKBP5", - "FLNB", - "FOS", - "FOXC1", - "FRK", - "GAB2", - "GFRA1", - "GJA1", - "GLA", - "GREB1", - "HES1", - "HR", - "HSPB8", - "IGF1R", - "IGFBP4", - "IL17RB", - "IL6ST", - "INHBB", - "INPP5F", - "ISG20L2", - "ITPK1", - "JAK2", - "KAZN", - "KCNK15", - "KCNK5", - "KDM4B", - "KLF10", - "KLF4", - "KLK10", - "KRT13", - "KRT15", - "KRT18", - "KRT19", - "KRT8", - "LAD1", - "LRIG1", - "MAPT", - "MAST4", - "MED13L", - "MED24", - "MICB", - "MINDY1", - "MLPH", - "MPPED2", - "MREG", - "MSMB", - "MUC1", - "MYB", - "MYBBP1A", - "MYBL1", - "MYC", - "MYOF", - "NADSYN1", - "NAV2", - "NBL1", - "NCOR2", - "NHERF1", - "NPY1R", - "NRIP1", - "NXT1", - "OLFM1", - "OLFML3", - "OPN3", - "OVOL2", - "P2RY2", - "PAPSS2", - "PDLIM3", - "PDZK1", - "PEX11A", - "PGR", - "PLAAT3", - "PMAIP1", - "PODXL", - "PPIF", - "PRSS23", - "PTGES", - "RAB17", - "RAB31", - "RAPGEFL1", - "RARA", - "RASGRP1", - "RBBP8", - "REEP1", - "RET", - "RETREG1", - "RHOBTB3", - "RHOD", - "RPS6KA2", - "RRP12", - "SCARB1", - "SCNN1A", - "SEC14L2", - "SEMA3B", - "SFN", - "SH3BP5", - "SIAH2", - "SLC16A1", - "SLC19A2", - "SLC1A1", - "SLC1A4", - "SLC22A5", - "SLC24A3", - "SLC26A2", - "SLC27A2", - "SLC2A1", - "SLC37A1", - "SLC39A6", - "SLC7A2", - "SLC7A5", - "SNX24", - "SOX3", - "STC2", - "SULT2B1", - "SVIL", - "SYBU", - "SYNGR1", - "SYT12", - "TBC1D30", - "TFAP2C", - "TFF1", - "TFF3", - "TGIF2", - "TGM2", - "THSD4", - "TIAM1", - "TIPARP", - "TJP3", - "TMEM164", - "TMPRSS3", - "TOB1", - "TPBG", - "TPD52L1", - "TSKU", - "TTC39A", - "TUBB2B", - "UGCG", - "UNC119", - "WFS1", - "WWC1", - "XBP1", - "ZNF185" - ], - "module1_hallmarks__HALLMARK_ESTROGEN_RESPONSE_LATE": [ - "ABCA3", - "ABHD2", - "ACOX2", - "ADD3", - "AFF1", - "AGR2", - "ALDH3A2", - "ALDH3B1", - "AMFR", - "ANXA9", - "AREG", - "ARL3", - "ASCL1", - "ASS1", - "ATP2B4", - "BAG1", - "BATF", - "BCL2", - "BLVRB", - "BTG3", - "CA12", - "CA2", - "CACNA2D2", - "CALCR", - "CAV1", - "CCN5", - "CCNA1", - "CCND1", - "CD44", - "CD9", - "CDC20", - "CDC6", - "CDH1", - "CELSR2", - "CHPT1", - "CHST8", - "CISH", - "CKB", - "CLIC3", - "COX6C", - "CPE", - "CXCL12", - "CXCL14", - "CYP26B1", - "CYP4F11", - "DCXR", - "DHCR7", - "DHRS2", - "DLG5", - "DNAJC1", - "DNAJC12", - "DUSP2", - "DYNLT3", - "EEIG1", - "EGR3", - "ELOVL5", - "EMP2", - "ETFB", - "FABP5", - "FARP1", - "FDFT1", - "FGFR3", - "FKBP4", - "FKBP5", - "FLNB", - "FOS", - "FOXC1", - "FRK", - "GAL", - "GALE", - "GFUS", - "GINS2", - "GJB3", - "GLA", - "GPER1", - "HMGCS2", - "HOMER2", - "HPRT1", - "HR", - "HSPA4L", - "HSPB8", - "ID2", - "IDH2", - "IGFBP4", - "IGSF1", - "IL17RB", - "IL6ST", - "IMPA2", - "ISG20", - "ITPK1", - "JAK1", - "JAK2", - "KCNK5", - "KIF20A", - "KLF4", - "KLK10", - "KLK11", - "KRT13", - "KRT19", - "LAMC2", - "LARGE1", - "LLGL2", - "LSR", - "LTF", - "MAPK13", - "MAPT", - "MDK", - "MEST", - "METTL3", - "MICB", - "MOCS2", - "MYB", - "MYOF", - "NAB2", - "NBL1", - "NCOR2", - "NHERF1", - "NMU", - "NPY1R", - "NRIP1", - "NXT1", - "OLFM1", - "OPN3", - "OVOL2", - "PAPSS2", - "PCP4", - "PDCD4", - "PDLIM3", - "PDZK1", - "PERP", - "PGR", - "PKP3", - "PLAAT3", - "PLAC1", - "PLK4", - "PLXNB1", - "PPIF", - "PRKAR2B", - "PRLR", - "PRSS23", - "PTGER3", - "PTGES", - "PTPN6", - "RAB31", - "RABEP1", - "RAPGEFL1", - "RBBP8", - "RET", - "RNASEH2A", - "RPS6KA2", - "S100A9", - "SCARB1", - "SCNN1A", - "SCUBE2", - "SEMA3B", - "SERPINA1", - "SERPINA3", - "SERPINA5", - "SFN", - "SGK1", - "SIAH2", - "SLC16A1", - "SLC1A4", - "SLC22A5", - "SLC24A3", - "SLC26A2", - "SLC27A2", - "SLC29A1", - "SLC2A8", - "SLC7A5", - "SNX10", - "SORD", - "SOX3", - "ST14", - "ST6GALNAC2", - "STIL", - "SULT2B1", - "TFAP2C", - "TFF1", - "TFF3", - "TFPI2", - "TH", - "TIAM1", - "TJP3", - "TMPRSS3", - "TNNC1", - "TOB1", - "TOP2A", - "TPBG", - "TPD52L1", - "TPSAB1", - "TRIM29", - "TSPAN13", - "TST", - "UGDH", - "UNC13B", - "WFS1", - "XBP1", - "XRCC3", - "ZFP36" - ], - "module1_hallmarks__HALLMARK_FATTY_ACID_METABOLISM": [ - "AADAT", - "ACAA1", - "ACAA2", - "ACADL", - "ACADM", - "ACADS", - "ACADVL", - "ACAT2", - "ACO2", - "ACOT2", - "ACOT8", - "ACOX1", - "ACSL1", - 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"INMT", - "KMT5A", - "LDHA", - "LGALS1", - "LTC4S", - "MAOA", - "MCEE", - "MDH1", - "MDH2", - "ME1", - "METAP1", - "MGLL", - "MIF", - "MIX23", - "MLYCD", - "NBN", - "NCAPH2", - "NSDHL", - "NTHL1", - "ODC1", - "OSTC", - "PCBD1", - "PDHA1", - "PDHB", - "PPARA", - "PRDX6", - "PSME1", - "PTPRG", - "PTS", - "RAP1GDS1", - "RDH11", - "RDH16", - "REEP6", - "RETSAT", - "S100A10", - "SDHA", - "SDHC", - "SDHD", - "SERINC1", - "SLC22A5", - "SMS", - "SUCLA2", - "SUCLG1", - "SUCLG2", - "TDO2", - "TP53INP2", - "UBE2L6", - "UGDH", - "UROD", - "UROS", - "VNN1", - "XIST", - "YWHAH" - ], - "module1_hallmarks__HALLMARK_G2M_CHECKPOINT": [ - "ABL1", - "AMD1", - "ARID4A", - "ATF5", - "ATRX", - "AURKA", - "AURKB", - "BARD1", - "BCL3", - "BIRC5", - "BRCA2", - "BUB1", - "BUB3", - "CASP8AP2", - "CBX1", - "CCNA2", - "CCNB2", - "CCND1", - "CCNF", - "CCNT1", - "CDC20", - "CDC25A", - "CDC25B", - "CDC27", - "CDC45", - "CDC6", - "CDC7", - "CDK1", - "CDK4", - "CDKN1B", - "CDKN2C", - "CDKN3", - "CENPA", - "CENPE", - "CENPF", - 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"ODF2", - "ORC5", - "ORC6", - "PAFAH1B1", - "PBK", - "PDS5B", - "PLK1", - "PLK4", - "PML", - "POLA2", - "POLE", - "POLQ", - "PRC1", - "PRIM2", - "PRMT5", - "PRP4K", - "PTTG1", - "PTTG3P", - "PURA", - "RACGAP1", - "RAD21", - "RAD23B", - "RAD54L", - "RASAL2", - "RBL1", - "RBM14", - "RPA2", - "RPS6KA5", - "SAP30", - "SFPQ", - "SLC12A2", - "SLC38A1", - "SLC7A1", - "SLC7A5", - "SMAD3", - "SMARCC1", - "SMC1A", - "SMC2", - "SMC4", - "SNRPD1", - "SQLE", - "SRSF1", - "SRSF10", - "SRSF2", - "SS18", - "STAG1", - "STIL", - "STMN1", - "SUV39H1", - "SYNCRIP", - "TACC3", - "TENT4A", - "TFDP1", - "TGFB1", - "TLE3", - "TMPO", - "TNPO2", - "TOP1", - "TOP2A", - "TPX2", - "TRA2B", - "TRAIP", - "TROAP", - "TTK", - "UBE2C", - "UBE2S", - "UCK2", - "UPF1", - "WRN", - "XPO1", - "YTHDC1" - ], - "module1_hallmarks__HALLMARK_GLYCOLYSIS": [ - "ABCB6", - "ADORA2B", - "AGL", - "AGRN", - "AK3", - "AK4", - "AKR1A1", - "ALDH7A1", - "ALDH9A1", - "ALDOA", - "ALDOB", - "ALG1", - "ANG", - "ANGPTL4", - "ANKZF1", - "ARPP19", - "ARTN", - "AURKA", - "B3GALT6", - "B3GAT1", - "B3GAT3", - "B3GNT3", - "B4GALT1", - "B4GALT2", - "B4GALT4", - "B4GALT7", - "BIK", - "BPNT1", - "CACNA1H", - "CAPN5", - "CASP6", - "CD44", - "CDK1", - "CENPA", - "CHPF", - "CHPF2", - "CHST1", - "CHST12", - "CHST2", - "CHST4", - "CHST6", - "CITED2", - "CLDN3", - "CLDN9", - "CLN6", - "COG2", - "COL5A1", - "COPB2", - "CTH", - "CXCR4", - "CYB5A", - "DCN", - "DDIT4", - "DEPDC1", - "DLD", - "DPYSL4", - "DSC2", - "ECD", - "EFNA3", - "EGFR", - "EGLN3", - "ELF3", - "ENO1", - "ENO2", - "ERO1A", - "EXT1", - "EXT2", - "FAM162A", - "FBP2", - "FKBP4", - "FUT8", - "G6PD", - "GAL3ST1", - "GALE", - "GALK1", - "GALK2", - "GAPDHS", - "GCLC", - "GFPT1", - "GFUS", - "GLCE", - "GLRX", - "GMPPA", - "GMPPB", - "GNE", - "GNPDA1", - "GOT1", - "GOT2", - "GPC1", - "GPC3", - "GPC4", - "GPR87", - "GUSB", - "GYS1", - "GYS2", - "HAX1", - "HDLBP", - "HK2", - "HMMR", - "HOMER1", - "HS2ST1", - "HS6ST2", - "HSPA5", - "IDH1", - "IDUA", - "IER3", - "IGFBP3", - "IL13RA1", - "IRS2", - 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"ACHE", - "ADGRG1", - "AMOT", - "CDK5R1", - "CDK6", - "CELSR1", - "CNTFR", - "CRMP1", - "DPYSL2", - "ETS2", - "GLI1", - "HEY1", - "HEY2", - "L1CAM", - "LDB1", - "MYH9", - "NF1", - "NKX6-1", - "NRCAM", - "NRP1", - "NRP2", - "OPHN1", - "PLG", - "PML", - "PTCH1", - "RASA1", - "RTN1", - "SCG2", - "SHH", - "SLIT1", - "THY1", - "TLE1", - "TLE3", - "UNC5C", - "VEGFA", - "VLDLR" - ], - "module1_hallmarks__HALLMARK_HEME_METABOLISM": [ - "ABCB6", - "ABCG2", - "ACKR1", - "ACP5", - "ACSL6", - "ADD1", - "ADD2", - "ADIPOR1", - "AGPAT4", - "AHSP", - "ALAD", - "ALAS2", - "ALDH1L1", - "ALDH6A1", - "ANK1", - "AQP3", - "ARHGEF12", - "ARL2BP", - "ASNS", - "ATG4A", - "ATP6V0A1", - "BACH1", - "BCAM", - "BLVRA", - "BLVRB", - "BMP2K", - "BNIP3L", - "BPGM", - "BSG", - "BTG2", - "BTRC", - "C3", - "CA1", - "CA2", - "CAST", - "CAT", - "CCDC28A", - "CCND3", - "CDC27", - "CDR2", - "CIR1", - "CLCN3", - "CLIC2", - "CPOX", - "CROCCP2", - "CTNS", - "CTSB", - "CTSE", - "DAAM1", - "DCAF10", - "DCAF11", - "DCUN1D1", - "DMTN", - "E2F2", - "EIF2AK1", - "ELL2", - "ENDOD1", - "EPB41", - "EPB42", - "EPOR", - "ERMAP", - "EZH1", - "FBXO34", - "FBXO7", - "FBXO9", - "FECH", - "FN3K", - "FOXJ2", - "FOXO3", - "FTCD", - "GAPVD1", - "GATA1", - "GCLC", - "GCLM", - "GDE1", - "GLRX5", - "GMPS", - "GYPA", - "GYPB", - "GYPC", - "GYPE", - "H1-0", - "H4C3", - "HAGH", - "HBB", - "HBBP1", - "HBD", - "HBQ1", - "HBZ", - "HDGF", - "HEBP1", - "HMBS", - "HTATIP2", - "HTRA2", - "ICAM4", - "IGSF3", - "ISCA1", - "KAT2B", - "KDM7A", - "KEL", - "KHNYN", - "KLF1", - "KLF3", - "LAMP2", - "LMO2", - "LPIN2", - "LRP10", - "MAP2K3", - "MARCHF2", - "MARCHF8", - "MARK3", - "MBOAT2", - "MFHAS1", - "MGST3", - "MINPP1", - "MKRN1", - "MOCOS", - "MOSPD1", - "MPP1", - "MXI1", - "MYL4", - "NARF", - "NCOA4", - "NEK7", - "NFE2", - "NFE2L1", - "NNT", - "NR3C1", - "NUDT4", - "OPTN", - "OSBP2", - "P4HA2", - "PC", - "PDZK1IP1", - "PGLS", - "PICALM", - "PIGQ", - "PPOX", - "PPP2R5B", - "PRDX2", - "PSMD9", - "RAD23A", - "RANBP10", - "RAP1GAP", - "RBM38", - "RBM5", - "RCL1", - "RHAG", - "RHCE", - "RHD", - "RIOK3", - "RNF123", - "RNF19A", - "SDCBP", - "SEC14L1", - "SELENBP1", - "SIDT2", - "SLC10A3", - "SLC11A2", - "SLC22A4", - "SLC25A37", - "SLC25A38", - "SLC2A1", - "SLC30A1", - "SLC30A10", - "SLC4A1", - "SLC66A2", - "SLC6A8", - "SLC6A9", - "SLC7A11", - "SMOX", - "SNCA", - "SPTA1", - "SPTB", - "SYNJ1", - "TAL1", - "TCEA1", - "TENT5C", - "TFDP2", - "TFRC", - "TMCC2", - "TMEM9B", - "TNRC6B", - "TNS1", - "TOP1", - "TRAK2", - "TRIM10", - "TRIM58", - "TSPAN5", - "TSPO2", - "TYR", - "UBAC1", - "UCP2", - "UROD", - "UROS", - "USP15", - "VEZF1", - "XK", - "XPO7", - "YPEL5" - ], - "module1_hallmarks__HALLMARK_HYPOXIA": [ - "ACKR3", - "ADM", - "ADORA2B", - "AK4", - "AKAP12", - "ALDOA", - "ALDOB", - "ALDOC", - "AMPD3", - "ANGPTL4", - "ANKZF1", - "ANXA2", - "ATF3", - "ATP7A", - "B3GALT6", - "B4GALNT2", - "BCAN", - "BCL2", - "BGN", - "BHLHE40", - "BNIP3L", - "BRS3", - "BTG1", - "CA12", - "CASP6", - "CAV1", - "CAVIN1", - "CAVIN3", - "CCN1", - "CCN2", - "CCN5", - "CCNG2", - "CDKN1A", - "CDKN1B", - "CDKN1C", - "CHST2", - "CHST3", - "CITED2", - "COL5A1", - "CP", - "CSRP2", - "CXCR4", - "DCN", - "DDIT3", - "DDIT4", - "DPYSL4", - "DTNA", - "DUSP1", - "EDN2", - "EFNA1", - "EFNA3", - "EGFR", - "ENO1", - "ENO2", - "ENO3", - "ERO1A", - "ERRFI1", - "ETS1", - "EXT1", - "F3", - "FAM162A", - "FBP1", - "FOS", - "FOSL2", - "FOXO3", - "GAA", - "GALK1", - "GAPDH", - "GAPDHS", - "GBE1", - "GCK", - "GCNT2", - "GLRX", - "GPC1", - "GPC3", - "GPC4", - "GPI", - "GRHPR", - "GYS1", - "HAS1", - "HDLBP", - "HEXA", - "HK1", - "HK2", - "HMOX1", - "HOXB9", - "HS3ST1", - "HSPA5", - "IDS", - "IER3", - "IGFBP1", - "IGFBP3", - "IL6", - "ILVBL", - "INHA", - "IRS2", - "ISG20", - "JMJD6", - "JUN", - "KDELR3", - "KDM3A", - "KIF5A", - "KLF6", - "KLF7", - "KLHL24", - "LALBA", - "LARGE1", - "LDHA", - "LDHC", - "LOX", - "LXN", - "MAFF", - "MAP3K1", - "MIF", - "MT1E", - "MT2A", - "MXI1", - "MYH9", - "NAGK", - "NCAN", - "NDRG1", - "NDST1", - "NDST2", - "NEDD4L", - "NFIL3", - "NOCT", - "NR3C1", - "P4HA1", - "P4HA2", - "PAM", - "PCK1", - "PDGFB", - "PDK1", - "PDK3", - "PFKFB3", - "PFKL", - "PFKP", - "PGAM2", - "PGF", - "PGK1", - "PGM1", - "PGM2", - "PHKG1", - "PIM1", - "PKLR", - "PKP1", - "PLAC8", - "PLAUR", - "PLIN2", - "PNRC1", - "PPARGC1A", - "PPFIA4", - "PPP1R15A", - "PPP1R3C", - "PRDX5", - "PRKCA", - "PYGM", - "RBPJ", - "RORA", - "RRAGD", - "S100A4", - "SAP30", - "SCARB1", - "SDC2", - "SDC3", - "SDC4", - "SELENBP1", - "SERPINE1", - "SIAH2", - "SLC25A1", - "SLC2A1", - "SLC2A3", - "SLC2A5", - "SLC37A4", - "SLC6A6", - "SRPX", - "STBD1", - "STC1", - "STC2", - "SULT2B1", - "TES", - "TGFB3", - "TGFBI", - "TGM2", - "TIPARP", - "TKTL1", - "TMEM45A", - "TNFAIP3", - "TPBG", - "TPD52", - "TPI1", - "TPST2", - "UGP2", - "VEGFA", - "VHL", - "VLDLR", - "WSB1", - "XPNPEP1", - "ZFP36", - "ZNF292" - ], - "module1_hallmarks__HALLMARK_IL2_STAT5_SIGNALING": [ - "ABCB1", - "ADAM19", - "AGER", - "AHCY", - "AHNAK", - "AHR", - "ALCAM", - "AMACR", - "ANXA4", - "APLP1", - "ARL4A", - "BATF", - "BATF3", - "BCL2", - "BCL2L1", - "BHLHE40", - "BMP2", - "BMPR2", - "CA2", - "CAPG", - "CAPN3", - "CASP3", - "CCND2", - "CCND3", - "CCNE1", - "CCR4", - "CD44", - "CD48", - "CD79B", - "CD81", - "CD83", - "CD86", - "CDC42SE2", - "CDC6", - "CDCP1", - "CDKN1C", - "CISH", - "CKAP4", - "COCH", - "COL6A1", - "CSF1", - "CSF2", - "CST7", - "CTLA4", - "CTSZ", - "CXCL10", - "CYFIP1", - "DCPS", - "DENND5A", - "DHRS3", - "DRC1", - "ECM1", - "EEF1AKMT1", - "EMP1", - "ENO3", - "ENPP1", - "EOMES", - "ETFBKMT", - "ETV4", - "F2RL2", - "FAH", - "FGL2", - "FLT3LG", - "FURIN", - "GABARAPL1", - "GADD45B", - "GALM", - "GATA1", - "GBP4", - "GLIPR2", - "GPR65", - "GPR83", - "GPX4", - "GSTO1", - "GUCY1B1", - "HIPK2", - "HK2", - "HOPX", - "HUWE1", - "HYCC2", - "ICOS", - "IFITM3", - "IFNGR1", - "IGF1R", - "IGF2R", - "IKZF2", - "IKZF4", - "IL10", - "IL10RA", - "IL13", - "IL18R1", - "IL1R2", - "IL1RL1", - "IL2RA", - "IL2RB", - "IL3RA", - "IL4R", - "IRF4", - "IRF6", - "IRF8", - "ITGA6", - "ITGAE", - "ITGAV", - "ITIH5", - "KLF6", - "LCLAT1", - "LIF", - "LRIG1", - "LRRC8C", - "LTB", - "MAFF", - "MAP3K8", - "MAP6", - "MAPKAPK2", - "MUC1", - "MXD1", - "MYC", - "MYO1C", - "MYO1E", - "NCOA3", - "NCS1", - "NDRG1", - "NFIL3", - "NFKBIZ", - "NOP2", - "NRP1", - "NT5E", - "ODC1", - "P2RX4", - "P4HA1", - "PDCD2L", - "PENK", - "PHLDA1", - "PHTF2", - "PIM1", - "PLAGL1", - "PLEC", - "PLIN2", - "PLPP1", - "PLSCR1", - "PNP", - "POU2F1", - "PRAF2", - "PRKCH", - "PRNP", - "PTCH1", - "PTGER2", - "PTH1R", - "PTRH2", - "PUS1", - "RABGAP1L", - "RGS16", - "RHOB", - "RHOH", - "RNH1", - "RORA", - "RRAGD", - "S100A1", - "SCN9A", - "SELL", - "SELP", - "SERPINB6", - "SERPINC1", - "SH3BGRL2", - "SHE", - "SLC1A5", - "SLC29A2", - "SLC2A3", - "SLC39A8", - "SMPDL3A", - "SNX14", - "SNX9", - "SOCS1", - "SOCS2", - "SPP1", - "SPRED2", - "SPRY4", - "ST3GAL4", - "SWAP70", - "SYNGR2", - "SYT11", - "TGM2", - "TIAM1", - "TLR7", - "TNFRSF18", - "TNFRSF1B", - "TNFRSF21", - "TNFRSF4", - "TNFRSF8", - "TNFRSF9", - "TNFSF10", - "TNFSF11", - "TRAF1", - "TTC39B", - "TWSG1", - "UCK2", - "UMPS", - "WLS", - "XBP1" - ], - "module1_hallmarks__HALLMARK_IL6_JAK_STAT3_SIGNALING": [ - "A2M", - "ACVR1B", - "ACVRL1", - "BAK1", - "CBL", - "CCL7", - "CCR1", - "CD14", - "CD36", - "CD38", - "CD44", - "CD9", - "CNTFR", - "CRLF2", - "CSF1", - "CSF2", - "CSF2RA", - "CSF2RB", - "CSF3R", - "CXCL1", - "CXCL10", - "CXCL11", - "CXCL13", - "CXCL3", - "CXCL9", - "DNTT", - "EBI3", - "FAS", - "GRB2", - "HAX1", - "HMOX1", - "IFNAR1", - "IFNGR1", - "IFNGR2", - "IL10RB", - "IL12RB1", - "IL13RA1", - "IL15RA", - "IL17RA", - "IL17RB", - "IL18R1", - "IL1B", - "IL1R1", - "IL1R2", - "IL2RA", - "IL2RG", - "IL3RA", - "IL4R", - "IL6", - "IL6ST", - "IL7", - "IL9R", - "INHBE", - "IRF1", - "IRF9", - "ITGA4", - "ITGB3", - "JUN", - "LEPR", - "LTB", - "LTBR", - "MAP3K8", - "MYD88", - "OSMR", - "PDGFC", - "PF4", - "PIK3R5", - "PIM1", - "PLA2G2A", - "PTPN1", - "PTPN11", - "PTPN2", - "REG1A", - "SOCS1", - "SOCS3", - "STAM2", - "STAT1", - "STAT2", - "STAT3", - "TGFB1", - "TLR2", - "TNF", - "TNFRSF12A", - "TNFRSF1A", - "TNFRSF1B", - "TNFRSF21", - "TYK2" - ], - "module1_hallmarks__HALLMARK_INFLAMMATORY_RESPONSE": [ - "ABCA1", - "ABI1", - "ACVR1B", - "ACVR2A", - "ADGRE1", - "ADM", - "ADORA2B", - "ADRM1", - "AHR", - "APLNR", - "AQP9", - "ATP2A2", - "ATP2B1", - "ATP2C1", - "AXL", - "BDKRB1", - "BEST1", - "BST2", - "BTG2", - "C3AR1", - "C5AR1", - "CALCRL", - "CCL17", - "CCL2", - "CCL20", - "CCL22", - "CCL24", - "CCL5", - "CCL7", - "CCR7", - "CCRL2", - "CD14", - "CD40", - "CD48", - "CD55", - "CD69", - "CD70", - "CD82", - "CDKN1A", - "CHST2", - "CLEC5A", - "CMKLR1", - "CSF1", - "CSF3", - "CSF3R", - "CX3CL1", - "CXCL10", - "CXCL11", - "CXCL6", - "CXCL8", - "CXCL9", - "CXCR6", - "CYBB", - "DCBLD2", - "EBI3", - "EDN1", - "EIF2AK2", - "EMP3", - "EREG", - "F3", - "FFAR2", - "FPR1", - "FZD5", - "GABBR1", - "GCH1", - "GNA15", - "GNAI3", - "GP1BA", - "GPC3", - "GPR132", - "GPR183", - "HAS2", - "HBEGF", - "HIF1A", - "HPN", - "HRH1", - "ICAM1", - "ICAM4", - "ICOSLG", - "IFITM1", - "IFNAR1", - "IFNGR2", - "IL10", - "IL10RA", - "IL12B", - "IL15", - "IL15RA", - "IL18", - "IL18R1", - "IL18RAP", - "IL1A", - "IL1B", - "IL1R1", - "IL2RB", - "IL4R", - "IL6", - "IL7R", - "INHBA", - "IRAK2", - "IRF1", - "IRF7", - "ITGA5", - "ITGB3", - "ITGB8", - "KCNA3", - "KCNJ2", - "KCNMB2", - "KIF1B", - "KLF6", - "LAMP3", - "LCK", - "LCP2", - "LDLR", - "LIF", - "LPAR1", - "LTA", - "LY6E", - "LYN", - "MARCO", - "MEFV", - "MEP1A", - "MET", - "MMP14", - "MSR1", - "MXD1", - "MYC", - "NAMPT", - "NDP", - "NFKB1", - "NFKBIA", - "NLRP3", - "NMI", - "NMUR1", - "NOD2", - "NPFFR2", - "OLR1", - "OPRK1", - "OSM", - "OSMR", - "P2RX4", - "P2RX7", - "P2RY2", - "PCDH7", - "PDE4B", - "PDPN", - "PIK3R5", - "PLAUR", - "PROK2", - "PSEN1", - "PTAFR", - "PTGER2", - "PTGER4", - "PTGIR", - "PTPRE", - "PVR", - "RAF1", - "RASGRP1", - "RELA", - "RGS1", - "RGS16", - "RHOG", - "RIPK2", - "RNF144B", - "ROS1", - "RTP4", - "SCARF1", - "SCN1B", - "SELE", - "SELENOS", - "SELL", - "SEMA4D", - "SERPINE1", - "SGMS2", - "SLAMF1", - "SLC11A2", - "SLC1A2", - "SLC28A2", - "SLC31A1", - "SLC31A2", - "SLC4A4", - "SLC7A1", - "SLC7A2", - "SPHK1", - "SRI", - "STAB1", - "TACR1", - "TACR3", - "TAPBP", - "TIMP1", - "TLR1", - "TLR2", - "TLR3", - "TNFAIP6", - "TNFRSF1B", - "TNFRSF9", - "TNFSF10", - "TNFSF15", - "TNFSF9", - "TPBG", - "VIP" - ], - "module1_hallmarks__HALLMARK_INTERFERON_ALPHA_RESPONSE": [ - "ADAR", - "B2M", - "BATF2", - "BST2", - "C1S", - "CASP1", - "CASP8", - "CCRL2", - "CD47", - "CD74", - "CMPK2", - "CMTR1", - "CNP", - "CSF1", - "CXCL10", - "CXCL11", - "DDX60", - "DHX58", - "EIF2AK2", - "ELF1", - "EPSTI1", - "GBP2", - "GBP4", - "GMPR", - "HELZ2", - "HERC6", - "HLA-C", - "IFI27", - "IFI30", - "IFI35", - "IFI44", - "IFI44L", - "IFIH1", - "IFIT2", - "IFIT3", - "IFITM1", - "IFITM2", - "IFITM3", - "IL15", - "IL4R", - "IL7", - "IRF1", - "IRF2", - "IRF7", - "IRF9", - "ISG15", - "ISG20", - "LAMP3", - "LAP3", - "LGALS3BP", - "LPAR6", - "LY6E", - "MOV10", - "MVB12A", - "MX1", - "NCOA7", - "NMI", - "NUB1", - "OAS1", - "OASL", - "OGFR", - "PARP12", - "PARP14", - "PARP9", - "PLSCR1", - "PNPT1", - "PROCR", - "PSMA3", - "PSMB8", - "PSMB9", - "PSME1", - "PSME2", - "RIPK2", - "RNF31", - "RSAD2", - "RTP4", - "SAMD9", - "SAMD9L", - "SELL", - "SLC25A28", - "SP110", - "STAT2", - "TAP1", - "TDRD7", - "TENT5A", - "TMEM140", - "TRAFD1", - "TRIM14", - "TRIM21", - "TRIM25", - "TRIM26", - "TRIM5", - "TXNIP", - "UBA7", - "UBE2L6", - "USP18", - "WARS1" - ], - "module1_hallmarks__HALLMARK_INTERFERON_GAMMA_RESPONSE": [ - "ADAR", - "APOL6", - "ARID5B", - "ARL4A", - "AUTS2", - "B2M", - "BANK1", - "BATF2", - "BPGM", - "BST2", - "BTG1", - "C1R", - "C1S", - "CASP1", - "CASP3", - "CASP4", - "CASP7", - "CASP8", - "CCL2", - "CCL5", - "CCL7", - "CD274", - "CD38", - "CD40", - "CD69", - "CD74", - "CD86", - "CDKN1A", - "CFB", - "CFH", - "CIITA", - "CMKLR1", - "CMPK2", - "CMTR1", - "CSF2RB", - "CXCL10", - "CXCL11", - "CXCL9", - "DDX60", - "DHX58", - "EIF2AK2", - "EIF4E3", - "EPSTI1", - "FAS", - "FCGR1A", - "FGL2", - "FPR1", - "GBP4", - "GBP6", - "GCH1", - "GPR18", - "GZMA", - "HELZ2", - "HERC6", - "HIF1A", - "HLA-A", - "HLA-B", - "HLA-DMA", - "HLA-DQA1", - "HLA-DRB1", - "HLA-G", - "ICAM1", - "IDO1", - "IFI27", - "IFI30", - "IFI35", - "IFI44", - "IFI44L", - "IFIH1", - "IFIT1", - "IFIT2", - "IFIT3", - "IFITM2", - "IFITM3", - "IFNAR2", - "IL10RA", - "IL15", - "IL15RA", - "IL18BP", - "IL2RB", - "IL4R", - "IL6", - "IL7", - "IRF1", - "IRF2", - "IRF4", - "IRF5", - "IRF7", - "IRF8", - "IRF9", - "ISG15", - "ISG20", - "ISOC1", - "ITGB7", - "JAK2", - "KLRK1", - "LAP3", - "LATS2", - "LCP2", - "LGALS3BP", - "LY6E", - "LYSMD2", - "MARCHF1", - "MT2A", - "MTHFD2", - "MVP", - "MX1", - "MX2", - "MYD88", - "NAMPT", - "NCOA3", - "NFKB1", - "NFKBIA", - "NLRC5", - "NMI", - "NOD1", - "NUP93", - "OAS2", - "OAS3", - "OASL", - "OGFR", - "P2RY14", - "PARP12", - "PARP14", - "PDE4B", - "PELI1", - "PFKP", - "PIM1", - "PLA2G4A", - "PLSCR1", - "PML", - "PNP", - "PNPT1", - "PSMA2", - "PSMA3", - "PSMB10", - "PSMB2", - "PSMB8", - "PSMB9", - "PSME1", - "PSME2", - "PTGS2", - "PTPN1", - "PTPN2", - "PTPN6", - "RAPGEF6", - "RBCK1", - "RIGI", - "RIPK1", - "RIPK2", - "RNF213", - "RNF31", - "RSAD2", - "RTP4", - "SAMD9L", - "SAMHD1", - "SECTM1", - "SELP", - "SERPING1", - "SLAMF7", - "SLC25A28", - "SOCS1", - "SOCS3", - "SOD2", - "SP110", - "SPPL2A", - "SRI", - "SSPN", - "ST3GAL5", - "ST8SIA4", - "STAT1", - "STAT2", - "STAT3", - "STAT4", - "TAP1", - "TAPBP", - "TDRD7", - "TMT1B", - "TNFAIP2", - "TNFAIP3", - "TNFAIP6", - "TNFSF10", - "TOR1B", - "TRAFD1", - "TRIM14", - "TRIM21", - "TRIM25", - "TRIM26", - "TXNIP", - "UBE2L6", - "UPP1", - "USP18", - "VAMP5", - "VAMP8", - "VCAM1", - "WARS1", - "XAF1", - "XCL1", - "ZBP1", - "ZNFX1" - ], - "module1_hallmarks__HALLMARK_KRAS_SIGNALING_DN": [ - "ABCB11", - "ABCG4", - "ACTC1", - "ADRA2C", - "AKR1B10", - "ALOX12B", - "AMBN", - "ARHGDIG", - "ARPP21", - "ASB7", - "ATP4A", - "ATP6V1B1", - "BARD1", - "BMPR1B", - "BRDT", - "BTG2", - "C5", - "CACNA1F", - "CACNG1", - "CALCB", - "CALML5", - "CAMK1D", - "CAPN9", - "CCDC106", - "CCNA1", - "CCR8", - "CD207", - "CD40LG", - "CD80", - "CDH16", - "CDKAL1", - "CELSR2", - "CHRNG", - "CHST2", - "CKM", - "CLDN16", - "CLDN8", - "CLPS", - "CLSTN3", - "CNTFR", - "COL2A1", - "COPZ2", - "COQ8A", - "CPA2", - "CPB1", - "CPEB3", - "CYP11B2", - "CYP39A1", - "DCC", - "DLK2", - "DTNB", - "EDAR", - "EDN1", - "EDN2", - "EFHD1", - "EGF", - "ENTPD7", - "EPHA5", - "FGF16", - "FGF22", - "FGFR3", - "FGGY", - "FSHB", - "GAMT", - "GDNF", - "GP1BA", - "GP2", - "GPR19", - "GPR3", - "GPRC5C", - "GRID2", - "GTF3C5", - "HNF1A", - "HSD11B2", - "HTR1B", - "HTR1D", - "IDUA", - "IFI44L", - "IFNG", - "IGFBP2", - "IL12B", - "IL5", - "INSL5", - "IRS4", - "ITGB1BP2", - "ITIH3", - "KCND1", - "KCNE2", - "KCNMB1", - "KCNN1", - "KCNQ2", - "KLHDC8A", - "KLK7", - "KLK8", - "KMT2D", - "KRT1", - "KRT13", - "KRT15", - "KRT4", - "KRT5", - "LFNG", - "LGALS7", - "LYPD3", - "MACROH2A2", - "MAGIX", - "MAST3", - "MEFV", - "MFSD6", - "MSH5", - "MTHFR", - "MX1", - "MYH7", - "MYO15A", - "MYOT", - "NGB", - "NOS1", - "NPHS1", - "NPY4R", - "NR4A2", - 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"ADAM8", - "ADAMDEC1", - "ADGRA2", - "ADGRL4", - "AKAP12", - "AKT2", - "ALDH1A2", - "ALDH1A3", - "AMMECR1", - "ANGPTL4", - "ANKH", - "ANO1", - "ANXA10", - "APOD", - "ARG1", - "ATG10", - "AVL9", - "BIRC3", - "BMP2", - "BPGM", - "BTBD3", - "BTC", - "C3AR1", - "CA2", - "CAB39L", - "CBL", - "CBR4", - "CBX8", - "CCL20", - "CCND2", - "CCSER2", - "CD37", - "CDADC1", - "CFB", - "CFH", - "CFHR2", - "CIDEA", - "CLEC4A", - "CMKLR1", - "CPE", - "CROT", - "CSF2", - "CSF2RA", - "CTSS", - "CXCL10", - "CXCR4", - "DCBLD2", - "DNMBP", - "DOCK2", - "DUSP6", - "EMP1", - "ENG", - "EPB41L3", - "EPHB2", - "EREG", - "ERO1A", - "ETS1", - "ETV1", - "ETV4", - "ETV5", - "EVI5", - "F13A1", - "F2RL1", - "FBXO4", - "FCER1G", - "FGF9", - "FLT4", - "FUCA1", - "G0S2", - "GABRA3", - "GADD45G", - "GALNT3", - "GFPT2", - "GLRX", - "GNG11", - "GPNMB", - "GPRC5B", - "GUCY1A1", - "GYPC", - "H2BC3", - "HBEGF", - "HDAC9", - "HKDC1", - "HOXD11", - "HSD11B1", - "ID2", - "IGF2", - "IGFBP3", - "IKZF1", - "IL10RA", - "IL1B", - "IL1RL2", - 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"TRAF1", - "TRIB1", - "TRIB2", - "TSPAN1", - "TSPAN13", - "TSPAN7", - "USH1C", - "USP12", - "VWA5A", - "WDR33", - "WNT7A", - "YRDC", - "ZNF277", - "ZNF639" - ], - "module1_hallmarks__HALLMARK_MITOTIC_SPINDLE": [ - "ABI1", - "ABL1", - "ABR", - "ACTN4", - "AKAP13", - "ALMS1", - "ALS2", - "ANLN", - "APC", - "ARAP3", - "ARF6", - "ARFGEF1", - "ARFIP2", - "ARHGAP10", - "ARHGAP27", - "ARHGAP29", - "ARHGAP4", - "ARHGAP5", - "ARHGDIA", - "ARHGEF11", - "ARHGEF12", - "ARHGEF2", - "ARHGEF3", - "ARHGEF7", - "ARL8A", - "ATG4B", - "AURKA", - "BCAR1", - "BCL2L11", - "BCR", - "BIN1", - "BIRC5", - "BRCA2", - "BUB1", - "CAPZB", - "CCDC88A", - "CCNB2", - "CD2AP", - "CDC27", - "CDC42", - "CDC42BPA", - "CDC42EP1", - "CDC42EP2", - "CDC42EP4", - "CDK1", - "CDK5RAP2", - "CENPE", - "CENPF", - "CENPJ", - "CEP131", - "CEP192", - "CEP250", - "CEP57", - "CEP72", - "CKAP5", - "CLASP1", - "CLIP1", - "CLIP2", - "CNTRL", - "CNTROB", - "CSNK1D", - "CTTN", - "CYTH2", - "DLG1", - "DLGAP5", - "DOCK2", - "DOCK4", - "DST", - 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"RHOT2", - "RICTOR", - "ROCK1", - "SAC3D1", - "SASS6", - "SEPTIN9", - "SHROOM1", - "SHROOM2", - "SMC1A", - "SMC3", - "SMC4", - "SORBS2", - "SOS1", - "SPTAN1", - "SPTBN1", - "SSH2", - "STAU1", - "STK38L", - "SUN2", - "SYNPO", - "TAOK2", - "TBCD", - "TIAM1", - "TLK1", - "TOP2A", - "TPX2", - "TRIO", - "TSC1", - "TTK", - "TUBA4A", - "TUBD1", - "TUBGCP2", - "TUBGCP3", - "TUBGCP5", - "TUBGCP6", - "UXT", - "VCL", - "WASF1", - "WASF2", - "WASL", - "YWHAE" - ], - "module1_hallmarks__HALLMARK_MTORC1_SIGNALING": [ - "ABCF2", - "ACACA", - "ACLY", - "ACSL3", - "ACTR2", - "ACTR3", - "ADD3", - "ADIPOR2", - "AK4", - "ALDOA", - "ARPC5L", - "ASNS", - "ATP2A2", - "ATP5MC1", - "ATP6V1D", - "AURKA", - "BCAT1", - "BHLHE40", - "BTG2", - "BUB1", - "CACYBP", - "CALR", - "CANX", - "CCNF", - "CCNG1", - "CCT6A", - "CD9", - "CDC25A", - "CDKN1A", - "CFP", - "COPS5", - "CORO1A", - "CTH", - "CTSC", - "CXCR4", - "CYB5B", - "CYP51A1", - "DAPP1", - "DDIT3", - "DDIT4", - "DDX39A", - "DHCR24", - "DHCR7", - "DHFR", - "EBP", - 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"PRPS2", - "PSMA1", - "PSMA2", - "PSMA4", - "PSMA6", - "PSMA7", - "PSMB2", - "PSMB3", - "PSMC4", - "PSMC6", - "PSMD1", - "PSMD14", - "PSMD3", - "PSMD7", - "PSMD8", - "PTGES3", - "PWP1", - "RACK1", - "RAD23B", - "RAN", - "RANBP1", - "RFC4", - "RNPS1", - "RPL14", - "RPL18", - "RPL22", - "RPL34", - "RPL6", - "RPLP0", - "RPS10", - "RPS2", - "RPS3", - "RPS5", - "RPS6", - "RRM1", - "RRP9", - "RSL1D1", - "RUVBL2", - "SERBP1", - "SET", - "SF3A1", - "SF3B3", - "SLC25A3", - "SMARCC1", - "SNRPA", - "SNRPA1", - "SNRPB2", - "SNRPD1", - "SNRPD2", - "SNRPD3", - "SNRPG", - "SRM", - "SRPK1", - "SRSF1", - "SRSF2", - "SRSF3", - "SRSF7", - "SSB", - "SSBP1", - "STARD7", - "SYNCRIP", - "TARDBP", - "TCP1", - "TFDP1", - "TOMM70", - "TRA2B", - "TRIM28", - "TUFM", - "TXNL4A", - "TYMS", - "U2AF1", - "UBA2", - "UBE2E1", - "UBE2L3", - "USP1", - "VBP1", - "VDAC1", - "VDAC3", - "XPO1", - "XPOT", - "XRCC6", - "YWHAE", - "YWHAQ" - ], - "module1_hallmarks__HALLMARK_MYC_TARGETS_V2": [ - "AIMP2", - "BYSL", - "CBX3", - "CDK4", - "DCTPP1", - "DDX18", - "DUSP2", - "EXOSC5", - "FARSA", - "GNL3", - "GRWD1", - "HK2", - "HSPD1", - "HSPE1", - "IMP4", - "IPO4", - "LAS1L", - "MAP3K6", - "MCM4", - "MCM5", - "MPHOSPH10", - "MRTO4", - "MYBBP1A", - "MYC", - "NDUFAF4", - "NIP7", - "NOC4L", - "NOLC1", - "NOP16", - "NOP2", - "NOP56", - "NPM1", - "PA2G4", - "PES1", - "PHB1", - "PLK1", - "PLK4", - "PPAN", - "PPRC1", - "PRMT3", - "PUS1", - "RABEPK", - "RCL1", - "RRP12", - "RRP9", - "SLC19A1", - "SLC29A2", - "SORD", - "SRM", - "SUPV3L1", - "TBRG4", - "TCOF1", - "TFB2M", - "TMEM97", - "UNG", - "UTP20", - "WDR43", - "WDR74" - ], - "module1_hallmarks__HALLMARK_MYOGENESIS": [ - "ABLIM1", - "ACHE", - "ACSL1", - "ACTA1", - "ACTC1", - "ACTN2", - "ACTN3", - "ADAM12", - "ADCY9", - "AEBP1", - "AGL", - "AGRN", - "AK1", - "AKT2", - "ANKRD2", - "APLNR", - "APOD", - "APP", - "ATP2A1", - "ATP6AP1", - "BAG1", - "BDKRB2", - "BHLHE40", - "BIN1", - "CACNA1H", - "CACNG1", - "CAMK2B", - "CASQ1", - "CASQ2", - "CAV3", - "CD36", - "CDH13", - "CDKN1A", - "CFD", - "CHRNA1", - "CHRNB1", - "CHRNG", - "CKB", - "CKM", - "CKMT2", - "CLU", - "CNN3", - "COL15A1", - "COL1A1", - "COL3A1", - "COL4A2", - "COL6A2", - "COL6A3", - "COX6A2", - "COX7A1", - "CRAT", - "CRYAB", - "CSRP3", - "CTF1", - "DAPK2", - "DENND2B", - "DES", - "DMD", - "DMPK", - "DTNA", - "EFS", - "EIF4A2", - "ENO3", - "EPHB3", - "ERBB3", - "FABP3", - "FDPS", - "FGF2", - "FHL1", - "FKBP1B", - "FLII", - "FOXO4", - "FST", - "FXYD1", - "GAA", - "GABARAPL2", - "GADD45B", - "GJA5", - "GNAO1", - "GPX3", - "GSN", - "HBEGF", - "HDAC5", - "HRC", - "HSPB2", - "HSPB8", - "IFRD1", - "IGF1", - "IGFBP3", - "IGFBP7", - "ITGA7", - "ITGB1", - "ITGB4", - "ITGB5", - "KCNH1", - "KCNH2", - "KIFC3", - "KLF5", - "LAMA2", - "LARGE1", - "LDB3", - "LPIN1", - "LSP1", - "MAPK12", - "MAPRE3", - "MB", - "MEF2A", - "MEF2C", - "MEF2D", - "MRAS", - "MYBPC3", - "MYBPH", - "MYF6", - "MYH1", - "MYH11", - "MYH2", - "MYH3", - "MYH4", - "MYH7", - "MYH8", - "MYH9", - "MYL1", - "MYL11", - "MYL2", - "MYL3", - "MYL4", - "MYL6B", - "MYL7", - "MYLK", - "MYO1C", - "MYOG", - "MYOM1", - "MYOM2", - "MYOZ1", - "NAV2", - "NCAM1", - "NOS1", - "NOTCH1", - "NQO1", - "OCEL1", - "PC", - "PDE4DIP", - "PDLIM7", - "PFKM", - "PGAM2", - "PICK1", - "PKIA", - "PLXNB2", - "PPFIA4", - "PPP1R3C", - "PRNP", - "PSEN2", - "PTGIS", - "PTP4A3", - "PVALB", - "PYGM", - "RB1", - "REEP1", - "RIT1", - "RYR1", - "SCD", - "SCHIP1", - "SGCA", - "SGCD", - "SGCG", - "SH2B1", - "SH3BGR", - "SIRT2", - "SLC6A8", - "SLN", - "SMTN", - "SOD3", - "SORBS1", - "SORBS3", - "SPARC", - "SPDEF", - "SPEG", - "SPHK1", - "SPTAN1", - "SSPN", - "STC2", - "SVIL", - "SYNGR2", - "TAGLN", - "TCAP", - "TEAD4", - "TGFB1", - "TNNC1", - "TNNC2", - "TNNI1", - "TNNI2", - "TNNT1", - "TNNT2", - "TNNT3", - "TPD52L1", - "TPM2", - "TPM3", - "TSC2", - "VIPR1", - "WWTR1" - ], - "module1_hallmarks__HALLMARK_NOTCH_SIGNALING": [ - "APH1A", - "ARRB1", - "CCND1", - "CUL1", - "DLL1", - "DTX1", - "DTX2", - "DTX4", - "FBXW11", - "FZD1", - "FZD5", - "FZD7", - "HES1", - "HEYL", - "JAG1", - "KAT2A", - "LFNG", - "MAML2", - "NOTCH1", - "NOTCH2", - "NOTCH3", - "PPARD", - "PRKCA", - "PSEN2", - "PSENEN", - "RBX1", - "SAP30", - "SKP1", - "ST3GAL6", - "TCF7L2", - "WNT2", - "WNT5A" - ], - "module1_hallmarks__HALLMARK_OXIDATIVE_PHOSPHORYLATION": [ - "ABCB7", - "ACAA1", - "ACAA2", - "ACADM", - "ACADSB", - "ACADVL", - "ACAT1", - "ACO2", - "AFG3L2", - "AIFM1", - "ALAS1", - "ALDH6A1", - "ATP1B1", - "ATP5F1A", - "ATP5F1B", - "ATP5F1C", - "ATP5F1D", - "ATP5F1E", - "ATP5MC1", - "ATP5MC2", - "ATP5MC3", - "ATP5ME", - "ATP5MF", - "ATP5MG", - "ATP5PB", - "ATP5PD", - "ATP5PF", - "ATP5PO", - "ATP6AP1", - "ATP6V0B", - "ATP6V0C", - "ATP6V0E1", - "ATP6V1C1", - "ATP6V1D", - "ATP6V1E1", - "ATP6V1F", - "ATP6V1G1", - "ATP6V1H", - "BAX", - "BCKDHA", - "BDH2", - "CASP7", - "COX10", - "COX11", - "COX15", - "COX17", - "COX4I1", - "COX5A", - "COX5B", - "COX6A1", - "COX6B1", - "COX6C", - "COX7A2", - "COX7A2L", - "COX7B", - "COX7C", - "COX8A", - "CPT1A", - "CS", - "CYB5A", - "CYB5R3", - "CYC1", - "CYCS", - "DECR1", - "DLAT", - "DLD", - "DLST", - "ECH1", - "ECHS1", - "ECI1", - "ETFA", - "ETFB", - "ETFDH", - "FDX1", - "FH", - "FXN", - "GLUD1", - "GOT2", - "GPI", - "GPX4", - "GRPEL1", - "HADHA", - "HADHB", - "HCCS", - "HSD17B10", - "HSPA9", - "HTRA2", - "IDH1", - "IDH2", - "IDH3A", - "IDH3B", - "IDH3G", - "IMMT", - "ISCA1", - "ISCU", - "LDHA", - "LDHB", - "LRPPRC", - "MAOB", - "MDH1", - "MDH2", - "MFN2", - "MGST3", - "MPC1", - "MRPL11", - "MRPL15", - "MRPL34", - "MRPL35", - "MRPS11", - "MRPS12", - "MRPS15", - "MRPS22", - "MRPS30", - "MTRF1", - "MTRR", - "MTX2", - "NDUFA1", - "NDUFA2", - "NDUFA3", - "NDUFA4", - "NDUFA5", - "NDUFA6", - "NDUFA7", - "NDUFA8", - "NDUFA9", - "NDUFAB1", - "NDUFB1", - "NDUFB2", - "NDUFB3", - "NDUFB4", - "NDUFB5", - "NDUFB6", - "NDUFB7", - "NDUFB8", - "NDUFC1", - "NDUFC2", - "NDUFS1", - "NDUFS2", - "NDUFS3", - "NDUFS4", - "NDUFS6", - "NDUFS7", - "NDUFS8", - "NDUFV1", - "NDUFV2", - "NNT", - "NQO2", - "OAT", - "OGDH", - "OPA1", - "OXA1L", - "PDHA1", - "PDHB", - "PDHX", - "PDK4", - "PDP1", - "PHB2", - "PHYH", - "PMPCA", - "POLR2F", - "POR", - "PRDX3", - "RETSAT", - "RHOT1", - "RHOT2", - "SDHA", - "SDHB", - "SDHC", - "SDHD", - "SLC25A11", - "SLC25A12", - "SLC25A20", - "SLC25A3", - "SLC25A4", - "SLC25A5", - "SLC25A6", - "SUCLA2", - "SUCLG1", - "SUPV3L1", - "SURF1", - "TCIRG1", - "TIMM10", - "TIMM13", - "TIMM17A", - "TIMM50", - "TIMM8B", - "TIMM9", - "TOMM22", - "TOMM70", - "UQCR10", - "UQCR11", - "UQCRB", - "UQCRC1", - "UQCRC2", - "UQCRFS1", - "UQCRH", - "UQCRQ", - "VDAC1", - "VDAC2", - "VDAC3" - ], - "module1_hallmarks__HALLMARK_P53_PATHWAY": [ - "ABAT", - "ABCC5", - "ABHD4", - "ACVR1B", - "ADA", - "AEN", - "AK1", - "ALOX15B", - "ANKRA2", - "APAF1", - "APP", - "ATF3", - "BAIAP2", - "BAK1", - "BAX", - "BLCAP", - "BMP2", - "BTG1", - "BTG2", - "CASP1", - "CCND2", - "CCND3", - "CCNG1", - "CCNK", - "CCP110", - "CD81", - "CD82", - "CDH13", - "CDK5R1", - "CDKN1A", - "CDKN2A", - "CDKN2AIP", - "CDKN2B", - "CEBPA", - "CGRRF1", - "CLCA2", - "COQ8A", - "CSRNP2", - "CTSD", - "CTSF", - "CYFIP2", - "DCXR", - "DDB2", - "DDIT3", - "DDIT4", - "DEF6", - "DGKA", - "DNTTIP2", - "DRAM1", - "EI24", - "ELP1", - "EPHA2", - "EPHX1", - "EPS8L2", - "ERCC5", - "F2R", - "FAM162A", - "FAS", - "FBXW7", - "FDXR", - "FGF13", - "FOS", - "FOXO3", - "FUCA1", - "GADD45A", - "GLS2", - "GM2A", - "GPX2", - "H1-2", - "H2AC25", - "H2AJ", - "HBEGF", - "HDAC3", - "HEXIM1", - "HINT1", - "HMOX1", - "HRAS", - "HSPA4L", - "IER3", - "IER5", - "IFI30", - "IL1A", - "INHBB", - "IP6K2", - "IRAG2", - "IRAK1", - "ISCU", - "ITGB4", - "JAG2", - "JUN", - "KIF13B", - "KLF4", - "KLK8", - "KRT17", - "LDHB", - "LIF", - "MAPKAPK3", - "MDM2", - "MKNK2", - "MXD1", - "MXD4", - "NDRG1", - "NHLH2", - "NINJ1", - "NOL8", - "NOTCH1", - "NUDT15", - "NUPR1", - "OSGIN1", - "PCNA", - "PDGFA", - "PERP", - "PHLDA3", - "PIDD1", - "PITPNC1", - "PLK2", - "PLK3", - "PLXNB2", - "PMM1", - "POLH", - "POM121", - "PPM1D", - "PPP1R15A", - "PRKAB1", - "PRMT2", - "PROCR", - "PTPN14", - "PTPRE", - "PVT1", - "RAB40C", - "RACK1", - "RAD51C", - "RAD9A", - "RALGDS", - "RAP2B", - "RB1", - "RCHY1", - "RETSAT", - "RGS16", - "RHBDF2", - "RNF19B", - "RPL18", - "RPL36", - "RPS12", - "RPS27L", - "RRAD", - "RRP8", - "RXRA", - "S100A10", - "S100A4", - "SAT1", - "SDC1", - "SEC61A1", - "SERPINB5", - "SERTAD3", - "SESN1", - "SFN", - "SLC19A2", - "SLC35D1", - "SLC3A2", - "SLC7A11", - "SOCS1", - "SP1", - "SPHK1", - "ST14", - "STEAP3", - "STOM", - "TAP1", - "TAX1BP3", - "TCHH", - "TCN2", - "TGFA", - "TGFB1", - "TM4SF1", - "TM7SF3", - "TNFSF9", - "TNNI1", - "TOB1", - "TP53", - "TP63", - "TPD52L1", - "TPRKB", - "TRAF4", - "TRAFD1", - "TRIAP1", - "TRIB3", - "TSC22D1", - "TSPYL2", - "TXNIP", - "UPP1", - "VAMP8", - "VDR", - "VWA5A", - "WRAP73", - "WWP1", - "XPC", - "ZBTB16", - "ZFP36L1", - "ZMAT3", - "ZNF365" - ], - "module1_hallmarks__HALLMARK_PANCREAS_BETA_CELLS": [ - "ABCC8", - "AKT3", - "CHGA", - "DCX", - "DPP4", - "ELP4", - "FOXA2", - "FOXO1", - "G6PC2", - "GCG", - "GCK", - "HNF1A", - "IAPP", - "INS", - "INSM1", - "ISL1", - "LMO2", - "MAFB", - "NEUROD1", - "NEUROG3", - "NKX2-2", - "NKX6-1", - "PAK3", - "PAX4", - "PAX6", - "PCSK1", - "PCSK2", - "PDX1", - "PKLR", - "SCGN", - "SEC11A", - "SLC2A2", - "SPCS1", - "SRP14", - "SRP9", - "SRPRB", - "SST", - "STXBP1", - "SYT13", - "VDR" - ], - "module1_hallmarks__HALLMARK_PEROXISOME": [ - "ABCB1", - "ABCB4", - "ABCB9", - "ABCC5", - "ABCC8", - "ABCD1", - "ABCD2", - "ABCD3", - "ACAA1", - "ACOT8", - "ACOX1", - "ACSL1", - "ACSL4", - "ACSL5", - "ALB", - "ALDH1A1", - "ALDH9A1", - "ATXN1", - "BCL10", - "CACNA1B", - "CADM1", - "CAT", - "CDK7", - "CEL", - "CLN6", - "CLN8", - "CNBP", - "CRABP1", - "CRABP2", - "CRAT", - "CTBP1", - "CTPS1", - "DHCR24", - "DHRS3", - "DIO1", - "DLG4", - "ECH1", - "ECI2", - "EHHADH", - "ELOVL5", - "EPHX2", - "ERCC1", - "ERCC3", - "ESR2", - "FABP6", - "FADS1", - "FDPS", - "FIS1", - "GNPAT", - "GSTK1", - "HAO2", - "HMGCL", - "HRAS", - "HSD11B2", - "HSD17B11", - "HSD17B4", - "HSD3B7", - "IDE", - "IDH1", - "IDH2", - "IDI1", - "ISOC1", - "ITGB1BP1", - "LONP2", - "MLYCD", - "MSH2", - "MVP", - "NR1I2", - "NUDT19", - "PABPC1", - "PEX11A", - "PEX11B", - "PEX13", - "PEX14", - "PEX2", - "PEX5", - "PEX6", - "PRDX1", - "PRDX5", - "RDH11", - "RETSAT", - "RXRG", - "SCGB1A1", - "SCP2", - "SEMA3C", - "SERPINA6", - "SIAH1", - "SLC23A2", - "SLC25A17", - "SLC25A19", - "SLC25A4", - "SLC27A2", - "SLC35B2", - "SMARCC1", - "SOD1", - "SOD2", - "STS", - "SULT2B1", - "TOP2A", - "TSPO", - "TTR", - "UGT2B17", - "VPS4B", - "YWHAH" - ], - "module1_hallmarks__HALLMARK_PI3K_AKT_MTOR_SIGNALING": [ - "ACACA", - "ACTR2", - "ACTR3", - "ADCY2", - "AKT1", - "AKT1S1", - "AP2M1", - "ARF1", - "ARHGDIA", - "ARPC3", - "ATF1", - "CAB39", - "CAB39L", - "CALR", - "CAMK4", - "CDK1", - "CDK2", - "CDK4", - "CDKN1A", - "CDKN1B", - "CFL1", - "CLTC", - "CSNK2B", - "CXCR4", - "DAPP1", - "DDIT3", - "DUSP3", - "E2F1", - "ECSIT", - "EGFR", - "EIF4E", - "FASLG", - "FGF17", - "FGF22", - "FGF6", - "GNA14", - "GNGT1", - "GRB2", - "GRK2", - "GSK3B", - "HRAS", - "HSP90B1", - "IL2RG", - "IL4", - "IRAK4", - "ITPR2", - "LCK", - "MAP2K3", - "MAP2K6", - "MAP3K7", - "MAPK1", - "MAPK10", - "MAPK8", - "MAPK9", - "MAPKAP1", - "MKNK1", - "MKNK2", - "MYD88", - "NCK1", - "NFKBIB", - "NGF", - "NOD1", - "PAK4", - "PDK1", - "PFN1", - "PIK3R3", - "PIKFYVE", - "PIN1", - "PITX2", - "PLA2G12A", - "PLCB1", - "PLCG1", - "PPP1CA", - "PPP2R1B", - "PRKAA2", - "PRKAG1", - "PRKAR2A", - "PRKCB", - "PTEN", - "PTPN11", - "RAC1", - "RAF1", - "RALB", - "RIPK1", - "RIT1", - "RPS6KA1", - "RPS6KA3", - "RPTOR", - "SFN", - "SLA", - "SLC2A1", - "SMAD2", - "SQSTM1", - "STAT2", - "TBK1", - "THEM4", - "TIAM1", - "TNFRSF1A", - "TRAF2", - "TRIB3", - "TSC2", - "UBE2D3", - "UBE2N", - "VAV3", - "YWHAB" - ], - "module1_hallmarks__HALLMARK_PROTEIN_SECRETION": [ - "ABCA1", - "ADAM10", - "ANP32E", - "AP1G1", - "AP2B1", - "AP2M1", - "AP2S1", - "AP3B1", - "AP3S1", - "ARCN1", - "ARF1", - "ARFGAP3", - "ARFGEF1", - "ARFGEF2", - "ARFIP1", - "ATP1A1", - "ATP6V1B1", - "ATP6V1H", - "ATP7A", - "BET1", - "BNIP3", - "CAV2", - "CD63", - "CLCN3", - "CLN5", - "CLTA", - "CLTC", - "COG2", - "COPB1", - "COPB2", - "COPE", - "CTSC", - "DNM1L", - "DOP1A", - "DST", - "EGFR", - "ERGIC3", - "GALC", - "GBF1", - "GLA", - "GNAS", - "GOLGA4", - "GOSR2", - "ICA1", - "IGF2R", - "KIF1B", - "KRT18", - "LAMP2", - "LMAN1", - "M6PR", - "MAPK1", - "MON2", - "NAPA", - "NAPG", - "OCRL", - "PAM", - "PPT1", - "RAB14", - "RAB22A", - "RAB2A", - "RAB5A", - "RAB9A", - "RER1", - "RPS6KA3", - "SCAMP1", - "SCAMP3", - "SCRN1", - "SEC22B", - "SEC24D", - "SEC31A", - "SGMS1", - "SH3GL2", - "SNAP23", - "SNX2", - "SOD1", - "SSPN", - "STAM", - "STX12", - "STX16", - "STX7", - "TMED10", - "TMED2", - "TMX1", - "TOM1L1", - "TPD52", - "TSG101", - "TSPAN8", - "USO1", - "VAMP3", - "VAMP4", - "VAMP7", - "VPS45", - "VPS4B", - "YIPF6", - "YKT6", - "ZW10" - ], - "module1_hallmarks__HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY": [ - "ABCC1", - "ATOX1", - "CAT", - "CDKN2D", - "EGLN2", - "ERCC2", - "FES", - "FTL", - "G6PD", - "GCLC", - "GCLM", - "GLRX", - "GLRX2", - "GPX3", - "GPX4", - "GSR", - "HHEX", - 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"HTR5A", - "IDE", - "IFT88", - "IL12RB2", - "IL13RA2", - "IP6K1", - "JAM3", - "KIF2C", - "LDHC", - "LPIN1", - "MAP7", - "MAST2", - "MEP1B", - "MLF1", - "MLLT10", - "MTNR1A", - "MTOR", - "NAA11", - "NCAPH", - "NEFH", - "NEK2", - "NF2", - "NOS1", - "NPHP1", - "NPY5R", - "OAZ3", - "ODF1", - "PACRG", - "PAPOLB", - "PARP2", - "PCSK1N", - "PCSK4", - "PDHA2", - "PEBP1", - "PGK2", - "PGS1", - "PHF7", - "PHKG2", - "PIAS2", - "POMC", - "PRKAR2A", - "PRM2", - "PSMG1", - "RAD17", - "RFC4", - "RPL39L", - "SCG3", - "SCG5", - "SEPTIN4", - "SHE", - "SIRT1", - "SLC12A2", - "SLC2A5", - "SNAP91", - "SPATA6", - "SPMAP2", - "STAM2", - "STRBP", - "SYCP1", - "TALDO1", - "TCP11", - "TEKT2", - "TKTL1", - "TLE4", - "TNNI3", - "TNP1", - "TNP2", - "TOPBP1", - "TSN", - "TSSK2", - "TTK", - "TUBA3C", - "TULP2", - "VDAC3", - "YBX2", - "ZC2HC1C", - "ZC3H14", - "ZNRF4", - "ZPBP" - ], - "module1_hallmarks__HALLMARK_TGF_BETA_SIGNALING": [ - "ACVR1", - "APC", - "ARID4B", - "BCAR3", - "BMP2", - "BMPR1A", - "BMPR2", - "CDH1", - 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"IKBKG" - ], - "module2_reactome_2022_filtered__Antigen Processing: Ubiquitination And Proteasome Degradation R-HSA-983168": [ - "WSB1", - "FBXO2", - "LNPEP", - "UBAC1", - "PSMB9", - "CDC20", - "MEX3C", - "PSMC6", - "PSMC4", - "PSMC5", - "ASB9", - "CDC16", - "PSMC2", - "PSMC3", - "ASB7", - "ASB8", - "PSMC1", - "ASB5", - "RBCK1", - "ASB6", - "RNF220", - "ASB4", - "TRIM11", - "ASB1", - "ASB2", - "KEAP1", - "CCNF", - "RNF7", - "CBLB", - "RNF6", - "UBE2L6", - "PSMB11", - "UBE2L3", - "PSMB10", - "RNF4", - "HERC6", - "PSMD9", - "RNF114", - "DCAF1", - "FBXL22", - "HERC5", - "CDC23", - "HERC4", - "PSMD7", - "FBXL20", - "FZR1", - "HERC3", - "PSMD8", - "RNF115", - "HERC2", - "PSMD5", - "CDC26", - "HERC1", - "PSMD6", - "CDC27", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "RNF111", - "SIAH1", - "SIAH2", - "RNF144B", - "PSME4", - "HECW2", - "PSME2", - "PSME3", - "FBXL4", - "FBXL3", - "PSME1", - "DZIP3", - "FBXL5", - "FBXL8", - "FBXL7", - "ANAPC13", - "LRR1", - "NEDD4L", - "ANAPC10", - "ANAPC11", - 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"CHST10", - "ST3GAL5", - "ST3GAL6", - "ST3GAL1", - "ST3GAL2", - "ST3GAL3", - "ST3GAL4", - "NUS1", - "ALG9", - "ST6GAL2", - "CHST8", - "COG8", - "NAPSA", - "TMED9", - "COG7", - "LMAN1L", - "PMM2", - "SLC35C1", - "ST8SIA1", - "ALG8", - "COG6", - "EDEM1", - "PMM1", - "ST6GAL1", - "ST8SIA2", - "ALG5", - "COG5", - "EDEM2", - "ST8SIA3", - "ALG6", - "COG4", - "EDEM3", - "ST8SIA4", - "ALG3", - "COG3", - "COG2", - "ALG1", - "ANK3", - "COG1", - "ALG2", - "ANK1", - "ANK2", - "ARFGAP2", - "COPZ2", - "ARFGAP3", - "COPZ1", - "ARFGAP1", - "GOLGB1", - "GORASP1", - "GNE", - "LHB", - "NGLY1", - "LMAN2L", - "GOLGA2", - "SEC31A", - "CTSA", - "FUCA1", - "SEC13", - "PTGES2", - "SEC16B", - "SEC16A", - "RENBP", - "COPG1", - "COPG2", - "ACTR10", - "CALR", - "ARF4", - "SPTBN4", - "UBXN1", - "ARF5", - "SPTBN5", - "SERPINA1", - "ARF3", - "MOGAT3", - "MOGAT2", - "RPN1", - "SEL1L", - "MOGAT1", - "RPN2", - "CTSZ", - "TGFA", - "UMOD", - "AREG", - "ACTR1A", - "NEU3", - "NEU4", - "SPTA1", - "MAGT1", - "NEU2", - "GNPNAT1", - "MLEC", - "ST8SIA5", - "ST8SIA6", - "TMED3", - "TMED2", - "RPS27A", - "SPTBN1", - "TMED7", - "DYNC1I2", - "SPTBN2", - "CTSC", - "DYNC1I1", - "DYNLL1", - "SYVN1", - "DYNLL2", - "ARCN1", - "F5", - "F8", - "SEC23IP", - "CANX", - "USO1", - "DYNC1H1", - "ENGASE", - "DERL2", - "NPL", - "ASGR1", - "MIA3", - "DERL1", - "ASGR2", - "LMAN1", - "NANP", - "LMAN2", - "NANS", - "OS9", - "RNF139", - "MGAT5", - "CD59", - "SPTAN1", - "NUDT14", - "CD55", - "MARCHF6", - "SLC17A5", - "SEC24A", - "SEC24C", - "ANKRD28", - "SEC24B", - "SRD5A3", - "TRAPPC10", - "ALG14", - "DOLPP1", - "ALG13", - "ALG12", - "ALG11", - "DHDDS", - "DPM1", - "DPM2", - "DPM3", - "CNIH1", - "CNIH2", - "CAPZA2", - "CAPZA3", - "TBC1D20", - "CAPZA1", - "RNF185", - "CNIH3", - "SEC24D", - "RAB1A", - "DPAGT1", - "RAB1B", - "TUSC3", - "FUOM", - "NAPG", - "MANEA", - "STX5", - "YKT6", - "MPDU1", - "TRAPPC2L", - "TRAPPC3", - "NSF", - "PDIA3", - "TRAPPC4", - "TRAPPC1", - "TRAPPC2", - "TRAPPC5", - "BET1L", - "DOLK", - "TRAPPC9", - "DDOST", - "TMEM115", - "STT3A", - "ST6GALNAC4", - "ST6GALNAC5", - "ST6GALNAC6", - "COPA", - "GRIA1", - "VCP", - "COPB2", - "COPB1", - "UBC", - "AMDHD2", - "MAN2A2", - "UBB", - "FPGT", - "MAN2A1", - "EGFL7", - "GFPT1", - "GFPT2", - "RAD23B", - "SPTB", - "RFT1", - "COL7A1", - "COPE", - "B4GALT3", - "B4GALT4", - "MCFD2", - "ST6GALNAC1", - "B4GALT1", - "ST6GALNAC2", - "B4GALT2", - "ST6GALNAC3", - "SAR1B", - "UAP1", - "FUT3", - "PPP6C", - "FUT8", - "SCFD1", - "PPP6R1", - "GMPPA", - "PPP6R3", - "CAPZB", - "GMPPB", - "B4GALNT2", - "MAN1A2", - "MAN1A1", - "SEC22A", - "GLB1", - "NAGK", - "TRAPPC6B", - "TRAPPC6A", - "FCSK", - "MGAT4A", - "DAD1", - "MGAT4B", - "MGAT4C", - "MAN1B1", - "KDELR3", - "MVD", - "SEC22C", - "UBA52", - "FOLR2", - "SEC22B", - "B4GALT5", - "B4GALT6", - "BET1", - "DCTN6", - "SEC23A", - "DCTN3", - "GOSR1", - "PRKCSH", - "DCTN2", - "DCTN5", - "DCTN4", - "GOSR2", - "DCTN1", - "INS", - "GANAB", - "MAN1C1", - "PGM3", - "GFUS", - "UGGT2", - "UGGT1" - ], - "module2_reactome_2022_filtered__Assembly Of Collagen Fibrils And Other Multimeric Structures R-HSA-2022090": [ - "CTSB", - "MMP7", - "LAMB3", - "MMP3", - "COL24A1", - "MMP9", - "COL1A2", - "MMP13", - "COL1A1", - "COL3A1", - "BMP1", - "COL5A2", - "PXDN", - "COL5A1", - "COL5A3", - "COL7A1", - "COL9A2", - "COL9A1", - "COL9A3", - "ITGA6", - "PLEC", - "COL18A1", - "LOXL4", - "LAMC2", - "CTSV", - "LOXL1", - "LOXL2", - "CTSS", - "LOXL3", - "CTSL", - "COL10A1", - "COL27A1", - "DST", - "PCOLCE", - "COL2A1", - "COL4A1", - "COL4A3", - "COL6A1", - "COL4A2", - "LOX", - "COL4A5", - "COL6A3", - "COL8A1", - "COL4A4", - "COL6A2", - "COL6A5", - "COL8A2", - "TLL2", - "COL6A6", - "TLL1", - "CD151", - "COL15A1", - "COL11A2", - "ITGB4", - "LAMA4", - "MMP28", - "COL11A1" - ], - "module2_reactome_2022_filtered__Assembly Of Pre-Replicative Complex R-HSA-68867": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "UBE2C", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "PSMB9", - "PSMC6", - "PSMC4", - "UBE2S", - "PSMC5", - "CDC16", - "PSMC2", - "PSMC3", - "PSMC1", - "UBE2D1", - "H2AB1", - "PSMB10", - "PSMD9", - "CDC23", - "PSMD7", - 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"MAD2L1", - "UBE2N", - "ZWILCH", - "UBE2D1", - "PMF1-BGLAP", - "NUP160", - "AURKB", - "PPP1CC", - "NSD2", - "CLASP1", - "KNTC1", - "RBBP8", - "CLASP2", - "PIAS4", - "CKAP5", - "NDC80", - "CCNA2", - "KIF18A", - "RNF168", - "CCNA1", - "SEM1", - "KIF2B", - "H4C15", - "KIF2A", - "BIRC5", - "MDM4", - "CARD16", - "KIF2C", - "MAD1L1", - "RAD9A", - "MDM2", - "RAD9B", - "BLM", - "PKMYT1", - "CCNB2", - "CCNB1", - "CLSPN", - "UBC", - "UBB", - "NDEL1", - "ZNF385A", - "RFC4", - "REXO2", - "RFC5", - "RFC2", - "RFC3", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "H2BC17", - "PPP2R5A", - "H2BC13", - "H2BC14", - "H2BC15", - "H2BC11", - "H2BC12", - "CDK2", - "MCM4", - "CDK1", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "YWHAG", - "PSMD13", - "SEH1L", - "PSMD12", - "YWHAE", - "YWHAB", - "PSMD14", - "MCM10", - "ORC6", - "ORC5", - "NUP43", - "ORC4", - "ORC3", - "YWHAQ", - "TOPBP1", - "ATRIP", - "YWHAH", - "YWHAZ", - "RAD50", - "RANGAP1", - "H3-4", - "WEE1", - "DBF4", - "POLR1A", - "POLR1B", - "ANAPC4", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "NUP37", - "SLC25A15", - "CENPS-CORT", - "ITGB3BP", - "NUP107", - "ERCC6L", - "MCM8", - "KANSL1", - "CDC45", - "H2BC26", - "EXO1", - "H2BC21", - "PPP2R1B", - "BUB3", - "SPDL1", - "BUB1" - ], - "module2_reactome_2022_filtered__Cell Death Signaling Via NRAGE, NRIF And NADE R-HSA-204998": [ - "ARHGEF33", - "PSENEN", - "ARHGEF35", - "ARHGEF38", - "ARHGEF37", - "BAD", - "ARHGEF39", - "VAV1", - "VAV2", - "VAV3", - "OBSCN", - "ARHGEF4", - "ARHGEF5", - "ARHGEF2", - "ARHGEF3", - "SOS2", - "SQSTM1", - "ARHGEF9", - "NGEF", - "ARHGEF6", - "ARHGEF7", - "SOS1", - "YWHAE", - "ARHGEF1", - "RASGRF2", - "ARHGEF10L", - "FGD1", - "KALRN", - "AATF", - "APH1B", - "FGD4", - "APH1A", - "BCL2L11", - "MCF2L", - "PREX1", - "CASP2", - "CASP3", - "ECT2", - "ARHGEF40", - "NCSTN", - "RPS27A", - "ARHGEF12", - "NGFR", - "ARHGEF11", - "PLEKHG5", - "PLEKHG2", - "PRPF38B", - "ARHGEF16", - "ARHGEF15", - "MCF2", - "ARHGEF18", - 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"ACTN2", - "ACTN1", - "CADM1", - "TYROBP", - "PTPN6", - "SKAP2", - "TESK1", - "PVR", - "CLDN3", - "CLDN2", - "CDH9", - "CLDN1", - "PARD6G", - "CDH8", - "CD2AP", - "PARD6A", - "CDH6", - "CDH5", - "CDH4", - "FZR1", - "PARD6B", - "CDH2", - "FLNA", - "FLNC", - "FYB1", - "PRKCI", - "PARD3", - "CASK", - "PTK2", - "VASP", - "SDK1", - "FERMT2", - "SDK2", - "PIK3R1", - "CDH10", - "CDH11", - "CDH12", - "CDH13", - "CDH15", - "GRB2", - "CDH17", - "CDH18", - "CDH19", - "COL17A1", - "PATJ", - "CD151", - "SIRPG", - "F11R", - "CDH24", - "SIRPA", - "JUP", - "LAMB3", - "PTPN11", - "PARVB", - "PARVA", - "AFDN", - "CLDN12", - "CLDN11", - "CLDN10", - "CLDN16", - "PIK3CB", - "PXDN", - "CLDN15", - "PIK3CA", - "CLDN14", - "CLDN19", - "CLDN18", - "CLDN17", - "ITGA6", - "LIMS1", - "PLEC", - "ARHGEF6", - "PALS1", - "LIMS2", - "FBLIM1", - "ILK", - "IQGAP1", - "LAMC2", - "PIK3R2", - "SIRPB1", - "RSU1", - "SPTBN1", - "MAGI2", - "DST", - "CLDN7", - "CLDN6", - "CLDN5", - "CLDN4", - "NPHS1", - "CLDN9", - "CLDN8", - "NECTIN1", - "ANG", - "CD47", - "NECTIN4", - "NECTIN3", - "NECTIN2", - "CRB3", - "ITGB1", - "SRC", - "CTNNB1", - "ITGB4", - "LAMA4", - "WASL", - "CLDN23", - "CLDN22", - "CLDN20", - "PTK2B", - "CTNNA1", - "FYN", - "SPTAN1", - "NCK1", - "NCK2" - ], - "module2_reactome_2022_filtered__Cell-cell Junction Organization R-HSA-421270": [ - "CADM2", - "CADM3", - "JUP", - "CADM1", - "AFDN", - "CLDN12", - "CLDN11", - "CLDN10", - "CLDN16", - "CLDN15", - "CLDN14", - "CLDN19", - "CLDN18", - "CLDN17", - "PALS1", - "PVR", - "CLDN3", - "CLDN2", - "CDH9", - "CLDN1", - "PARD6G", - "CDH8", - "PARD6A", - "CDH6", - "CDH5", - "CDH4", - "FZR1", - "PARD6B", - "CDH2", - "PRKCI", - "PARD3", - "CLDN7", - "SDK1", - "CLDN6", - "SDK2", - "CLDN5", - "CLDN4", - "CDH10", - "CDH11", - "CLDN9", - "CDH12", - "CLDN8", - "CDH13", - "NECTIN1", - "ANG", - "CDH15", - "CDH17", - "CDH18", - "NECTIN4", - "CDH19", - "NECTIN3", - "NECTIN2", - "CRB3", - "PATJ", - "CTNNB1", - "F11R", - "CLDN23", - "CLDN22", - "CLDN20", - "CDH24", - "CTNNA1" - ], - 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"PSMF1" - ], - "module2_reactome_2022_filtered__Cellular Response To Starvation R-HSA-9711097": [ - "ATP6V1E1", - "SZT2", - "RPL22", - "RPS8", - "RPL21", - "RPS9", - "RPS6", - "RPL23", - "RPS7", - "RPSA", - "RRAGB", - "RRAGA", - "RRAGD", - "RRAGC", - "BMT2", - "FAU", - "RPL26", - "RPL28", - "RPL22L1", - "RPL27", - "RPL29", - "RPL26L1", - "ATF3", - "ATP6V0D1", - "RPL11", - "NPRL2", - "NPRL3", - "RPL12", - "RPS4Y2", - "RPS4Y1", - "RPL36AL", - "SH3BP4", - "RPL15", - "RPL14", - "RPS5", - "RPS2", - "RPS3", - "ATP6V1E2", - "RPL19", - "RPL18", - "ATP6V0C", - "WDR59", - "ATP6V0B", - "RPL41", - "EIF2S3", - "EIF2S2", - "EIF2S1", - "RPL39L", - "RPL3L", - "RPS27", - "RPS29", - "RPS28", - "RHEB", - "GDNF", - "RPL27A", - "RPS21", - "RPS20", - "RPS23", - "ATF4", - "ATP6V1B1", - "RPS25", - "RSL24D1", - "ATP6V1B2", - "RPS24", - "RPL5", - "ATP6V1A", - "RPL6", - "ATP6V1D", - "RPL3", - "RPL31", - "RPL30", - "RPL4", - "RPL32", - "RPL35", - "RPL34", - "MRPL13", - "RPL9", - "RPL7", - "RPL8", - "RPS16", - "RPS15", - 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"H2AZ2", - "ANKRD28", - "H2AZ1", - "H2BC9", - "KNL1", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "SMARCA5", - "MIS18A", - "SHQ1", - "DKC1", - "DNA2", - "NHP2", - "PCNA", - "HJURP", - "H2AB1", - "RUVBL1", - "RBBP4", - "RUVBL2", - "RBBP7", - "DAXX", - "H2AC7", - "H2AC6", - "H2AC4", - "TERF1", - "TERF2", - "CCNA2", - "CCNA1", - "H2AC20", - "H4C15", - "NOP10", - "BLM", - "CHTF18", - "H2AC19", - "CHTF8", - "H2AC14", - "OIP5", - "SLC19A1", - "CENPU", - "ACD", - "CTC1", - "CENPW", - "RFC4", - "CENPX", - "RFC5", - "RFC2", - "RFC3", - "H2BC17", - "H2BC13", - "POLA1", - "WRAP53", - "H2BC14", - "POLA2", - "H2BC15", - "CENPH", - "H2AJ", - "CENPI", - "CENPK", - "H2BC11", - "CENPL", - "H2BC12", - "CDK2", - "CENPM", - "CENPN", - "CENPO", - "CENPP", - "CENPQ", - "POT1", - "TERF2IP", - "CENPT", - "PIF1", - "RTEL1", - "PPP6C", - "MIS18BP1", - "TERT", - "PPP6R3", - "GAR1", - "H3-3B", - "NPM1", - "STN1", - "ATRX", - "TINF2", - "RPA3", - "H3-4", - "POLR1A", - "POLR1B", - "CENPS-CORT", - "ITGB3BP", - "FEN1", - "H2AX", - "DSCC1", - "PRIM2", - "PRIM1", - "CENPA", - "RSF1", - "POLD4", - "H2BC26", - "POLR2A", - "WRN", - "POLR2B", - "H2BC21", - "POLD1", - "POLR2C", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "POLR2F", - "LRIG1", - "POLR2G", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__Cilium Assembly R-HSA-5617833": [ - "DYNC2H1", - "BBIP1", - "IFT81", - "PPP2R1A", - "RPGRIP1L", - "IFT80", - "TUBA4A", - "IFT88", - "CCP110", - "TTC30A", - "TTC30B", - "CEP83", - "B9D2", - "B9D1", - "CEP89", - "SSNA1", - "IFT172", - "AKAP9", - "ASAP1", - "CSNK1D", - "TMEM67", - "IFT74", - "NPHP1", - "CEP250", - "CLASP1", - "NPHP4", - "CEP97", - "RAB11FIP3", - "PCNT", - "PLK4", - "ODF2", - "PLK1", - "RAB3IP", - "TTC21B", - "CSNK1E", - "KIF24", - "PRKCA", - "CYS1", - "TUBG1", - "CKAP5", - "BBS10", - "IFT20", - "BBS12", - "IFT22", - "DYNLRB1", - "CEP63", - "DYNLRB2", - "PRKD2", - "CNGB1", - "IFT27", - "MKS1", - "UNC119B", - "ARL6", - "ARL3", - "MCHR1", - "TTBK2", - "KIF3C", - "KIF3B", - "CEP70", - "INPP5E", - "KIF3A", - "PRKAR2B", - "CDK5RAP2", - "CEP72", - "NEK2", - "KIFAP3", - "CEP76", - "CEP192", - "MARK4", - "CEP78", - "BBS2", - "MKKS", - "BBS1", - "HSP90AA1", - "IQCB1", - "PTGES2", - "EXOC8", - "OFD1", - "TUBB", - "TCP1", - "IFT122", - "SSTR3", - "WDR35", - "AHI1", - "IFT43", - "CENPJ", - "TMEM216", - "CEP162", - "EXOC5", - "PAFAH1B1", - "CDK1", - "EXOC4", - "CEP41", - "EXOC7", - "EXOC6", - "CEP43", - "EXOC1", - "SFI1", - "IFT46", - "EXOC3", - "SDCCAG8", - "EXOC2", - "ARF4", - "TTC26", - "YWHAG", - "YWHAE", - "C2CD3", - "CETN2", - "CEP164", - "DYNLT2B", - "FBF1", - "ACTR1A", - "KIF17", - "HSPB11", - "TNPO1", - "CNTRL", - "DYNC2LI1", - "CNGA2", - "BBS9", - "CNGA4", - "BBS7", - "CC2D2A", - "CEP290", - "BBS5", - "DYNC1I2", - "BBS4", - "CEP57", - "CCT4", - "CCT3", - "CCT2", - "CEP135", - "DYNLL1", - "DYNLT2", - "SEPTIN2", - "SMOX", - "NDE1", - "CEP131", - "IFT140", - "WDR19", - "DYNLT5", - "HAUS5", - "HAUS4", - "SCLT1", - "HAUS7", - "HAUS6", - "DYNLL2", - "HAUS1", - "RAB11A", - "TUBB4B", - "HAUS3", - "TUBB4A", - "HAUS2", - "LZTFL1", - "ATAT1", - "NEDD1", - "TTC8", - "TRIP11", - "CLUAP1", - "DYNC1H1", - "MAPRE1", - "DYNC2I2", - "HDAC6", - "PKD1", - "DCTN3", - "DCTN2", - "TRAF3IP1", - "ALMS1", - "DCTN1", - "TUBA1A", - "PCM1", - "ARL13B", - "HAUS8", - "IFT52", - "TCTN3", - "TCTN2", - "TCTN1", - "CEP152", - "NUDT19", - "PDE6D", - "RAB8A", - "RHO", - "CCT8", - "NINL", - "IFT57", - "DYNC2I1", - "CCT5" - ], - "module2_reactome_2022_filtered__Circadian Clock R-HSA-400253": [ - "NCOA2", - "NCOA1", - "NCOA6", - "RNASE12", - "SIRT1", - "PPP1CA", - "PPP1CB", - "NCOR1", - "TBL1XR1", - "BHLHE40", - "BHLHE41", - "CARM1", - "BMAL1", - "BMAL2", - "CRTC3", - "CRTC1", - "LIN9", - "CRTC2", - "CHD9", - "SERPINE1", - "HSD17B4", - "NPAS2", - "PPP1CC", - "RXRA", - "NRIP1", - "CSNK1D", - "NOCT", - "NLRP3", - "TBL1X", - "NFIL3", - "PPARGC1A", - "BTRC", - "RPS27A", - "MBD4", - "SREBF1", - "CREBBP", - "MEF2D", - "CPT1A", - "MEF2C", - "PPARA", - "ELOVL3", - "NR1D1", - "CSNK1E", - "KLF15", - "F7", - "PER2", - "CREB1", - "GDNF", - "LOC102724428", - "PER1", - "CRY2", - "CRY1", - "FBXL3", - "UBA52", - "CLOCK", - "HDAC3", - "SMARCD3", - "CREM", - "CUL1", - "HIF1A", - "NR3C1", - "RORA", - "RBM4", - "HELZ2", - "NAMPT", - "UBC", - "UBB", - "EP300", - "SKP1" - ], - "module2_reactome_2022_filtered__Class A/1 (Rhodopsin-like Receptors) R-HSA-373076": [ - "TRHR", - "CHGA", - "TAAR9", - "NPFF", - "TAAR8", - "GPR37L1", - "TAAR5", - "TAAR6", - "TAAR1", - "PDYN", - "APLN", - "HTR5A", - "SST", - "LPAR6", - "AGTR2", - "AGTR1", - "APP", - "MRLN", - "RRH", - "C3AR1", - "DHX34", - "OXER1", - "NTSR1", - "NTSR2", - "PROK2", - "PTGDR", - "PROK1", - "XCR1", - "PPBP", - "AVPR1A", - "NMBR", - "AVPR1B", - "KEL", - "MC1R", - "PNOC", - "CCKBR", - "GPR183", - "PTGFR", - "NPFFR1", - "NPFFR2", - "OXTR", - "LHB", - "LPAR2", - "LPAR3", - "LPAR4", - "LPAR5", - "RXFP1", - "GRPR", - "HTR6", - "RXFP2", - "NLN", - "RXFP3", - "C3", - "HTR4", - "LPAR1", - "RXFP4", - "C5", - "HTR7", - "GALR3", - "GALR2", - "GALR1", - "PCDHA6", - "FFAR4", - "PPY", - "FFAR3", - "PCDHA4", - "FFAR2", - "CMKLR1", - "CCR2", - "GPR17", - "CCL25", - "CCR1", - "EDN1", - "GPR18", - "ANXA1", - "EDN2", - "CCL23", - "EDN3", - "CCL22", - "FFAR1", - "CCL21", - "CCL20", - "FSHB", - "GPBAR1", - "TACR3", - "TACR1", - "TACR2", - "CD200R1", - "MC3R", - "NMS", - "NMU", - "NPY4R2", - "FSHR", - "GNRH2", - "CCL28", - "GNRH1", - "CCL27", - "OPRD1", - "PTGDR2", - "FPR2", - "ADRB1", - "FPR1", - "ADRB2", - "ADRB3", - "FPR3", - "CX3CL1", - "BRS3", - "MC2R", - "CCKAR", - "OPN1LW", - "PENK", - "CCR9", - "GNRHR", - "CCR8", - "RIPK2", - "CCR7", - "CCR6", - "CCR5", - "HCRT", - "OXGR1", - "CCR3", - "NPSR1", - "GPR39", - "P2RY13", - "P2RY14", - "GPR37", - "P2RY12", - "GPR35", - "GPR31", - "GPR4", - "P2RY10", - "GAL", - "GPR143", - "MC5R", - "F2RL1", - "SAA2", - "F2RL2", - "GHRL", - "RGR", - "F2RL3", - "NPB", - "CCL13", - "GPHA2", - "PLPPR1", - "PLPPR2", - "CCL11", - "PLPPR3", - "PTGER1", - "PLPPR4", - "PTGER2", - "PLPPR5", - "PTAFR", - "PTGER3", - "UCMA", - "PTGER4", - "CXCR6", - "CXCR5", - "QRFP", - "NPS", - "GPR132", - "CXCR2", - "CXCR1", - "NPW", - "CXCR4", - "CXCR3", - "GPNMB", - "MC4R", - "NPY", - "RHO", - "CCL19", - "CCL17", - "CCL16", - "PPAN-P2RY11", - "GPR55", - "NPY1R", - "CXCL10", - "CNOT6", - "CXCL11", - "CXCL12", - "MTNR1B", - "XK", - "CXCL13", - "MTNR1A", - "LTB4R2", - "APLNR", - "UTS2", - "GPHB5", - "PF4", - "EEF1AKMT4-ECE2", - "CHRM3", - "CHRM4", - "CHRM1", - "CHRM2", - "GPR68", - "SUCNR1", - "CHRM5", - "GPR65", - "CXCL16", - "PROKR1", - "PROKR2", - "CYSLTR2", - "CYSLTR1", - "CCRL2", - "NLRP3", - "PRLH", - "S1PR2", - "ADORA3", - "KISS1R", - "S1PR1", - "S1PR4", - "ADORA1", - "S1PR3", - "S1PR5", - "GHSR", - "OPN3", - "OPN5", - "OPN4", - "F2R", - "ADRA2C", - "ADRA2B", - "ADRA2A", - "RLN3", - "NMUR2", - "NMUR1", - "RLN2", - "C5AR1", - "PMCH", - "C5AR2", - "MCHR1", - "TTF2", - "MCHR2", - "NTS", - "HCAR3", - "HCAR1", - "HCAR2", - "NPY5R", - "HTR1F", - "AVPR2", - "HTR1D", - "XCL1", - "HTR1E", - "HTR1B", - "OPRM1", - "SSTR1", - "HTR1A", - "SSTR2", - "SSTR3", - "SSTR4", - "PYY", - "SSTR5", - "KISS1", - "ADORA2B", - "ADORA2A", - "XCL2", - "OPN1SW", - "CXCL8", - "LTB4R", - "CCL3L1", - "CXCL9", - "HTR2C", - "TRH", - "CXCL2", - "HTR2A", - "ADRA1D", - "CXCL1", - "OPRL1", - "OXT", - "ADRA1B", - "CXCL3", - "ADRA1A", - "CXCL6", - "CXCL5", - "KNG1", - "HRH2", - "HEBP1", - "P2RY6", - "HRH4", - "TBXA2R", - "HRH3", - "NPBWR1", - "P2RY4", - "BDKRB1", - "NPBWR2", - "GPER1", - "P2RY2", - "P2RY1", - "BDKRB2", - "DRD1", - "DRD2", - "TAC3", - "DRD3", - "DRD4", - "QRFPR", - "TAC1", - "DRD5", - "PRLHR", - "LHCGR", - "PTGIR", - "UTS2R", - "HCRTR1", - "OPRK1", - "TSHR", - "POMC", - "CORT", - "ACKR1", - "OPN1MW3", - "UTS2B", - "ACKR4", - "ACKR3", - "ACKR2", - "SFTPA2", - "AGXT", - "MLNR", - "NPY2R", - "CX3CR1", - "ECE1", - "EDNRA", - "EDNRB", - "CCL7", - "CCL5", - "CCL4", - "CCL3", - "CCL2", - "CCL1", - "INSL3", - "INSL5", - "TSHB" - ], - "module2_reactome_2022_filtered__Class B/2 (Secretin Family Receptors) R-HSA-373080": [ - "PTCH2", - "WNT5A", - "PTCH1", - "WNT9A", - "GCG", - "WNT9B", - "GNG13", - "GNG12", - "WNT16", - "IAPP", - "ADM2", - "UCN", - "GIPR", - "RAMP3", - "SCT", - "VIPR2", - "VIPR1", - "GLP1R", - "PTH2", - "WNT8B", - "ADM", - "IHH", - "FZD10", - "PTH1R", - "WNT8A", - "ADGRE2", - "CRHBP", - "GNG2", - "GNGT2", - "WNT6", - "ADGRE3", - "GNGT1", - "CYSLTR2", - "GNG4", - "WNT11", - "ADGRE1", - "GNG3", - "SHH", - "GNG5", - "GNG8", - "ADGRE5", - "GNG7", - "WNT1", - "WNT2", - "WNT3", - "WNT4", - "FZD2", - "FZD1", - "FZD4", - "WNT10B", - "WNT10A", - "FZD6", - "SMOX", - "WNT3A", - "FZD8", - "GLP2R", - "FZD7", - "WNT7A", - "FZD9", - "WNT7B", - "SCTR", - "PTH2R", - "ADCYAP1", - "PTHLH", - "GNB1", - "GNB3", - "GHRH", - "GNB2", - "GNB5", - "CRH", - "GNAS", - "GNB4", - "PTRH2", - "RAMP1", - "RAMP2", - "CRHR1", - "CALCB", - "CALCRL", - "DHH", - "WNT2B", - "CALCA", - "ADCYAP1R1", - "CRHR2", - "GHRHR", - "CALCR", - "GNG11", - "GNG10", - "UCN3", - "CPAMD8", - "UCN2", - "C1QTNF1", - "CD55" - ], - "module2_reactome_2022_filtered__Class I MHC Mediated Antigen Processing And Presentation R-HSA-983169": [ - "WSB1", - "FBXO2", - "LNPEP", - "ATG14", - "UBAC1", - "PSMB9", - "CDC20", - "MEX3C", - "PSMC6", - "PSMC4", - "PSMC5", - "ASB9", - "CDC16", - "PSMC2", - "PSMC3", - "ASB7", - "CD207", - "ASB8", - "PSMC1", - "ASB5", - "RBCK1", - "ASB6", - "RNF220", - "ASB4", - "TRIM11", - "ASB1", - "ASB2", - "KEAP1", - "CCNF", - "RNF7", - "CBLB", - "RNF6", - "UBE2L6", - "PSMB11", - "UBE2L3", - "PSMB10", - "RNF4", - "HERC6", - "PSMD9", - "RNF114", - "DCAF1", - "FBXL22", - "HERC5", - "CDC23", - "HERC4", - "PSMD7", - "FBXL20", - "FZR1", - "HERC3", - "PSMD8", - "RNF115", - "HERC2", - "PSMD5", - "CDC26", - "HERC1", - "PSMD6", - "CDC27", - "PSMD3", - "PSMD4", - "PSMD2", - "B2M", - "BTRC", - "RNF111", - "SIAH1", - "SIAH2", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "HLA-E", - "HLA-F", - "RNF144B", - "VAMP8", - "PSME4", - "HECW2", - "PSME2", - "BTK", - "PSME3", - "FBXL4", - "FBXL3", - "PSME1", - "DZIP3", - "FBXL5", - "FBXL8", - "VAMP3", - "FBXL7", - "ANAPC13", - "LRR1", - "NEDD4L", - "ANAPC10", - "ANAPC11", - "UBE2J1", - "UBE2J2", - "RNF213", - "RNF217", - "PSMF1", - "ATG7", - "SEC31A", - "SKP1", - "SKP2", - "HACE1", - "SEC13", - "KLHL2", - "GPR75-ASB3", - "KLHL3", - "UBE2G2", - "KLHL5", - "UBE2G1", - "TLR2", - "TLR1", - "KBTBD6", - "KBTBD7", - "KLHL9", - "CALR", - "KBTBD8", - "TLR6", - "TLR4", - "DET1", - "CTSV", - "LRSAM1", - "CTSS", - "SEC14L3", - "SOCS3", - "TRIM9", - "SOCS1", - "SEC14L2", - "CTSL", - "SEC61G", - "TRIM4", - "SEC61B", - "CD36", - "RPS27A", - "UBE2E2", - "UBE4A", - "WWP1", - "UBE2E3", - "UBE2E1", - "TRAIP", - "ITCH", - "MIB2", - "PSMA6", - "SPSB4", - "PSMA7", - "GAN", - "PSMA4", - "SPSB2", - "PSMA5", - "RNF126", - "SPSB1", - "PSMA2", - "PSMA3", - "NEDD4", - "PSMA1", - "CANX", - "TRIP12", - "UBE2V1", - "RLIM", - "UBE2V2", - "RNF123", - "PSMA8", - "SH3RF1", - "PSMB7", - "IKBKB", - "PSMB8", - "PSMB5", - "RNF138", - "PSMB6", - "PSMB3", - "PSMB4", - "MRC1", - "PSMB1", - "MRC2", - "PSMB2", - "ELOB", - "ELOC", - "RNF130", - "ARIH2", - "IKBKG", - "FBXW5", - "MYLIP", - "SEC24A", - "FBXW4", - "UBE2H", - "AREL1", - "FBXW7", - "SEC24C", - "SEC24B", - "FBXW11", - "FBXW9", - "UBE2C", - "FBXW12", - "FBXW8", - "FBXO17", - "S100A1", - "FBXW10", - "UBE2F", - "ZBTB16", - "BTBD6", - "FBXO15", - "KLHL22", - "UBE2A", - "HUWE1", - "KLHL25", - "LMO7", - "UBE2B", - "FBXO11", - "KLHL20", - "RBX1", - "RNF41", - "FBXO10", - "FBXW2", - "KLHL21", - "UBE2W", - "UBE2Z", - "UBOX5", - "UBE2S", - "UBE2U", - "UBE2O", - "RNF182", - "NCF1", - "UBE2K", - "SEC24D", - "S100A9", - "UBE2M", - "S100A8", - "UBE2N", - "PRKN", - "RNF34", - "UBE3D", - "UBE2D3", - "UBE2D4", - "ASB14", - "UBE2D1", - "ASB15", - "UBE2D2", - "UBE3A", - "ASB12", - "UBE3B", - "ASB13", - "UBE3C", - "ASB18", - "RCHY1", - "ASB16", - "ASB17", - "ASB10", - "ASB11", - "RBBP6", - "RNF19B", - "RNF19A", - "STX4", - "PDIA3", - "GLMN", - "LRRC41", - "TIRAP", - "CYBB", - "CYBA", - "FBXO31", - "FBXO32", - "FBXO30", - "MYD88", - "SEM1", - "MKRN1", - "TRAF7", - "UBE2R2", - "UBA3", - "THOP1", - "TPP2", - "UBA1", - "CUL7", - "NCF2", - "CUL5", - "NCF4", - "CUL3", - "TRIM71", - "CUL2", - "KLHL11", - "CUL1", - "FBXO27", - "LTN1", - "KLHL13", - "FBXO22", - "NPEPPS", - "FBXO21", - "UBC", - "BTBD1", - "UBB", - "TRIM69", - "TRIM63", - "DTX3L", - "HSPA5", - "SMURF2", - "SMURF1", - "PJA1", - "TAPBP", - "PJA2", - "BLMH", - "PIK3C3", - "PSMD11", - "TRIM50", - "PSMD10", - "PSMD13", - "PSMD12", - "SAR1B", - "PSMD14", - "UBA7", - "SNAP23", - "UBA6", - "UBA5", - "PIK3R4", - "UBR4", - "HMGB1", - "LY96", - "UBR2", - "FBXO44", - "UBR1", - "FBXO40", - "FBXO41", - "KBTBD13", - "UBE2Q2", - "UBE2Q1", - "FGA", - "LONRF1", - "FGB", - "TRIM41", - "FGG", - "RNF25", - "CHUK", - "ERAP1", - "ERAP2", - "FBXL18", - "NDUFA2", - "FBXL19", - "FBXL16", - "FBXL14", - "FBXL15", - "KCTD6", - "KLHL41", - "FBXL12", - "KCTD7", - "KLHL42", - "FBXL13", - "CDC34", - "ANAPC4", - "TRIM36", - "ANAPC5", - "LNX1", - "TRIM37", - "ANAPC7", - "STUB1", - "TRIM39", - "TRIM32", - "UBA52", - "ANAPC1", - "CBLL2", - "SEC22B", - "ANAPC2", - "BECN1", - "SEC23A", - "MGRN1", - "RNF14", - "ITGB5", - "ZNRF1", - "SEC61A2", - "ZNRF2", - "HECTD1", - "UFL1", - "FCGR1A", - "HECTD2", - "HECTD3", - "SEC61A1", - "VHL", - "FBXO4", - "FBXO7", - "FBXO6", - "ITGAV", - "FBXO9", - "TRIM21", - "UNKL" - ], - "module2_reactome_2022_filtered__Clathrin-mediated Endocytosis R-HSA-8856828": [ - "EGF", - "TOR1A", - "TOR1B", - "FNBP1", - "WNT5A", - "OCRL", - "GAPVD1", - "EREG", - "COPS5", - "NECAP1", - "COPS4", - "CD4", - "COPS6", - "NECAP2", - "COPS3", - "COPS2", - "HIP1R", - "AGTR1", - "STAM2", - "COPS8", - "CHRM2", - "LRP2", - "GPS1", - "ARPC4-TTLL3", - "EGFR", - "FNBP1L", - "LOC112268437", - "TGOLN2", - "NLRP3", - "AP2S1", - "LDLR", - "REPS1", - "REPS2", - "IGF2R", - "FZD4", - "SYT2", - "SYT1", - "M6PR", - "ARPC5", - "STAM", - "SYT9", - "SYT8", - "PICALM", - "ARFGAP1", - "BTC", - "HGS", - "VAMP7", - "VAMP8", - "ARPC3", - "SYNJ2", - "SYNJ1", - "BIN1", - "ARPC2", - "VAMP2", - "GRB2", - "RAB5A", - "AMPH", - "RAB5B", - "VAMP3", - "CFTR", - "IL7R", - "VAMP4", - "TFRC", - "ITSN1", - "SH3KBP1", - "ITSN2", - "COPS7A", - "SLC2A8", - "UBC", - "UBB", - "PIP5K1C", - "AGFG1", - "SH3GL1", - "SH3GL3", - "AP2M1", - "APOB", - "SH3GL2", - "ACTR3", - "ACTR2", - "AVPR2", - "HSPA8", - "SGIP1", - "EPS15L1", - "TACR1", - "DNM1", - "EPN1", - "SNAP91", - "DNM2", - "EPN2", - "DNM3", - "TF", - "DAB2", - "DNAJC6", - "ARPC1A", - "RAB5C", - "CLTC", - "CLTB", - "ADRB2", - "CLTA", - "TGFA", - "NEDD8", - "AREG", - "TARP", - "CD3D", - "GRK2", - "GRK3", - "FCHO2", - "FCHO1", - "CLTCL1", - "DVL2", - "PACSIN3", - "EPS15", - "KIAA0319", - "RPS27A", - "SNX9", - "PACSIN1", - "SLC18A3", - "PACSIN2", - "AP2B1", - "EPGN", - "GAK", - "CTTN", - "SNX18", - "SYT12", - "HBEGF", - "UBA52", - "ARF6", - "TRIP10", - "HIP1", - "SCARB2", - "STON2", - "STON1", - "AP2A2", - "ARRB2", - "WASL", - "AP2A1", - "CBL", - "PIK3C2A", - "ARRB1", - "UBQLN2", - "UBQLN1", - "LDLRAP1" - ], - "module2_reactome_2022_filtered__Collagen Biosynthesis And Modifying Enzymes R-HSA-1650814": [ - "COL28A1", - "COL26A1", - "CRTAP", - "COL24A1", - "P3H3", - "P3H2", - "P3H1", - "COL1A2", - "COL1A1", - "COL3A1", - "P4HA1", - "BMP1", - "COL5A2", - "P4HA2", - "PLOD1", - "COL5A1", - "P4HA3", - "COL5A3", - "COL7A1", - "COL9A2", - "COL22A1", - "COL9A1", - "COL20A1", - "COL9A3", - "PPIB", - "COL18A1", - "COL16A1", - "COL14A1", - "COL12A1", - "PLOD3", - "PLOD2", - "ADAMTS14", - "COL10A1", - "COL27A1", - "COL25A1", - "COL23A1", - "PCOLCE", - "COL2A1", - "COL4A1", - "COL4A3", - "COL6A1", - "COL4A2", - "COL4A5", - "COL6A3", - "COL8A1", - "COL4A4", - "COL6A2", - "COL21A1", - "COL6A5", - "P4HB", - "COL4A6", - "COL8A2", - "TLL2", - "COL6A6", - "PCOLCE2", - "TLL1", - "COL19A1", - "COL17A1", - "COL15A1", - "COL11A2", - "COL13A1", - "ADAMTS3", - "ADAMTS2", - "COL11A1", - "SERPINH1", - "COLGALT1", - "COLGALT2" - ], - "module2_reactome_2022_filtered__Collagen Formation R-HSA-1474290": [ - "CTSB", - "MMP7", - "COL28A1", - "COL26A1", - "CRTAP", - "LAMB3", - "MMP3", - "COL24A1", - "P3H3", - "P3H2", - "MMP9", - "P3H1", - "COL1A2", - "MMP13", - "COL1A1", - "COL3A1", - 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"CFHR4", - "HNRNPC", - "FCN1", - "MASP1", - "CD81", - "C3AR1", - "PROS1", - "C4BPA", - "C4BPB", - "C8A", - "CLU", - "C8B", - "C4A", - "C8G", - "C4B", - "CPNE2", - "CPNE1", - "ELANE", - "CR1", - "CR2", - "AKR1C1", - "IGLL5", - "PSMA7", - "GZMM", - "SERPING1", - "MASP2", - "CD46", - "CFB", - "C1QC", - "FCN2", - "C1QB", - "FCN3", - "C1QA", - "C1R", - "COLEC11", - "COLEC10", - "CPB2", - "C5AR1", - "CFI", - "C1S", - "CFH", - "C5AR2", - "TTF2", - "VTN", - "C3", - "C5", - "CD19", - "CD59", - "MBL2", - "CD55" - ], - "module2_reactome_2022_filtered__Complex I Biogenesis R-HSA-6799198": [ - "ACAD9", - "COA1", - "NDUFC2", - "NDUFC1", - "ECSIT", - "NDUFS8", - "NDUFS7", - "NDUFS6", - "NDUFS5", - "NDUFS4", - "TMEM126B", - "NDUFS3", - "NDUFS2", - "NDUFS1", - "NDUFB9", - "NDUFB8", - "NDUFB11", - "NDUFB7", - "NDUFB6", - "NDUFB5", - "NDUFB10", - "NDUFB4", - "NDUFB3", - "NDUFB2", - "NDUFB1", - "NDUFV3", - "NDUFV2", - "NDUFV1", - "NDUFA9", - "NDUFA8", - "TIMMDC1", - "NDUFA7", - "NDUFA6", - "NUBPL", - "NDUFA5", - 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"PPP2R1A", - "PPP2R5E", - "PPP2R5D", - "VAV1", - "PPP2R5A", - "CD4", - "PIK3CA", - "HLA-DRA", - "CD28", - "CD247", - "PDCD1", - "PTPN6", - "ARHGEF7", - "HLA-DQB2", - "CD86", - "CD80", - "MAPKAP1", - "PIK3R3", - "PIK3R2", - "HOXC13", - "CD3E", - "TARP", - "CD3D", - "AKT3", - "AKT1", - "AKT2", - "GRAP2", - "CTLA4", - "MAP3K8", - "TNFRSF14", - "HLA-DQA2", - "HLA-DQA1", - "ICOS", - "YES1", - "HLA-DRB5", - "PDPK1", - "MTOR", - "RNASE1", - "CDC42", - "PPP2CA", - "LCK", - "PIK3R1", - "PKN1", - "GRB2", - "PKN2", - "HLA-DRB4", - "MAP3K14", - "HLA-DRB3", - "TRIB3", - "HLA-DRB1", - "PRR5", - "CD274", - "SRC", - "PPP2CB", - "PPP2R1B", - "THEM4", - "MLST8", - "HLA-DPA1", - "BTLA", - "CSK", - "FYN", - "RICTOR", - "ICOSLG" - ], - "module2_reactome_2022_filtered__Cyclin A:Cdk2-associated Events At S Phase Entry R-HSA-69656": [ - "CDC25A", - "CDC25B", - "PSMB9", - "PSMC6", - "CDK7", - "RBL2", - "PSMC4", - "PSMC5", - "CDK4", - "PSMC2", - "PSMC3", - "CDK2", - "PSMC1", - "PSMD11", - "PSMD10", - "RB1", - "PSMD13", - "PSMD12", - "LIN9", - "PSMD14", - "CCNH", - "CKS1B", - "PSMB10", - "PSMD9", - "PSMD7", - "FZR1", - "PSMD8", - "PSMD5", - "CCND1", - "PSMD6", - "AKT3", - "PSMD3", - "E2F1", - "MYC", - "PSMD4", - "RBBP4", - "AKT1", - "AKT2", - "PSMD2", - "E2F4", - "E2F5", - "LIN52", - "RPS27A", - "LIN54", - "CABLES1", - "PTK6", - "PSMA6", - "CCNA2", - "PSMA7", - "WEE1", - "CCNA1", - "PSMA4", - "TFDP1", - "PSMA5", - "SEM1", - "TFDP2", - "PSMA2", - "CCNE2", - "PSMA3", - "CCNE1", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "MNAT1", - "CDKN1B", - "MAX", - "CDKN1A", - "LIN37", - "CUL1", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "SKP1", - "SKP2" - ], - "module2_reactome_2022_filtered__Cyclin E Associated Events During G1/S Transition R-HSA-69202": [ - "CDC25A", - "PSMB9", - "PSMC6", - "CDK7", - "RBL2", - "PSMC4", - "PSMC5", - "CDK4", - "PSMC2", - "PSMC3", - "CDK2", - "PSMC1", - "PSMD11", - "PSMD10", - "RB1", - "PSMD13", - 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"EP300", - "POLR2E", - "POLR2F", - "TREX1", - "POLR2H", - "IKBKG", - "TRIM21", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__DDX58/IFIH1-mediated Induction Of Interferon-Alpha/Beta R-HSA-168928": [ - "MAP3K1", - "HSP90AA1", - "ATG12", - "NFKBIB", - "NFKBIA", - "CYLD", - "SIKE1", - "IRF3", - "TAX1BP1", - "IRF7", - "UBE2K", - "APP", - "OTUD5", - "RELA", - "NLRX1", - "UBA7", - "UBE2D3", - "TNFAIP3", - "UBE2D1", - "UBE2D2", - "HMGB1", - "NLRC5", - "HOXC13", - "UBE2L6", - "TANK", - "HERC5", - "CASP8", - "NKIRAS1", - "TKFC", - "NKIRAS2", - "CASP10", - "TRIM4", - "FADD", - "RIPK1", - "RPS27A", - "S100A12", - "CREBBP", - "IFNA14", - "IFNA16", - "CHUK", - "IFNA17", - "TOMM70", - "TRAF3", - "ISG15", - "S100B", - "ITCH", - "NFKB1", - "NFKB2", - "RNF125", - "MAVS", - "TRAF6", - "SAA2", - "UBA52", - "IFNA10", - "RIGI", - "IFNA6", - "IFNA5", - "IFNA8", - "IFNA7", - "HSP90AB1", - "IFNA2", - "IFNA1", - "IFNA4", - "AGER", - "IFIH1", - "IKBKB", - "RNF135", - "PDZD2", - "TBK1", - "UBC", - "RNF216", - "UBB", - "DHX58", - "EP300", - "PCBP2", - "TRIM25", - "IFNB1", - "IKBKG", - "ATG5", - "IFNA21", - "IKBKE" - ], - "module2_reactome_2022_filtered__DNA Damage/Telomere Stress Induced Senescence R-HSA-2559586": [ - "H2AZ2", - "ACD", - "H2AZ1", - "H2BC9", - "H2BC4", - "H2BC5", - "HMGA2", - "H2BC3", - "H2BC1", - "H2BC12L", - "HMGA1", - "H2BC17", - "H2BC13", - "H2BC14", - "H2BC15", - "H2AJ", - "ARTN", - "H2BC11", - "H2BC12", - "CDK2", - "ASF1A", - "ATM", - "MRE11", - "POT1", - "TERF2IP", - "TP53", - "CABIN1", - "RB1", - "LMNB1", - "UBN1", - "H2AB1", - "HIRA", - "EP400", - "H2AC7", - "TINF2", - "H2AC6", - "H2AC4", - "H1-0", - "TERF1", - "H1-2", - "TERF2", - "H1-1", - "RAD50", - "H1-4", - "H1-3", - "H3-4", - "CCNA2", - "H1-5", - "CCNA1", - "CCNE2", - "H2AC20", - "H4C15", - "CCNE1", - "H2AX", - "CDKN1B", - "CDKN1A", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "KAT5" - ], - "module2_reactome_2022_filtered__DNA Double Strand Break Response R-HSA-5693606": [ - "KDM4A", - "KDM4B", - "UBE2I", - "H2BC9", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "SMARCA5", - "H2BC13", - "H2BC14", - "PPP5C", - "H2BC15", - "ARTN", - "H2BC11", - "H2BC12", - "ATM", - "MRE11", - "TP53", - "UBE2N", - "UBXN1", - "ABRAXAS1", - "BRCA1", - "RNF8", - "BABAM2", - "BABAM1", - "HERC2", - "NSD2", - "UIMC1", - "CHEK2", - "APBB1", - "RPS27A", - "KPNA2", - "PIAS4", - "EYA1", - "EYA2", - "EYA3", - "EYA4", - "BAZ1B", - "RAD50", - "CNKSR3", - "H3-4", - "RNF168", - "H4C15", - "UBE2V2", - "UBA52", - "MDC1", - "H2AX", - "BRCC3", - "H2BC26", - "MAPK8", - "UBC", - "H2BC21", - "KAT5", - "SUMO1", - "UBB", - "TP53BP1", - "ABL1", - "BARD1" - ], - "module2_reactome_2022_filtered__DNA Double-Strand Break Repair R-HSA-5693532": [ - "GEN1", - "UBE2I", - "H2BC9", - "H2BC4", - "H2BC5", - "HUS1", - "H2BC3", - "LIG4", - "H2BC1", - "H2BC12L", - "SMARCA5", - "POLE2", - "POLE3", - "POLE4", - "DNA2", - "SLX1A", - "MRE11", - "RHNO1", - "TP53", - "UBE2N", - "DCLRE1C", - "PCNA", - "ABRAXAS1", - "RNF8", - "RNF4", - "SLX4", - "PPP4R2", - "CHEK1", - "HERC2", - "NSD2", - "CHEK2", - "APBB1", - "RBBP8", - "NHEJ1", - "TIPIN", - "KPNA2", - "PIAS4", - "XRCC5", - "XRCC6", - "TOP3A", - "XRCC3", - "XRCC4", - "XRCC1", - "XRCC2", - "BAZ1B", - "CNKSR3", - "CCNA2", - "RNF168", - "CCNA1", - "SEM1", - "H4C15", - "TDP2", - "TDP1", - "RAD17", - "RAD9A", - "RAD9B", - "MDC1", - "PAXIP1", - "BLM", - "MUS81", - "BRCC3", - "PRKDC", - "BRIP1", - "PPP4C", - "EME1", - "CLSPN", - "EME2", - "UBC", - "KAT5", - "UBB", - "TP53BP1", - "ABL1", - "SLC19A1", - "BARD1", - "KDM4A", - "KDM4B", - "RFC4", - "RFC5", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "SIRT6", - "H2BC17", - "PALB2", - "SPIDR", - "H2BC13", - "RAD51C", - "H2BC14", - "PPP5C", - "RAD51B", - "H2BC15", - "RAD51D", - "ARTN", - "H2BC11", - "H2BC12", - "CDK2", - "ATM", - "UBXN1", - "SERPINA2", - "RRAD", - "BRCA1", - "BRCA2", - "RTEL1", - "BABAM2", - "RAD51AP1", - "BABAM1", - "UIMC1", - "TOPBP1", - "RPS27A", - "ATRIP", - "EYA1", - "EYA2", - "PARP2", - "EYA3", - "EYA4", - "PARP1", - "RPA3", - "RAD50", - "H3-4", - "ERCC1", - "RAD51", - "RAD52", - "ERCC4", - "POLR1A", - "POLR1B", - "LRIF1", - "TIMELESS", - "UBE2V2", - "UBA52", - "FEN1", - "H2AX", - "POLD4", - "H2BC26", - "POLE", - "MAPK8", - "WRN", - "EXO1", - "SUMO2", - "H2BC21", - "POLD1", - "SUMO1", - "POLD2", - "POLD3", - "POLK", - "POLM", - "LRIG3", - "POLL", - "POLH" - ], - "module2_reactome_2022_filtered__DNA Repair R-HSA-73894": [ - "H2AZ2", - "ISY1-RAB43", - "H2AZ1", - "HUS1", - "H2BC12L", - "REV1", - "SMARCA5", - "NEIL3", - "ADPRS", - "DNA2", - "NEIL2", - "RHNO1", - "NEIL1", - "ZNF830", - "TP53", - "PARG", - "ABRAXAS1", - "CCNH", - "PNKP", - "RNF8", - "UBE2L6", - "RNF4", - "TFPT", - "PPP4R2", - "CHEK1", - "HERC2", - "NTHL1", - "CHEK2", - "APBB1", - "PCLAF", - "NHEJ1", - "TIPIN", - "FAAP100", - "RNF111", - "AQR", - "XRCC5", - "XRCC6", - "ACTL6A", - "TOP3A", - "XRCC3", - "XRCC4", - "INO80", - "XRCC1", - "XRCC2", - "BAZ1B", - "TERF1", - "TERF2", - "CNKSR3", - "NPLOC4", - "H2AC20", - "TDP2", - "TDP1", - "RAD17", - "RAD18", - "SPRTN", - "MDC1", - "PAXIP1", - "WDR48", - "MUS81", - "XPA", - "BRCC3", - "PRKDC", - "COPS7A", - "XPC", - "H2AC19", - "ALKBH3", - "BRIP1", - "H2AC14", - "PPP4C", - "ALKBH5", - "KAT5", - "ALKBH2", - "TP53BP1", - "SLC19A1", - "BARD1", - "ACD", - "CENPX", - "RMI1", - "MCRS1", - "RMI2", - "SIRT6", - "PALB2", - "RAD51C", - "RAD51B", - "H2AJ", - "PRPF19", - "RAD51D", - "ARTN", - "ATM", - "POT1", - "UBXN1", - "SERPINA2", - "RRAD", - "CETN2", - "ELL", - "BRCA1", - "BRCA2", - "BABAM2", - "RAD51AP1", - "BABAM1", - "UIMC1", - "RPS27A", - "MBD4", - "MLH1", - "MGMT", - "PARP2", - "PARP1", - "CHD1L", - "GTF2H1", - "RPA3", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "DDB2", - "XAB2", - "DDB1", - "APEX1", - "TCEA1", - "TIMELESS", - "UBE2V2", - "FEN1", - "H2AX", - "OGG1", - "UNG", - "WRN", - "EP300", - "UFD1", - "NFRKB", - "REV3L", - "GEN1", - "UBE2I", - "H2BC9", - "USP7", - "H2BC4", - "H2BC5", - "H2BC3", - "LIG4", - "H2BC1", - "MSH2", - "UBE2B", - "RBX1", - "COPS5", - "COPS4", - "COPS6", - "MSH3", - "POLE2", - "UBE2T", - "COPS3", - "FANCD2", - "POLE3", - "COPS2", - "MSH6", - "POLE4", - "SLX1A", - "MRE11", - "COPS8", - "PPIE", - "MAD2L2", - "UBE2N", - "DCLRE1C", - "PCNA", - "DCLRE1B", - "FAAP20", - "MPG", - "GPS1", - "H2AB1", - "RCHY1", - "FAAP24", - "RUVBL1", - "YY1", - "SLX4", - "NSD2", - "POTEF", - "USP1", - "ASCC3", - "RBBP8", - "DCLRE1A", - "HMGN1", - "PMS2", - "KPNA2", - "FANCI", - "PIAS4", - "FANCL", - "H2AC7", - "USP43", - "FANCM", - "USP45", - "H2AC6", - "FANCB", - "FANCA", - "H2AC4", - "FANCC", - "ISG15", - "FANCF", - "PIAS1", - "FANCE", - "PIAS3", - "FANCG", - "CCNA2", - "RNF168", - "CCNA1", - "SEM1", - "H4C15", - "POLG2", - "MNAT1", - "INO80B", - "RAD9A", - "RAD9B", - "INO80D", - "FTO", - "INO80C", - "BLM", - "INO80E", - "VCP", - "UVSSA", - "EME1", - "CLSPN", - "EME2", - "UBC", - "UBB", - "ABL1", - "KDM4A", - "KDM4B", - "RFC4", - "RFC5", - "ACTR8", - "RFC2", - "RFC3", - "ACTR5", - "RAD23B", - "RAD23A", - "H2BC17", - "SPIDR", - "CUL4B", - "H2BC13", - "CUL4A", - "H2BC14", - "PPP5C", - "H2BC15", - "CDK7", - "H2BC11", - "H2BC12", - "CDK2", - "FAN1", - "TERF2IP", - "USP32", - "UBA7", - "RTEL1", - "TOPBP1", - "ATRIP", - "MUTYH", - "EYA1", - "ASCC1", - "EYA2", - "ASCC2", - "EYA3", - "POLN", - "EYA4", - "TINF2", - "RAD50", - "H3-4", - "ERCC1", - "SMUG1", - "RAD51", - "RAD52", - "ERCC4", - "POLR1A", - "ERCC5", - "POLR1B", - "ERCC2", - "ERCC3", - "ERCC8", - "LRIF1", - "DTL", - "ERCC6", - "UBA52", - "CENPS-CORT", - "POLD4", - "H2BC26", - "POLE", - "MAPK8", - "POLR2A", - "EXO1", - "POLR2B", - "SUMO2", - "H2BC21", - "POLD1", - "POLR2C", - "SUMO1", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "TDG", - "POLK", - "POLR2F", - "TRIM25", - "LRIG1", - "POLR2G", - "POLM", - "POLR2H", - "LRIG3", - "POLL", - "POLR2I", - "POLR2J", - "POLR2K", - "POLI", - "POLR2L", - "POLH" - ], - "module2_reactome_2022_filtered__DNA Replication Pre-Initiation R-HSA-69002": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "UBE2C", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "PSMB9", - "PSMC6", - "PSMC4", - "UBE2S", - "POLE2", - "PSMC5", - "CDC16", - "POLE3", - "PSMC2", - "POLE4", - "PSMC3", - "PSMC1", - "UBE2D1", - "H2AB1", - "PSMB10", - "PSMD9", - "CDC23", - "PSMD7", - "FZR1", - "PSMD8", - "PSMD5", - "CDC26", - "PSMD6", - "CDC27", - "PSMD3", - "PSMD4", - "PSMD2", - "KPNA6", - "KPNA1", - "H2AC7", - "H2AC6", - "H2AC4", - "SEM1", - "H2AC20", - "H4C15", - "PSME2", - "PSME3", - "PSME1", - "ANAPC15", - "ANAPC16", - "GMNN", - "ANAPC10", - "ANAPC11", - "H2AC19", - "H2AC14", - "UBC", - "UBB", - "PSMF1", - "CDT1", - "CDC7", - "CDC6", - "H2BC17", - "H2BC13", - "POLA1", - "H2BC14", - "POLA2", - "H2BC15", - "H2AJ", - "H2BC11", - "H2BC12", - "CDK2", - "MCM4", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "MCM10", - "ORC6", - "ORC5", - "ORC4", - "ORC3", - "RPS27A", - "H3-3B", - "H3C7", - "RPA3", - "UBE2E1", - "PSMA6", - "KPNB1", - "PSMA7", - "PSMA4", - "PSMA5", - "DBF4", - "POLR1A", - "PSMA2", - "POLR1B", - "PSMA3", - "RPA4", - "PSMA1", - "ANAPC4", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "SLC25A15", - "H2AX", - "MCM8", - "PRIM2", - "PRIM1", - "PSMB7", - "PSMB8", - "CDC45", - "H2BC26", - "POLE", - "PSMB5", - "PSMB6", - "PSMB3", - "H2BC21", - "PSMB4", - "PSMB1", - "H3C13", - "PSMB2" - ], - "module2_reactome_2022_filtered__DNA Replication R-HSA-69306": [ - "GINS2", - "H2AZ2", - "GINS3", - "GINS1", - "H2AZ1", - "H2BC9", - "UBE2C", - "GINS4", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "UBE2S", - "POLE2", - "PSMC5", - "CDC16", - "POLE3", - "PSMC2", - "POLE4", - "PSMC3", - "PSMC1", - "DNA2", - "PCNA", - "UBE2D1", - "H2AB1", - "PSMB11", - "PSMB10", - "PSMD9", - "CDC23", - "PSMD7", - "FZR1", - "PSMD8", - "PSMD5", - "CDC26", - "PSMD6", - "CDC27", - "PSMD3", - "PSMD4", - "PSMD2", - "KPNA6", - "KPNA1", - "H2AC7", - "H2AC6", - "H2AC4", - "CCNA2", - "CCNA1", - "SEM1", - "H2AC20", - "H4C15", - "PSME4", - "PSME2", - "PSME3", - "PSME1", - "ANAPC15", - "ANAPC16", - "GMNN", - "CUL1", - "ANAPC10", - "ANAPC11", - "H2AC19", - "H2AC14", - "UBC", - "UBB", - "PSMF1", - "SLC19A1", - "SKP1", - "SKP2", - "CDT1", - "RFC4", - "RFC5", - "RFC2", - "RFC3", - "CDC7", - "CDC6", - "H2BC17", - "H2BC13", - "POLA1", - "H2BC14", - "POLA2", - "H2BC15", - "H2AJ", - "H2BC11", - "H2BC12", - "CDK2", - "MCM4", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "MCM10", - "ORC6", - "ORC5", - "ORC4", - "ORC3", - "RPS27A", - "H3-3B", - "H3C7", - "RPA3", - "UBE2E1", - "PSMA6", - "KPNB1", - "PSMA7", - "PSMA4", - "PSMA5", - "DBF4", - "POLR1A", - "PSMA2", - "CCNE2", - "POLR1B", - "PSMA3", - "CCNE1", - "RPA4", - "PSMA1", - "ANAPC4", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "SLC25A15", - "FEN1", - "H2AX", - "MCM8", - "PRIM2", - "PRIM1", - "PSMA8", - "POLD4", - "PSMB7", - "PSMB8", - "CDC45", - "H2BC26", - "POLE", - "PSMB5", - "PSMB6", - "PSMB3", - "H2BC21", - "POLD1", - "PSMB4", - "POLD2", - "PSMB1", - "H3C13", - "POLD3", - "PSMB2", - "LRIG1" - ], - "module2_reactome_2022_filtered__Deadenylation-dependent mRNA Decay R-HSA-429914": [ - "DIS3", - "CNOT11", - "CNOT10", - "CNOT6", - "SKIC8", - "CNOT7", - "CNOT8", - "CNOT1", - "SKIC3", - "CNOT2", - "XRN1", - "CNOT3", - "CNOT4", - "PABPC1", - "CNOT9", - "DCP1B", - "DCP1A", - "EIF4B", - "HBS1L", - "PARN", - "SKIC2", - "EXOSC8", - "EXOSC7", - "EXOSC6", - "EXOSC5", - "EXOSC9", - "NLRP4", - "EIF4E", - "EXOSC4", - "EXOSC3", - "EXOSC2", - "EXOSC1", - "CNOT6L", - "PATL1", - "LSM2", - "LSM1", - "LSM6", - "LSM5", - "LSM4", - "LSM3", - "NT5C3B", - "PAN3", - "LSM7", - "EIF4G1", - "DCP2", - "EIF4A3", - "EIF4A2", - "DDX6", - "EIF4A1", - "TUT7", - "TUT4", - "EDC3", - "EDC4", - "DCPS", - "PAIP1", - "TNKS1BP1" - ], - "module2_reactome_2022_filtered__Death Receptor Signaling R-HSA-73887": [ - "ARHGEF33", - "ARHGEF35", - "ARHGEF38", - "ARHGEF37", - "BAD", - "USP4", - "ARHGEF39", - "MADD", - "OBSCN", - "OTULIN", - "RBCK1", - "SQSTM1", - "NGEF", - "NSMAF", - "RELA", - "PRDM4", - "TNF", - "FGD1", - "TANK", - "RNF31", - "FGD4", - "BCL2L11", - "PREX1", - "USP2", - "MAP3K7", - "ARHGEF40", - "NCSTN", - "ARHGEF12", - "NGFR", - "ARHGEF11", - "PRKCI", - "PRPF38B", - "ARHGEF16", - "ARHGEF15", - "ARHGEF18", - "ARHGEF17", - "TNFRSF10D", - "TRAF1", - "ARHGEF19", - "OMG", - "TNFRSF10B", - "TNFRSF10A", - "NFKB1", - "MYD88", - "MAG", - "FASN", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "BIRC2", - "ARHGEF10", - "BIRC3", - "BEX3", - "MAGED1", - "TRIO", - "ARHGEF26", - "ITSN1", - "SPPL2B", - "SPPL2A", - "PSEN1", - "FASLG", - "PSEN2", - "ABR", - "UBC", - "BAG4", - "UBB", - "TNFSF10", - "USP25", - "PSENEN", - "RHOA", - "CFLAR", - "SORCS3", - "VAV1", - "LINGO1", - "TNFRSF1A", - "VAV2", - "VAV3", - "NFKBIA", - "CYLD", - "ARHGEF4", - "ARHGEF5", - "TAX1BP1", - "ARHGEF2", - "ARHGEF3", - "SOS2", - "ARHGEF9", - "ARHGEF6", - "ARHGEF7", - "SOS1", - "RTN4", - "YWHAE", - "ARHGEF1", - "RASGRF2", - "TRADD", - "OTUD7B", - "TNFAIP3", - "ARHGEF10L", - "KALRN", - "AATF", - "APH1B", - "APH1A", - "CASP8", - "SHARPIN", - "MCF2L", - "CASP10", - "IRAK1", - "CASP2", - "CASP3", - "FADD", - "RIPK2", - "ECT2", - "RIPK1", - "RPS27A", - "PLEKHG5", - "PLEKHG2", - "CHUK", - "MCF2", - "NGF", - "RNASE1", - "TIAM1", - "CLIP3", - "TIAM2", - "ADAM17", - "XIAP", - "UBA52", - "ITGB3BP", - "HDAC3", - "HDAC1", - "HDAC2", - "AKAP13", - "ARHGDIA", - "IKBKB", - "SMPD3", - "MAPK8", - "SMPD2", - "GNA13", - "RACK1", - "IKBKG" - ], - "module2_reactome_2022_filtered__Dectin-1 Mediated Noncanonical NF-kB Signaling R-HSA-5607761": [ - "CCL22", - "FBXW11", - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "UBE2M", - "PSMD11", - "PSMD10", - "RELA", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "RELB", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "RPS27A", - "CHUK", - "NFKB2", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - 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"SMURF2", - "AXIN2", - "AXIN1", - "PSMB9", - "RNF146", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "TNKS", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "TNKS2", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Degradation Of DVL R-HSA-4641258": [ - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "HECW1", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "DVL2", - "PSMD4", - "DVL3", - "PSMD2", - "RPS27A", - "DACT1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "CUL3", - "KLHL12", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Degradation Of GLI1 By Proteasome R-HSA-5610780": [ - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "NUMB", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "RPS27A", - "PRKCA", - "ITCH", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "CUL1", - "GLI1", - "PSMA8", - "PSMB7", - "PRKACG", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "SUFU", - "PSMF1", - "PRKACB", - "SKP1" - ], - "module2_reactome_2022_filtered__Degradation Of GLI2 By Proteasome R-HSA-5610783": [ - "CSNK1A1", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "RPS27A", - "PRKCA", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "CUL1", - "GLI2", - "PSMA8", - "PSMB7", - "PRKACG", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "SUFU", - "PSMF1", - "PRKACB", - "SKP1" - ], - "module2_reactome_2022_filtered__Degradation Of Beta-Catenin By Destruction Complex R-HSA-195253": [ - "TLE5", - "TLE4", - "CSNK1A1", - "TLE3", - "TLE2", - "TLE1", - "AXIN2", - "PPP2R5C", - "AXIN1", - "PPP2R5B", - "PPP2R1A", - "PPP2R5E", - "PPP2R5D", - "PPP2R5A", - 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"SBSPON", - "GALNT3", - "ADAMTS20", - "MUC5AC", - "MUC3B", - "B4GAT1", - "SEMA5B", - "GALNT12", - "SEMA5A", - "POMT1", - "POMT2", - "ADAMTS13", - "ADAMTS12", - "ADAMTS10", - "ADAMTS17", - "ADAMTS16", - "MUC1", - "ADAMTS15", - "MUC2", - "ADAMTS14", - "C1GALT1C1", - "ADAMTS19", - "ADAMTS18", - "DAG1", - "MUCL1", - "MUC4", - "MUC6", - "MUC7", - "MUC5B", - "MUC17", - "C1GALT1", - "MUC19", - "POMGNT1", - "MUC20", - "MUC21", - "NOTCH3", - "NOTCH4", - "NOTCH1", - "SPON2", - "NOTCH2", - "SPON1", - "B3GLCT", - "THBS1", - "THSD1", - "CFP", - "THBS2", - "THSD4", - "ADAMTS5", - "ADAMTSL2", - "ADAMTS6", - "ADAMTSL1", - "LFNG", - "ADAMTS3", - "ADAMTS4", - "ADAMTS1", - "ADAMTS2", - "ADAMTSL5", - "ADAMTSL4", - "ADAMTSL3", - "MUC13", - "MUC15", - "THSD7A", - "MUC16", - "THSD7B", - "ADAMTS9", - "ADAMTS7", - "ADAMTS8", - "LARGE1", - "MUC12" - ], - "module2_reactome_2022_filtered__Diseases Of DNA Repair R-HSA-9675135": [ - "RFC4", - "RFC5", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "HUS1", - "MSH2", - "PALB2", - 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"GSDME", - "H2AZ2", - "H2AZ1", - "H2BC9", - "JUN", - "DNMT3A", - "H2BC4", - "DNMT3B", - "H2BC5", - "CDC25A", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "CDC25B", - "CDC25C", - "H2BC13", - "POLA1", - "H2BC14", - "POLA2", - "H2BC15", - "H2AJ", - "CDK5", - "H2BC11", - "H2BC12", - "TP53", - "APP", - "YWHAE", - "TRADD", - "LMNB1", - "H2AB1", - "FOXO3", - "TANK", - "LMNA", - "BCL2L11", - "CASP8", - "CAPNS2", - "C1QBP", - "CAPNS1", - "RBBP4", - "CAPN1", - "FADD", - "RBBP7", - "CAPN2", - "RIPK1", - "H3-3B", - "H3C7", - "SUZ12", - "EED", - "CDKN2A", - "H2AC7", - "H2AC6", - "H2AC4", - "H2AC20", - "H4C15", - "CDK5R1", - "DNMT1", - "EZH2", - "H2AX", - "PRIM2", - "PRIM1", - "FASLG", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "PRDX2", - "GOLGA2", - "H3C13", - "PRDX1", - "ERC2" - ], - "module2_reactome_2022_filtered__Diseases Of Signal Transduction By Growth Factor Receptors And Second Messengers R-HSA-5663202": [ - "SND1", - "EGF", - "BAD", - "FRS3", - "FRS2", - "DKK1", - "DKK2", - "PSMB9", - "DKK4", - "EML4", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "MAML3", - "ZFYVE9", - "MAML2", - "MAML1", - "ARAF", - "DLL1", - "DLL4", - "NEURL1B", - "PSMD9", - "WDCP", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "TFG", - "AKT3", - "PSMD3", - "MYC", - "PSMD4", - "AKT1", - "AKT2", - "PSMD2", - "NCSTN", - "HES5", - "CREBBP", - "FZD4", - "JAG1", - "JAG2", - "FZD6", - "FZD8", - "ERLIN2", - "BIN2", - "PRKAR1A", - "PSME4", - "NOX4", - "PSME2", - "PSME3", - "PSME1", - "STRN", - "KDM7A", - "DHH", - "WDR48", - "PHB1", - "CTBP2", - "CTBP1", - "PDGFB", - "PDGFA", - "PPFIBP1", - "RBPJ", - "RNF213", - "MLST8", - "MYO18A", - "PLCG1", - "PSMF1", - "ATG7", - "DUSP6", - "LYN", - "CSNK1A1", - "FN1", - "BRAF", - "DUSP9", - "DUSP7", - "DUSP8", - "APBB1IP", - "KAT2B", - "MRAS", - "NCOR1", - "KAT2A", - "NCOR2", - "CEP43", - "SEL1L", - "CLTC", - "ZC3HAV1", - "IQGAP1", - "AREG", - "MECP2", - "ATIC", - "SHH", - "RPS27A", - "ICOS", - "STAT1", - "STAT3", - "SYVN1", - "ADAM10", - "RNASE3", - 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"PPP2R5D", - "TGFBR1", - "ETV6", - "PPP2R5A", - "TGFBR2", - "CLCN6", - "AKT1S1", - "EEF1G", - "MYH9", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "GSK3A", - "PSMD12", - "YWHAB", - "BCL2A1", - "PSMD14", - "PIK3R3", - "PIK3R2", - "TENT4A", - "CASP9", - "ERBB4", - "ERBB2", - "ERLEC1", - "ERBB3", - "FGA", - "FGB", - "TCF7L2", - "FGG", - "PDPK1", - "RPS6KB2", - "FAM131B", - "CAMK2D", - "PRR5", - "CAMK2A", - "CAMK2B", - "DCTN1", - "TNKS2", - "CBL", - "SPRED3", - "SPRED2", - "SPRED1", - "PPP2R1B", - "MAPK3", - "MAPK1", - "TRAT1", - "CAMK2G" - ], - "module2_reactome_2022_filtered__Disorders Of Transmembrane Transporters R-HSA-5619115": [ - "ABCG8", - "SLC12A3", - "SLC35A3", - "SLC35A2", - "SLC16A1", - "SLC12A6", - "SLC12A1", - "SLC35A1", - "NUP214", - "NUP210", - "PSMB9", - "SLC5A7", - "NUP93", - "PSMC6", - "ABCA12", - "NUP98", - "PSMC4", - "SLC9A7", - "PSMC5", - "PSMC2", - "SLC9A9", - "SLCO2A1", - "PSMC3", - "PSMC1", - "RNF185", - "ABCG5", - "NUP205", - "SLC1A1", - "SLC1A3", - "SLC5A1", - "SLC5A2", - "RNF5", - "NUP160", - "PSMB11", - "SLC5A5", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "BSG", - "PSMD5", - "PSMD6", - "NUP85", - "PSMD3", - "PSMD4", - "NLRP3", - "PSMD2", - "NUP88", - "SLC39A4", - "ABCA3", - "SLC34A3", - "ABCA1", - "SLC6A19", - "SLC35C1", - "SLC6A18", - "SLC11A2", - "SLC34A2", - "SLC34A1", - "ERLIN2", - "SLC6A14", - "ERLIN1", - "NUP153", - "AVPR1A", - "AVPR1B", - "NUP155", - "SEM1", - "PSME4", - "PSME2", - "PSME3", - "PSME1", - "SLC27A4", - "CFTR", - "SLC22A5", - "POM121", - "SLC26A3", - "SLC26A2", - "VCP", - "SLC2A1", - "SLC2A2", - "SLC6A2", - "SLC6A20", - "SLC6A3", - "AAAS", - "SLC2A9", - "SLC6A5", - "TPR", - "UBC", - "POM121C", - "UBB", - "NUP62", - "PSMF1", - "ABCC2", - "KCNJ11", - "SEC13", - "ABCC8", - "SLC33A1", - "ABCC9", - "ABCC6", - "AVPR2", - "NDC1", - "NUP133", - "CP", - "NUP50", - "SLC7A7", - "SLC7A9", - "NUP54", - "APOA1", - "NUP58", - "SLC26A4", - "PSMD11", - "ABCB4", - "PSMD10", - "PSMD13", - "SEH1L", - "PSMD12", - "PSMD14", - "SEL1L", - "ABCB6", - "SLC3A1", - "SLC3A2", - "SLC40A1", - "SLCO1B1", - "NUP42", - "NUP43", - "SLC22A18", - "ERLEC1", - "SLCO1B3", - "TAC4", - "RPS27A", - "SLC22A12", - "MAP4K2", - "SLC36A2", - "RANBP2", - "RNASE7", - "SLC2A10", - "PSMA6", - "LMBRD1", - "PSMA7", - "PSMA4", - "ABCB11", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "NUP35", - "UBA52", - "RHAG", - "NUP37", - "SLC29A3", - "NUP107", - "SLC24A5", - "ABCD4", - "SLC24A4", - "SLC20A2", - "SLC24A1", - "DERL2", - "DERL3", - "SLC4A1", - "DERL1", - "NUP188", - "SLC4A4", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "HEPH", - "OS9", - "PSMB4", - "PSMB1", - "PSMB2", - "SLC17A8", - "ABCD1", - "MAP4K5", - "SLC17A5" - ], - "module2_reactome_2022_filtered__Downstream TCR Signaling R-HSA-202424": [ - "FBXW11", - "PSMB9", - "NFKBIA", - "PSMC6", - "CD4", - "PIK3CB", - "PSMC4", - "PIK3CA", - "PSMC5", - "PSMC2", - "PSMC3", - "HLA-DRA", - "PSMC1", - "CD247", - "HLA-DQB2", - "UBE2N", - "PSMD11", - "PSMD10", - "RELA", - "PSMD13", - "PSMD12", - "PSMD14", - "UBE2D1", - "UBE2D2", - "PIK3R2", - "CD3E", - "TARP", - "CD3D", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RIPK2", - "MAP3K7", - "BTRC", - "RPS27A", - "HLA-DQA2", - "HLA-DQA1", - "CARD11", - "HLA-DRB5", - "CHUK", - "PDPK1", - "NFKB1", - "PSMA6", - "PSMA7", - "CDC34", - "LCK", - "PSMA4", - "PSMA5", - "SEM1", - "PIK3R1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "TRAF6", - "PSME2", - "PSME3", - "UBE2V1", - "PRKCQ", - "PSME1", - "TAB2", - "HLA-DRB4", - "UBA52", - "HLA-DRB3", - "HLA-DRB1", - "BCL10", - "CUL1", - "PTEN", - "PSMA8", - "MALT1", - "PSMB7", - "IKBKB", - "INPP5D", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "HLA-DPA1", - "PSMF1", - "TRAT1", - "IKBKG", - "SKP1" - ], - "module2_reactome_2022_filtered__Downstream Signaling Events Of B Cell Receptor (BCR) R-HSA-1168372": [ - "FBXW11", - "NFATC3", - "NFATC2", - "NFATC1", - "PSMB9", - "NFKBIB", - "NFKBIA", - "PPIA", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "REL", - "NFKBIE", - "CALM1", - "PSMD11", - "PSMD10", - "RELA", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PPP3CA", - "PSMD9", - "NRAS", - "PPP3CB", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "MAP3K7", - "BTRC", - "RPS27A", - "CARD11", - "CHUK", - "PRKCB", - "NFKB1", - "PSMA6", - "FKBP1A", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "BCL10", - "CUL1", - "RASGRP3", - "PSMA8", - "MALT1", - "PSMB7", - "IKBKB", - "PPP3R1", - "PSMB8", - "PSMB5", - "RASGRP1", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "HRAS", - "PSMF1", - "IKBKG", - "SKP1" - ], - "module2_reactome_2022_filtered__Drug ADME R-HSA-9748784": [ - "ABCC4", - "ABCC5", - "ABCC2", - "BCHE", - "GLYATL3", - "SLC16A1", - "ABCC3", - "GLYATL2", - "GLYATL1", - "GSTM1", - "UGT2A1", - "VAV1", - "VAV2", - "NME2", - "VAV3", - "CYP2C9", - "CYP2C8", - "ALB", - "GSTA2", - "GSTA1", - "SLC28A3", - "ABCC1", - "SLC28A2", - "ABCG2", - "CES1", - "UGT1A10", - "ABCB1", - "PON1", - "PON3", - "ACY1", - "ADH1A", - "ACSM2A", - "ACSM2B", - "GMPS", - "CYP3A4", - "CNDP2", - "NT5C2", - "SLCO1B1", - "SULT1C4", - "SULT2A1", - "ADAL", - "BSG", - "SLCO1B3", - "UGT2A3", - "UGT2A2", - "PCK1", - "GGT6", - "UGT2B11", - "GGT5", - "UGT2B10", - "GGT7", - "SLC38A1", - "UGT2B15", - "UGT1A1", - "UGT2B17", - "RNASE1", - "LOC107987479", - "IMPDH2", - "HPRT1", - "UGT2B4", - "CYP2E1", - "SLC29A2", - "SLC29A1", - "CES2", - "UGT2B7", - "SLC22A7", - "ACSM4", - "SLC22A1", - "TPMT", - "SLC22A3", - "UGT2B28", - "GSTP1", - "SLC22A2", - "GLYAT", - "ACSM5", - "GSTT1", - "CYP2C19", - "SLCO2B1", - "SULT1A1", - "GUK1", - "SULT1E1", - "UGT1A6", - "UGT1A5", - "UGT1A4", - "UGT3A2", - "XDH", - "UGT1A3", - "UGT3A1", - "GGT1", - "NUDT15", - "SLC38A3", - "UGT1A9", - "SULT1A4", - "UGT1A8", - "UGT1A7" - ], - "module2_reactome_2022_filtered__Dual Incision In TC-NER R-HSA-6782135": [ - "ISY1-RAB43", - "RFC4", - "RFC5", - "USP7", - "RFC2", - "RFC3", - "RBX1", - "CUL4B", - "CUL4A", - "CDK7", - "PRPF19", - "POLE2", - "POLE3", - "POLE4", - "ZNF830", - "PPIE", - "PCNA", - "CCNH", - "RPS27A", - "HMGN1", - "AQR", - "GTF2H1", - "RPA3", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "XAB2", - "DDB1", - "ERCC1", - "ERCC4", - "POLR1A", - "ERCC5", - "POLR1B", - "ERCC2", - "ERCC3", - "ERCC8", - "ERCC6", - "TCEA1", - "UBA52", - "MNAT1", - "UVSSA", - "XPA", - "POLD4", - "POLE", - "POLR2A", - "POLR2B", - "UBC", - "POLD1", - "POLR2C", - "UBB", - "EP300", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "POLK", - "POLR2F", - "POLR2G", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "SLC19A1", - "POLR2L" - ], - "module2_reactome_2022_filtered__E3 Ubiquitin Ligases Ubiquitinate Target Proteins R-HSA-8866654": [ - "H2BC9", - "SHPRH", - "H2BC4", - "H2BC5", - "H2BC3", - "PEX10", - "UBE2A", - "H2BC1", - "PEX12", - "PEX2", - "UBE2B", - "H2BC17", - "PEX13", - "PEX14", - "RNF40", - "H2BC13", - "H2BC14", - "SKIC8", - "H2BC15", - "RRAGA", - "PEX5", - "SELENOS", - "H2BC11", - "H2BC12", - "UBE2N", - "PCNA", - "UBE2D3", - "UBE2D1", - "UBE2D2", - "RTF1", - "CDC73", - "UBE2L3", - "HLTF", - "TMEM129", - "LEO1", - "RNF152", - "RPS27A", - "HLA-A", - "HLA-B", - "UBE2E1", - "RNF20", - "RNF144A", - "RAD18", - "WAC", - "UBE2V2", - "UBA52", - "PAF1", - "VCP", - "RNF181", - "BCL10", - "CTR9", - "PRKDC", - "DERL1", - "UBE2J2", - "UBC", - "UBB" - ], - "module2_reactome_2022_filtered__ECM Proteoglycans R-HSA-3000178": [ - "TGFB1", - "LUM", - "TGFB3", - "TGFB2", - "ITGA2", - "LAMB1", - "VCAN", - "DMTF1", - "COL9A2", - "ITGA9", - "COL9A1", - "ITGA8", - "ITGA7", - "COL9A3", - "AGRN", - "HAPLN1", - "PTPRS", - "TNXB", - "NCAM1", - "ITGA2B", - "SERPINE1", - "LAMC1", - "LRP4", - "LMNB2", - "COMP", - "TNN", - "TNR", - "DAG1", - "MUSK", - "BGN", - "DSPP", - "DCN", - "ASPN", - "BCAN", - "HSPG2", - "MATN1", - "FMOD", - "MATN4", - "MATN3", - "ITGB1", - "SPARC", - "ITGB6", - "ITGB5", - "LAMA2", - "NCAN", - "LAMA5", - "ITGB3", - "LAMA4", - "LAMA1", - "VTN", - "ACAN", - "IBSP", - "ITGAX", - "TNNI3", - "ITGAV" - ], - "module2_reactome_2022_filtered__EML4 And NUDC In Mitotic Spindle Formation R-HSA-9648025": [ - "KNL1", - "PPP2R1A", - "CDC20", - "EML4", - "DYNC1LI1", - "SGO2", - "DYNC1LI2", - "ZW10", - "SGO1", - "NUP98", - "BUB1B", - "B9D2", - "MAD2L1", - "ZWILCH", - "PMF1-BGLAP", - "NUP160", - "AURKB", - "PPP1CC", - "NUP85", - "CLASP1", - "KNTC1", - "CLASP2", - "NEK9", - "PLK1", - "NEK6", - "NEK7", - "CKAP5", - "NDC80", - "RPS27", - "KIF18A", - "KIF2B", - "KIF2A", - "BIRC5", - "KIF2C", - "MAD1L1", - "CDCA8", - "NDEL1", - "RCC2", - "CENPU", - "NUDC", - "SEC13", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "ZWINT", - "PPP2R5A", - "CENPE", - "CENPF", - "CENPH", - "CENPI", - "INCENP", - "CENPK", - "CENPL", - "CENPM", - "PAFAH1B1", - "CENPN", - "CENPO", - "CENPP", - "CENPQ", - "CENPT", - "SKA1", - "SEH1L", - "AHCTF1", - "SKA2", - "DSN1", - "NUP43", - "DYNC1I2", - "DYNC1I1", - "RANBP2", - "DYNLL1", - "NDE1", - "DYNLL2", - "PPP2CA", - "RANGAP1", - "SPC24", - "CLIP1", - "TAOK1", - "DYNC1H1", - "NUP37", - "SPC25", - "CENPS-CORT", - "ITGB3BP", - "MAPRE1", - "NUP107", - "ERCC6L", - "MIS12", - "KANSL1", - "CENPA", - "PPP2CB", - "XPO1", - "PPP2R1B", - "NUF2", - "BUB3", - "SPDL1", - "BUB1" - ], - "module2_reactome_2022_filtered__EPH-Ephrin Signaling R-HSA-2682334": [ - "EPHA6", - "ACTR3", - "EPHA5", - "LYN", - "PSENEN", - "RHOA", - "ACTR2", - "EPHA8", - "EPHA7", - "LIMK1", - "MMP2", - "LIMK2", - "MMP9", - "DNM1", - "VAV2", - "VAV3", - "MYL6", - "GPRIN1", - "MYH9", - "EPHA2", - "MYL9", - "EPHA1", - "NGEF", - "EPHA4", - "ARHGEF7", - "EPHA3", - "ARPC1A", - "ROCK1", - "ROCK2", - "ARPC1B", - "CLTC", - "CLTB", - "CLTA", - "ARPC4-TTLL3", - "EFNA5", - "KALRN", - "MYL12B", - "APH1B", - "EFNB3", - "APH1A", - "EFNB2", - "CLTCL1", - "EFNB1", - "MYH14", - "AP2S1", - "POTEF", - "MYH10", - "MYH11", - "NCSTN", - "YES1", - "AP2B1", - "ADAM10", - "ARPC5", - "RNASE1", - "SDCBP", - "TIAM1", - "CDC42", - "PTK2", - "EFNA2", - "EFNA1", - "MYL12A", - "EFNA4", - "EFNA3", - "ARPC3", - "ARPC2", - "PKN1", - "SDC2", - "PKN2", - "EPHB6", - "SRC", - "EPHA10", - "ITSN1", - "ARHGEF28", - "ACTG1", - "AP2A2", - "PSEN1", - "WASL", - "AP2A1", - "PSEN2", - "GRIN2B", - "SYNGAP1", - "VPS72", - "EPHB1", - "HRAS", - "FYN", - "EPHB3", - "EPHB2", - "AP2M1", - "EPHB4", - "GIT1", - "NCK2" - ], - "module2_reactome_2022_filtered__ER To Golgi Anterograde Transport R-HSA-199977": [ - "CFAP73", - "SEC24A", - "SEC24C", - "ANKRD28", - "SEC24B", - "TRAPPC10", - "DYNC1LI1", - "DYNC1LI2", - "CNIH1", - "CNIH2", - "CAPZA2", - "CAPZA3", - "TBC1D20", - "CAPZA1", - "PREB", - "CNIH3", - "SEC24D", - "RAB1A", - "RAB1B", - "KDELR1", - "KDELR2", - "STX17", - "NAPG", - "CSNK1D", - "TFG", - "STX5", - "YKT6", - "TRAPPC2L", - "TRAPPC3", - "NSF", - "TRAPPC4", - "TRAPPC1", - "COG8", - "NAPSA", - "TMED9", - "TRAPPC2", - "COG7", - "LMAN1L", - "COG6", - "COG5", - "TRAPPC5", - "COG4", - "COG3", - "BET1L", - "COG2", - "ANK3", - "COG1", - "TRAPPC9", - "ANK1", - "ANK2", - "ARFGAP2", - "COPZ2", - "ARFGAP3", - "COPZ1", - "ARFGAP1", - "TMEM115", - "GOLGB1", - "GORASP1", - "COPA", - "GRIA1", - "COPB2", - "COPB1", - "LMAN2L", - "GOLGA2", - "SEC31A", - "SEC13", - "PTGES2", - "SEC16B", - "SEC16A", - "SPTB", - "COL7A1", - "COPG1", - "COPG2", - "ACTR10", - "COPE", - "ARF4", - "SPTBN4", - "ARF5", - "MCFD2", - "SPTBN5", - "SERPINA1", - "ARF3", - "SAR1B", - "CTSZ", - "TGFA", - "AREG", - "ACTR1A", - "PPP6C", - "SPTA1", - "SCFD1", - "PPP6R1", - "PPP6R3", - "CAPZB", - "TMED3", - "TMED2", - "SPTBN1", - "TMED7", - "DYNC1I2", - "SPTBN2", - "CTSC", - "DYNC1I1", - "SEC22A", - "DYNLL1", - "DYNLL2", - "ARCN1", - "F5", - "F8", - "TRAPPC6B", - "SEC23IP", - "TRAPPC6A", - "USO1", - "KDELR3", - "SEC22C", - "FOLR2", - "SEC22B", - "DYNC1H1", - "BET1", - "DCTN6", - "SEC23A", - "DCTN3", - "GOSR1", - "DCTN2", - "DCTN5", - "DCTN4", - "GOSR2", - "MIA3", - "DCTN1", - "INS", - "LMAN1", - "LMAN2", - "CD59", - "SPTAN1", - "CD55" - ], - "module2_reactome_2022_filtered__ER-Phagosome Pathway R-HSA-1236974": [ - "S100A1", - "PSMB9", - "TAPBP", - "TLR2", - "TLR1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "CALR", - "TLR6", - "S100A9", - "TLR4", - "S100A8", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "SNAP23", - "HMGB1", - "LY96", - "PSMB11", - "PSMB10", - "PSMD9", - "SEC14L3", - "PSMD7", - "PSMD8", - "SEC14L2", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "SEC61G", - "SEC61B", - "PSMD2", - "STX4", - "B2M", - "CD36", - "RPS27A", - "FGA", - "FGB", - "PDIA3", - "FGG", - "CHUK", - "HLA-C", - "NDUFA2", - "TIRAP", - "HLA-A", - "HLA-B", - "HLA-G", - "HLA-E", - "HLA-F", - "PSMA6", - "MYD88", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "VAMP8", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "BTK", - "PSME3", - "PSME1", - "UBA52", - "SEC22B", - "VAMP3", - "PSMA8", - "PSMB7", - "IKBKB", - "PSMB8", - "SEC61A2", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "SEC61A1", - "PSMB2", - "PSMF1", - "IKBKG" - ], - "module2_reactome_2022_filtered__ESR-mediated Signaling R-HSA-8939211": [ - "H2AZ2", - "EGF", - "TLE3", - "H2AZ1", - "H2BC9", - "H2BC4", - "H2BC5", - "RUNX1", - "H2BC3", - "GTF2F1", - "H2BC1", - "H2BC12L", - "GTF2F2", - "EREG", - "CXCL12", - "FKBP5", - "DAND5", - "FKBP4", - "CALM1", - "PPID", - "KANK1", - "KDM1A", - "CCNT1", - "H2AB1", - "EGFR", - "FOXO3", - "GNG2", - "UHMK1", - "NRAS", - "YY1", - "GNG4", - "GNG3", - "GNAT3", - "MYB", - "CCND1", - "GNG5", - "AKT3", - "GNG8", - "GNG7", - "MYC", - "AKT1", - "AKT2", - "S1PR3", - "KPNA2", - "CREBBP", - "JUND", - "H2AC7", - "H2AC6", - "H2AC4", - "ESR1", - "ESR2", - "PTK2", - "MOV10", - "BTC", - "CREB1", - "PRKCZ", - "STAG2", - "GNB1", - "PIK3R1", - "GDNF", - "H2AC20", - "H4C15", - "GNB3", - "GNB2", - "GNB5", - "GNB4", - "PGR", - "STRN", - "DDX5", - "CBFB", - "GATA3", - "IGF1R", - "H2AC19", - "SMC3", - "GNG11", - "H2AC14", - "GNG10", - "KAT5", - "PMEPA1", - "NCOA2", - "KDM4B", - "NCOA3", - "MMP7", - "POU2F1", - "HSP90AA1", - "JUN", - "NCOA1", - "MMP2", - "PTGES3", - "CAV2", - "GREB1", - "MMP3", - "CAV1", - "SPHK1", - 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"LUM", - "CMA1", - "PHYKPL", - "ACTN1", - "COL24A1", - "P3H3", - "P3H2", - "OPTC", - "P3H1", - "CAPN14", - "COL1A2", - "CAPN15", - "COL1A1", - "CAPN12", - "CAPN13", - "CAPN10", - "CAPN11", - "COL9A2", - "COL9A1", - "COL9A3", - "PRSS2", - "COL12A1", - "SERPINE1", - "LRP4", - "NTN4", - "FZR1", - "BSG", - "CAPNS2", - "SPOCK3", - "CAPNS1", - "DAG1", - "KDR", - "MUSK", - "NCSTN", - "ELANE", - "COL25A1", - "GDF5", - "PRKCA", - "COL2A1", - "BCAN", - "HSPG2", - "SDC1", - "SDC2", - "SPARC", - "COL13A1", - "ICAM3", - "ICAM4", - "PDGFB", - "ICAM5", - "PDGFA", - "F11R", - "ICAM1", - "ICAM2", - "SERPINH1", - "TPSAB1", - "CTSB", - "FN1", - "MMP11", - "MMP10", - "MMP13", - "MMP12", - "MMP15", - "CEACAM1", - "COL3A1", - "MMP14", - "P4HA1", - "CEACAM8", - "MMP17", - "P4HA2", - "PXDN", - "MMP16", - "P4HA3", - "CEACAM6", - "MMP19", - "COL22A1", - "KLKB1", - "COL18A1", - "DDR2", - "DDR1", - "PTPRS", - "KLK2", - "TMPRSS6", - "SDC3", - "SDC4", - "CTSV", - "CTSS", - "KLK7", - "SH3PXD2A", - "CTSL", - "CTSK", - 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"CD151", - "LTBP3", - "PSEN1", - "HTRA1", - "LTBP4", - "LTBP1", - "VTN", - "EFEMP1", - "MMP24", - "EFEMP2", - "MMP28", - "IBSP", - "ITGA1", - "MMP7", - "MMP8", - "ITGA5", - "MMP1", - "COL26A1", - "ITGA4", - "LAMB3", - "MMP2", - "ITGA3", - "MMP3", - "ITGA2", - "LAMB1", - "MMP9", - "BMP7", - "BMP4", - "MFAP5", - "VCAN", - "BMP2", - "BMP1", - "ITGAE", - "MFAP2", - "ITGAD", - "MFAP4", - "COL7A1", - "MFAP3", - "ITGA9", - "ITGA8", - "PECAM1", - "ITGA7", - "ITGA6", - "PLEC", - "FBN3", - "HAPLN1", - "SMIM6", - "NID1", - "COL14A1", - "BMP10", - "NCAM1", - "NRXN1", - "LAMC1", - "LAMC3", - "LAMC2", - "NID2", - "LMNB2", - "ADAMTS16", - "ADAMTS14", - "ADAMTS18", - "CASP3", - "MADCAM1", - "FGA", - "FGB", - "COL27A1", - "FGG", - "DST", - "CTRB2", - "PCOLCE", - "DCN", - "ASPN", - "COL8A1", - "CTRB1", - "P4HB", - "COL8A2", - "TLL2", - "PCOLCE2", - "TLL1", - "ITGB2", - "ITGB1", - "ITGB6", - "COL15A1", - "FURIN", - "ITGB5", - "LAMA2", - "ITGB4", - "LAMA5", - "ITGB3", - "LAMA4", - "CXXC1", - "LAMA1", - "THBS1", - "PLG", - "ITGAM", - "ITGAL", - "ACAN", - "ADAMTS5", - "TTR", - "ADAMTS3", - "ADAMTS4", - "ADAMTS1", - "ADAMTS2", - "ITGAX", - "TNNI3", - "ITGB8", - "ERC2", - "ITGAV", - "ITGB7", - "ADAMTS9", - "COLGALT1", - "COLGALT2", - "ADAMTS8" - ], - "module2_reactome_2022_filtered__FBXL7 Down-Regulates AURKA During Mitotic Entry And In Early Mitosis R-HSA-8854050": [ - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "AURKA", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "FBXL18", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "FBXL7", - "CUL1", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "SKP1" - ], - "module2_reactome_2022_filtered__FCERI Mediated NF-kB Activation R-HSA-2871837": [ - "LYN", - "FBXW11", - "PSMB9", - "NFKBIA", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "UBE2N", - "PSMD11", - "PSMD10", - "RELA", - "PSMD13", - "PSMD12", - "PSMD14", - "UBE2D1", - "UBE2D2", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "MAP3K7", - "BTRC", - "RPS27A", - "CARD11", - "CHUK", - "PDPK1", - "NFKB1", - "IGLL5", - "PSMA6", - "PSMA7", - "CDC34", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "TRAF6", - "PSME2", - "PSME3", - "TAB3", - "UBE2V1", - "PRKCQ", - "PSME1", - "TAB2", - "TAB1", - "UBA52", - "BCL10", - "CUL1", - "RASGRP2", - "PSMA8", - "MALT1", - "RASGRP4", - "PSMB7", - "IKBKB", - "PSMB8", - "PSMB5", - "RASGRP1", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "IKBKG", - "SKP1" - ], - "module2_reactome_2022_filtered__FCGR3A-mediated Phagocytosis R-HSA-9664422": [ - "ACTR3", - "LYN", - "ACTR2", - "BAIAP2", - "VAV1", - "VAV2", - "VAV3", - "MYH2", - "ABI1", - "ABI2", - "ELMO1", - "ELMO2", - "MYH9", - "CD247", - "DOCK1", - "ARPC1A", - "CYFIP2", - "CYFIP1", - "ARPC1B", - "BRK1", - "ARPC4-TTLL3", - "TARP", - "NCKIPSD", - "POTEF", - "WASF1", - "WASF2", - "WASF3", - "YES1", - "NCKAP1L", - "SYK", - "MYO10", - "LOC124905743", - "MYO5A", - "ARPC5", - "MYO9B", - "HCK", - "RNASE1", - "CDC42", - "PTK2", - "IGLL5", - "MYO1E", - "FGR", - "ARPC3", - "ARPC2", - "BTK", - "CRK", - "GRB2", - "SRC", - "WIPF1", - "WIPF2", - "NCKAP1", - "WIPF3", - "ACTG1", - "WAS", - "WASL", - "MAPK3", - "MAPK1", - "ABL1", - "FYN", - "NCK1" - ], - "module2_reactome_2022_filtered__FOXO-mediated Transcription R-HSA-9614085": [ - "TRIM63", - "REXO2", - "CAV1", - "G6PC1", - "SIRT1", - "SIRT3", - "KAT2B", - "PINK1", - "RBL2", - "BCL6", - "CCNG2", - "MSTN", - "AGRP", - "YWHAG", - "BTG1", - "CITED2", - "YWHAB", - "FOXG1", - "FOXO6", - "RETN", - "FOXO4", - "FOXO3", - "FOXO1", - "BBC3", - "ATXN3", - "BCL2L11", - "AKT3", - "YWHAQ", - "AKT1", - "AKT2", - "PPARGC1A", - "PCK1", - "MAP4K2", - "SMAD3", - "SREBF1", - "CREBBP", - "IGFBP1", - "SMAD2", - "SMAD4", - "ABCA6", - "NFYB", - "NFYC", - "GADD45A", - "YWHAZ", - "KLF4", - "FBXO32", - "POMC", - "TXNIP", - "DDIT3", - "TXN2", - "CDKN1B", - "HDAC1", - "CDKN1A", - "HDAC2", - "GLYAT", - "FASLG", - "INS", - "NR3C1", - "STK11", - "PCBP4", - "EP300", - "SIN3A", - "NPY", - "NFYA", - "PLXNA4" - ], - "module2_reactome_2022_filtered__Factors Involved In Megakaryocyte Development And Platelet Production R-HSA-983231": [ - "CARMIL1", - "HBE1", - "KIF16B", - "CAPZA2", - "KIF4B", - "KIF4A", - "CAPZA1", - "KIF20A", - "KIF20B", - "TP53", - "AKAP1", - "KDM1A", - "DOCK11", - "KIF5A", - "DOCK10", - "MYB", - "RACGAP1", - "KIF5C", - "KIF5B", - "MFN2", - "SH2B1", - "SH2B3", - "MFN1", - "SH2B2", - "NFE2", - "HBG1", - "IFNA14", - "IFNA16", - "KIF22", - "HBG2", - "IFNA17", - "KIF25", - "KIF23", - "PRKCA", - "EHD1", - "KIF27", - "EHD2", - "MAFF", - "EHD3", - "KIF18A", - "KIF18B", - "ZFPM1", - "KIF2B", - "KIF2A", - "KIFC2", - "PRKAR1B", - "KIFC1", - "PRKAR1A", - "MAFG", - "KIF2C", - "MAFK", - "RAB5A", - "ZFPM2", - "IFNA10", - "RCOR1", - "DOCK7", - "DOCK6", - "DOCK5", - "DOCK4", - "DOCK9", - "DOCK8", - "AK3", - "JMJD1C", - "GATA6", - "PHF21A", - "GATA5", - "GATA4", - "GATA3", - "GATA2", - "GATA1", - "PRKACG", - "KIF3C", - "KIF3B", - "KIF3A", - "PRKAR2B", - "MICAL1", - "PRKAR2A", - "KIF21A", - "KIF21B", - "ABL1", - "KIFAP3", - "PRKACB", - "IFNA21", - "CBX5", - "ITPK1", - "RBSN", - "KIF6", - "CENPE", - "RAD51C", - "IRF1", - "RAD51B", - "KIF9", - "CDK5", - "IRF2", - "CDK2", - "DOCK3", - "DOCK2", - "DOCK1", - "KIF11", - "HBD", - "KIF15", - "KIF12", - "KIF19", - "KLC1", - "KLC4", - "KLC3", - "KIF1A", - "CAPZB", - "KIF1C", - "KIF1B", - "H3-3B", - "H3C7", - "CABLES1", - "CABLES2", - "RNASE1", - "CDC42", - "WEE1", - "KIF26A", - "KIF26B", - "VPS45", - "IFNA6", - "IFNA5", - "IFNA8", - "IFNA7", - "HDAC1", - "IFNA2", - "HDAC2", - "IFNA1", - "IFNA4", - "SIN3A", - "H3C13", - "KIF13B", - "HMG20B", - "IFNB1", - "AKAP10", - "JAK2" - ], - "module2_reactome_2022_filtered__Fatty Acid Metabolism R-HSA-8978868": [ - "ACOT9", - "MECR", - "ACOT8", - "PHYH", - "ACSL3", - "ACSL1", - "ACSL6", - "ACSL5", - "ACSL4", - "TECRL", - "PRKAB2", - "CYP2C9", - "SCD", - "CYP2C8", - "CYP2U1", - "MAPKAPK2", - "CYP1A2", - "DPEP3", - "ACOT2", - "CYP1A1", - "DPEP2", - "ACOT1", - "DPEP1", - "ACOT7", - "ACOT6", - "CRAT", - "ACOT4", - "PRKAA2", - "SLC27A2", - "PON1", - "HPGD", - "PON3", - "PON2", - "HSD17B4", - "ALOX12B", - "HSD17B8", - "HPGDS", - "RXRA", - "ALOX5", - "HSD17B3", - "CYP1B1", - "AWAT1", - "MUSK", - "MORC2", - "ALOXE3", - "PTGDS", - "ACBD7", - "GGT5", - "ACBD6", - "ACBD5", - "CYP4F2", - "CYP4F3", - "EPHX2", - "PLA2G4A", - "CYP4F11", - "CYP4F8", - "MCAT", - "ACSF3", - "ALOX15B", - "ACSF2", - "ACLY", - "FA2H", - "ALDH3A2", - "AMACR", - "FASN", - "PRXL2B", - "TBXAS1", - "PPT1", - "ACSBG2", - "ACSBG1", - "ACBD4", - "SLC27A3", - "PTGES", - "SLC22A5", - "HACD2", - "HACD1", - "ACSM3", - "ECI1", - "ECI2", - "PECR", - "ACSM6", - 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"ABCD1", - "ACADS", - "FADS2", - "CBR4" - ], - "module2_reactome_2022_filtered__Fc Epsilon Receptor (FCERI) Signaling R-HSA-2454202": [ - "MAP3K1", - "FBXW11", - "PSMB9", - "TEC", - "PSMC6", - "MAPK10", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "SPNS1", - "CALM1", - "UBE2N", - "AHCYL1", - "RELA", - "UBE2D1", - "UBE2D2", - "PSMB11", - "PSMB10", - "PSMD9", - "NRAS", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "GRAP2", - "PSMD2", - "MAP3K7", - "BTRC", - "MAP2K4", - "CARD11", - "NFKB1", - "SEM1", - "PIK3R1", - "PSME4", - "TRAF6", - "PSME2", - "BTK", - "PSME3", - "TAB3", - "GRB2", - "PRKCQ", - "PSME1", - "TAB2", - "TAB1", - "TXK", - "BCL10", - "CUL1", - "RASGRP2", - "RASGRP4", - "PPP3R1", - "RASGRP1", - "UBC", - "UBB", - "PLCG1", - "PSMF1", - "PLCG2", - "SKP1", - "MAP2K7", - "LYN", - "JUN", - "GAB2", - "NFATC3", - "NFATC2", - "NFATC1", - "VAV1", - "VAV2", - "VAV3", - "NFKBIA", - "SLC7A8", - "PIK3CB", - "PIK3CA", - "LCP2", - "MS4A2", - "ITK", - "SOS1", - 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"SPINK5", - "CDSN", - "SPINK9", - "JUP", - "SPINK6", - "KLK14", - "KAZN", - "KLK12", - "KLK13", - "LCE4A", - "DSG1", - "PKP3", - "DSG2", - "PKP2", - "DSG3", - "LORICRIN", - "DSG4", - "PKP4", - "DSC3", - "PKP1", - "SPRR2F", - "SPRR2G", - "SPRR3", - "KLK5", - "EVPL", - "PCSK6", - "KLK8", - "LCE1B", - "LCE1C", - "CASP14", - "LCE1A", - "CAPNS1", - "LCE5A", - "PERP", - "PRSS8", - "CAPN1", - "SPRR2A", - "SPRR2B", - "SPRR2D", - "SPRR2E", - "CELA2A", - "ST14", - "LCE1F", - "DSPP", - "LCE1D", - "LCE1E", - "LCE2C", - "RCAN1", - "LCE2D", - "LCE2A", - "LCE2B", - "LCE6A", - "SPRR1A", - "SPRR1B", - "IVL", - "FLG", - "CSTA", - "FURIN", - "TCHH", - "PPL", - "LCE3D", - "LCE3E", - "RPTN", - "TGM1", - "LCE3B", - "LCE3C", - "LIPK", - "LCE3A", - "LIPJ", - "PI3", - "LIPN", - "LIPM", - "LELP1", - "TGM5" - ], - "module2_reactome_2022_filtered__Formation Of Ternary Complex, And Subsequently, 43S Complex R-HSA-72695": [ - "RPS8", - "RPS9", - "EIF1AX", - "RPS6", - "RPS7", - "RPS3A", - "RPSA", - "FAU", - "RPS27L", - 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"INSL3", - "TSHB" - ], - "module2_reactome_2022_filtered__G-protein Mediated Events R-HSA-112040": [ - "CAMK4", - "CALM1", - "AHCYL1", - "ADCY1", - "ADCY5", - "ITPR2", - "ADCY4", - "ITPR3", - "ADCY2", - "PRKX", - "ADCY9", - "ADCY8", - "ADCY7", - "ADCY6", - "ITPR1", - "GRK2", - "GNAT3", - "KPNA2", - "PRKCG", - "PRKCD", - "PLA2G4A", - "NBEA", - "PRKCA", - "GNAL", - "CREB1", - "PLCB4", - "PRKAR1B", - "PRKAR1A", - "GNAQ", - "PLCB2", - "PLCB3", - "PLCB1", - "CAMK2D", - "PDE1C", - "CAMK2A", - "PDE1B", - "CAMK2B", - "PDE1A", - "CAMKK1", - "GNAI1", - "CAMKK2", - "GNAI2", - "GNAI3", - "GNA14", - "GNA15", - "PRKACG", - "GNA11", - "PRKAR2B", - "PRKAR2A", - "MAPK1", - "CAMK2G", - "PRKACB" - ], - "module2_reactome_2022_filtered__G1/S DNA Damage Checkpoints R-HSA-69615": [ - "CDC25A", - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "CDK2", - "ATM", - "PSMC1", - "TP53", - "PSMD11", - "PSMD10", - "PHF20", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - 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"GEN1", - "RFC4", - "RFC5", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "HUS1", - "PALB2", - "SPIDR", - "RAD51C", - "RAD51B", - "RAD51D", - "ARTN", - "POLE2", - "POLE3", - "POLE4", - "ATM", - "DNA2", - "SLX1A", - "MRE11", - "RHNO1", - "PCNA", - "SERPINA2", - "RRAD", - "BRCA1", - "BRCA2", - "RTEL1", - "RAD51AP1", - "SLX4", - "CHEK1", - "TOPBP1", - "RBBP8", - "RPS27A", - "ATRIP", - "TOP3A", - "XRCC3", - "RPA3", - "XRCC2", - "RAD50", - "RAD51", - "SEM1", - "POLR1A", - "POLR1B", - "RAD17", - "UBA52", - "RAD9A", - "RAD9B", - "BLM", - "MUS81", - "POLD4", - "BRIP1", - "POLE", - "EME1", - "WRN", - "EME2", - "EXO1", - "UBC", - "KAT5", - "POLD1", - "UBB", - "POLD2", - "POLD3", - "POLK", - "SLC19A1", - "BARD1", - "POLH" - ], - "module2_reactome_2022_filtered__HDR Thru Homologous Recombination (HRR) Or Single Strand Annealing (SSA) R-HSA-5693567": [ - "GEN1", - "UBE2I", - "H2BC9", - "H2BC4", - "H2BC5", - "HUS1", - "H2BC3", - "H2BC1", - "H2BC12L", - "POLE2", - "POLE3", - "POLE4", - "DNA2", - "SLX1A", - "MRE11", - "RHNO1", - "UBE2N", - "PCNA", - "ABRAXAS1", - "RNF8", - "RNF4", - "SLX4", - "PPP4R2", - "CHEK1", - "HERC2", - "NSD2", - "RBBP8", - "TIPIN", - "PIAS4", - "TOP3A", - "XRCC3", - "XRCC2", - "CCNA2", - "RNF168", - "CCNA1", - "SEM1", - "H4C15", - "RAD17", - "RAD9A", - "RAD9B", - "MDC1", - "BLM", - "MUS81", - "BRCC3", - "BRIP1", - "PPP4C", - "EME1", - "CLSPN", - "EME2", - "UBC", - "KAT5", - "UBB", - "TP53BP1", - "ABL1", - "SLC19A1", - "BARD1", - "RFC4", - "RFC5", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "SIRT6", - "H2BC17", - "PALB2", - "SPIDR", - "H2BC13", - "RAD51C", - "H2BC14", - "RAD51B", - "H2BC15", - "RAD51D", - "ARTN", - "H2BC11", - "H2BC12", - "CDK2", - "ATM", - "SERPINA2", - "RRAD", - "BRCA1", - "BRCA2", - "RTEL1", - "BABAM2", - "RAD51AP1", - "BABAM1", - "UIMC1", - "TOPBP1", - "RPS27A", - "ATRIP", - "RPA3", - "RAD50", - "H3-4", - "ERCC1", - "RAD51", - "RAD52", - "ERCC4", - "POLR1A", - "POLR1B", - "TIMELESS", - "UBE2V2", - "UBA52", - "H2AX", - "POLD4", - "H2BC26", - "POLE", - "WRN", - "EXO1", - "SUMO2", - "H2BC21", - "POLD1", - "POLD2", - "POLD3", - "POLK", - "POLH" - ], - "module2_reactome_2022_filtered__HIV Infection R-HSA-162906": [ - "AP1S1", - "NUP214", - "AP1B1", - "NUP210", - "PSMB9", - "NUP93", - "PSMC6", - "CD4", - "NUP98", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "RAN", - "VPS28", - "NUP205", - "CCNK", - "CCNH", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "NUP85", - "PSMD3", - "PSMD4", - "MVB12B", - "AP2S1", - "MVB12A", - "PSMD2", - "NUP88", - "B2M", - "BTRC", - "TAF9B", - "APOBEC3G", - "XRCC5", - "XRCC6", - "XRCC4", - "VPS37D", - "HLA-A", - "VPS37B", - "VPS37C", - "VPS37A", - "PSME4", - "NMT2", - "PSME2", - "PSME3", - "PSME1", - "NMT1", - "POM121", - "NEDD4L", - "AAAS", - "POM121C", - "NUP62", - "AP1S3", - "RCC1", - "AP1S2", - "PSMF1", - "SKP1", - "SEC13", - "NDC1", - "UBAP1", - "CD28", - "ELL", - "SUPT4H1", - "NELFCD", - "AP1M1", - "CCR5", - "RPS27A", - "AP1M2", - "NPM1", - "RANBP2", - "RANBP1", - "AP2B1", - "RNASE7", - "GTF2H1", - "GTF2H2", - "PACS1", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "HCK", - "RNASE1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "TCEA1", - "PKN2", - "FEN1", - "CTDP1", - "AP2A2", - "AP2A1", - "NUP188", - "PSMA8", - "PSMB7", - "XPO1", - "PSMB8", - "PSMB5", - "PSMB6", - "TAF7L", - "PSMB3", - "PSMB4", - "CXCR4", - "PSMB1", - "PSMB2", - "ELOA", - "ELOB", - "ELOC", - "SUPT16H", - "LIG4", - "GTF2F1", - "GTF2F2", - "LOC124904439", - "RBX1", - "PPIA", - "ELMO1", - "CHMP4C", - "CHMP4B", - "CCNT2", - "RNMT", - "CCNT1", - "GTF2B", - "GTF2E1", - "GTF2E2", - "NUP160", - "TAF1L", - "BANF1", - "KPNA1", - "VTA1", - "ELOA2", - "NUP153", - "NUP155", - "PDCD6IP", - "SUPT5H", - "RNF103-CHMP3", - "SEM1", - "CD8B", - "CHMP2B", - "CHMP2A", - "CHMP7", - "MNAT1", - "CHMP5", - "CHMP6", - "TSG101", - "NELFA", - "CUL5", - "TPR", - "UBC", - "UBB", - "NELFE", - "NELFB", - "ATP6V1H", - "AP2M1", - "NCBP2", - "SGIP1", - "NCBP1", - "HMGA1", - "NUP133", - "CDK9", - "NUP50", - "CDK7", - "NUP54", - "NUP58", - "CD247", - "DOCK2", - "GTF2A1", - "PSMD11", - "GTF2A2", - "PSMD10", - "PSMD13", - "SEH1L", - "SLC25A4", - "PSMD12", - "VPS4A", - "PSMD14", - "VPS4B", - "PSIP1", - "NUP42", - "NUP43", - "TBP", - "RNGTT", - "TAF15", - "TAF13", - "TAF11", - "TAF12", - "TAF10", - "SSRP1", - "RANGAP1", - "TAF1", - "KPNB1", - "LCK", - "ERCC2", - "ERCC3", - "NUP35", - "TAF9", - "TAF7", - "TAF6", - "SLC25A6", - "TAF5", - "UBA52", - "SLC25A5", - "TAF4", - "NUP37", - "TAF3", - "TAF2", - "NUP107", - "FURIN", - "AP1G1", - "POLR2A", - "POLR2B", - "POLR2C", - "POLR2D", - "POLR2E", - "POLR2F", - "LRIG1", - "POLR2G", - "POLR2H", - "FYN", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__HIV Life Cycle R-HSA-162587": [ - "SUPT16H", - "NUP214", - "LIG4", - "GTF2F1", - "NUP210", - "GTF2F2", - "LOC124904439", - "NUP93", - "PPIA", - "CD4", - "NUP98", - "CHMP4C", - "CHMP4B", - "RAN", - "VPS28", - "NUP205", - "CCNK", - "CCNT2", - "RNMT", - "CCNT1", - "CCNH", - "GTF2B", - "GTF2E1", - "GTF2E2", - "NUP160", - "TAF1L", - "NUP85", - "MVB12B", - "BANF1", - "MVB12A", - "NUP88", - "KPNA1", - "TAF9B", - "XRCC5", - "VTA1", - "XRCC6", - "XRCC4", - "ELOA2", - "VPS37D", - "NUP153", - "VPS37B", - "NUP155", - "VPS37C", - "PDCD6IP", - "SUPT5H", - "VPS37A", - "RNF103-CHMP3", - "CHMP2B", - "NMT2", - "CHMP2A", - "CHMP7", - "MNAT1", - "CHMP5", - "CHMP6", - "NMT1", - "TSG101", - "POM121", - "NELFA", - "NEDD4L", - "AAAS", - "TPR", - "UBC", - "POM121C", - "UBB", - "NUP62", - "NELFE", - "RCC1", - "NELFB", - "SEC13", - "NCBP2", - "SGIP1", - "NCBP1", - "NDC1", - "HMGA1", - "NUP133", - "CDK9", - "NUP50", - "CDK7", - "NUP54", - "UBAP1", - "NUP58", - "GTF2A1", - "GTF2A2", - "SEH1L", - "VPS4A", - "ELL", - "VPS4B", - "PSIP1", - "SUPT4H1", - "NUP42", - "NUP43", - "NELFCD", - "CCR5", - "RPS27A", - "TBP", - "RANBP2", - "RNGTT", - "TAF15", - "TAF13", - "RANBP1", - "TAF11", - "TAF12", - "RNASE7", - "GTF2H1", - "TAF10", - "GTF2H2", - "SSRP1", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "RANGAP1", - "TAF1", - "ERCC2", - "ERCC3", - "NUP35", - "TAF9", - "TAF7", - "TCEA1", - "TAF6", - "TAF5", - "UBA52", - "TAF4", - "NUP37", - "TAF3", - "TAF2", - "FEN1", - "NUP107", - "CTDP1", - "FURIN", - "NUP188", - "XPO1", - "POLR2A", - "TAF7L", - "POLR2B", - "POLR2C", - "CXCR4", - "POLR2D", - "POLR2E", - "ELOA", - "POLR2F", - "ELOB", - "LRIG1", - "POLR2G", - "ELOC", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__Hedgehog Off State R-HSA-5610787": [ - "DYNC2H1", - "RPGRIP1L", - "PSMB9", - "RBX1", - "PSMC6", - "IFT88", - "PSMC4", - "PSMC5", - "PSMC2", - "GPR161", - "PSMC3", - "PSMC1", - "ADCY10", - "ADCY1", - "IFT172", - "ADCY5", - "ADCY4", - "ADCY2", - "ADCY9", - "ADCY8", - "ADCY7", - "PSMB11", - "ADCY6", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "TTC21B", - "PRKCA", - "SEM1", - "PRKAR1B", - "PRKAR1A", - "PSME4", - "PSME2", - "GNAS", - "PSME3", - "PSME1", - "MKS1", - "CUL1", - "PRKACG", - "UBC", - "UBB", - "KIF3A", - "PRKAR2B", - "PRKAR2A", - "PSMF1", - "PRKACB", - "SKP1", - "CSNK1A1", - "OFD1", - "PTCH1", - "IFT122", - "FUZ", - "WDR35", - "KIF7", - "NUMB", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "INTU", - "RPS27A", - "SMOX", - "IFT140", - "WDR19", - "ITCH", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "UBA52", - "TULP3", - "GLI2", - "GLI1", - "PSMA8", - "GLI3", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "IFT52", - "PSMB1", - "PSMB2", - "SUFU", - "IFT57" - ], - "module2_reactome_2022_filtered__Hedgehog On State R-HSA-5632684": [ - "CDON", - "CSNK1A1", - "SMURF2", - "PTCH2", - "SMURF1", - "PTCH1", - "IQCE", - "KIF7", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "GAS1", - "PSMC2", - "GPR161", - "PSMC3", - "PSMC1", - "ULK3", - "SPOPL", - "GAS8", - "NUMB", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "IHH", - "CDC73", - "PSMB11", - "PSMB10", - "GRK2", - "PSMD9", - "PSMD7", - "PSMD8", - "SHH", - "BOC", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "SPOP", - "RPS27A", - "SMOX", - "ITCH", - "EVC", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "DZIP1", - "PSME1", - "UBA52", - "DHH", - "CUL3", - "HHIP", - "ARRB2", - "EFCAB7", - "GLI2", - "GLI1", - "PSMA8", - "GLI3", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "ARRB1", - "PSMB4", - "UBB", - "KIF3A", - "PSMB1", - "PSMB2", - "SUFU", - "PSMF1", - "EVC2" - ], - "module2_reactome_2022_filtered__Hedgehog Ligand Biogenesis R-HSA-5358346": [ - "PSMB9", - "DISP2", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "SEL1L", - "IHH", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "SCUBE2", - "SHH", - "PSMD5", - "PSMD6", - "ERLEC1", - "PSMD3", - "PSMD4", - "PSMD2", - "GPC5", - "RPS27A", - "SYVN1", - "PSMA6", - "PSMA7", - "PSMA4", - "ADAM17", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "P4HB", - "PSME2", - "PSME3", - "NOTUM", - "PSME1", - "UBA52", - "DHH", - "VCP", - "DERL2", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "HHAT", - "PSMB6", - "PSMB3", - "UBC", - "OS9", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Heparan Sulfate/Heparin (HS-GAG) Metabolism R-HSA-1638091": [ - "HS3ST1", - "HS3ST3B1", - "B3GAT3", - "B3GAT2", - "GLCE", - "B3GAT1", - "HS3ST6", - "HS3ST4", - "HS3ST5", - "HS3ST2", - "VCAN", - "CSPG5", - "CSPG4", - "GLB1L", - "AGRN", - "HS3ST3A1", - "IDUA", - "SDC3", - "XYLT1", - "SDC4", - "XYLT2", - "HPSE2", - "HS6ST1", - "HS6ST2", - "HS2ST1", - "HS6ST3", - "GPC2", - "IDS", - "GPC1", - "GPC4", - "GPC3", - "GPC6", - "GPC5", - "BGN", - "B3GALT6", - "DCN", - "EXT1", - "BCAN", - "GLB1", - "HSPG2", - "HPSE", - "SDC1", - "SDC2", - "B4GALT7", - "SLC35D2", - "NCAN", - "SGSH", - "NDST3", - "NDST2", - "NDST1", - "NAGLU", - "NDST4", - "GUSB" - ], - "module2_reactome_2022_filtered__Hh Mutants Abrogate Ligand Secretion R-HSA-5387390": [ - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "SEL1L", - "IHH", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "SHH", - "PSMD5", - "PSMD6", - "ERLEC1", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "SYVN1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "DHH", - "VCP", - "DERL2", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "HHAT", - "PSMB6", - "PSMB3", - "UBC", - "OS9", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Hh Mutants Are Degraded By ERAD R-HSA-5362768": [ - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "SEL1L", - 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"IQGAP1", - "ADIPOR1", - "AGPAT1", - "ADIPOR2", - "ACACB", - "ACACA", - "ITPR1", - "GNGT2", - "GNGT1", - "RAP1A", - "CD36", - "ADIPOQ", - "PKLR", - "PPP2CA", - "PLCB2", - "PLCB3", - "TKT", - "PLCB1", - "PRKAG2", - "INS", - "GNAI1", - "GNAI2", - "GNA14", - "PPP2CB", - "GNA15", - "GNA11", - "PPP2R1B" - ], - "module2_reactome_2022_filtered__Integrin Cell Surface Interactions R-HSA-216083": [ - "ITGA1", - "LUM", - "ITGA5", - "ITGA4", - "ITGA3", - "ITGA2", - "FN1", - "VCAM1", - "ITGA10", - "ITGAE", - "ITGAD", - "ITGA11", - "COL9A2", - "ITGA9", - "COL9A1", - "ITGA8", - "PECAM1", - "ITGA7", - "COL9A3", - "ITGA6", - "AGRN", - "COL18A1", - "COL16A1", - "ITGA2B", - "COMP", - "FZR1", - "BSG", - "JAM2", - "KDR", - "MADCAM1", - "FGA", - "JAM3", - "FGB", - "FGG", - "COL23A1", - "COL4A1", - "HSPG2", - "COL4A3", - "COL4A2", - "COL4A4", - "CD47", - "FBN1", - "CD44", - "ITGB2", - "ITGB1", - "ITGB6", - "ITGB5", - "COL13A1", - "ITGB3", - "ICAM3", - "CXXC1", - "ICAM4", - "ICAM5", - "THBS1", - "F11R", - "ITGAM", - "ITGAL", - "ICAM1", - "VTN", - "ICAM2", - "IBSP", - "ITGAX", - "TNNI3", - "ITGB8", - "ITGAV", - "ITGB7" - ], - "module2_reactome_2022_filtered__Interferon Signaling R-HSA-913531": [ - "GBP7", - "GBP6", - "MX1", - "MX2", - "NUP214", - "RSAD2", - "NUP210", - "PML", - "NUP93", - "NUP98", - "IFI27", - "TRIM14", - "TRIM17", - "TRIM10", - "NUP205", - "IFITM2", - "IFITM3", - "IFITM1", - "SP100", - "UBE2L6", - "HERC5", - "NUP85", - "GBP1", - "NUP88", - "B2M", - "GBP2", - "GBP5", - "GBP4", - "EGR1", - "HLA-DRB5", - "PRKCD", - "BCL11B", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "HLA-E", - "HLA-F", - "PDE12", - "HLA-DRB4", - "HLA-DRB3", - "HLA-DRB1", - "POM121", - "IFI35", - "IFIT5", - "IFIT2", - "IFI30", - "IFIT1", - "ICAM1", - "AAAS", - "IFIT3", - "MT2A", - "POM121C", - "NUP62", - "PLCG1", - "SEC13", - "NDC1", - "GNL3", - "TYK2", - "BST2", - "HLA-DQB2", - "TRIM2", - "HOXC13", - "SOCS3", - "SOCS1", - "TRIM8", - "TRIM5", - "TRIM3", - "RPS27A", - "TRIM6-TRIM34", - "HLA-DQA2", - "HLA-DQA1", - "RANBP2", - "IFNGR1", - "STAT1", - "IFNGR2", - "STAT2", - "RNASE7", - "UBE2E1", - "NEDD4", - "CD44", - "IFNA6", - "IFNA5", - "IFNA8", - "IFNA7", - "RNASEL", - "IFNA2", - "IFNA1", - "IFNA4", - "PTAFR", - "NUP188", - "PSMB8", - "HLA-DPA1", - "ARIH1", - "IFNB1", - "JAK1", - "JAK2", - "PTPN1", - "PTPN2", - "MID1", - "VCAM1", - "PTPN6", - "UBE2N", - "IFI6", - "NUP160", - "FLNA", - "KPNA7", - "FLNB", - "KPNA5", - "KPNA3", - "KPNA4", - "KPNA1", - "KPNA2", - "IFNA14", - "IFNA16", - "IFNA17", - "NUP153", - "ISG15", - "NUP155", - "PIAS1", - "SMAD7", - "ISG20", - "EIF4G1", - "XAF1", - "EIF4G3", - "IFNA10", - "IFNAR1", - "RIGI", - "EIF4G2", - "IFNAR2", - "PDZD2", - "TPR", - "UBC", - "UBB", - "IFNA21", - "TRIM68", - "TRIM62", - "EIF2AK2", - "SGIP1", - "PTPN11", - "NUP133", - "OAS1", - "IRF1", - "NUP50", - "IRF4", - "OAS2", - "IRF5", - "NUP54", - "OAS3", - "IRF2", - "IFNG", - "IRF3", - "HLA-DRA", - "IRF8", - "IRF9", - "NUP58", - "IRF6", - "IRF7", - "EIF4E3", - "EIF4E2", - "SEH1L", - "NCAM1", - "UBA7", - "ADAR", - "NUP42", - "NUP43", - "TRIM48", - "EIF4E", - "IP6K2", - "CIITA", - "TRIM45", - "TRIM46", - "KPNB1", - "PPM1B", - "NUP35", - "TRIM38", - "UBA52", - "NUP37", - "ABCE1", - "TRIM35", - "EIF4A3", - "CAMK2D", - "EIF4A2", - "EIF4A1", - "NUP107", - "TRIM31", - "CAMK2A", - "CAMK2B", - "USP18", - "OASL", - "SAMHD1", - "FCGR1A", - "TRIM29", - "SUMO1", - "MAPK3", - "TRIM25", - "TRIM26", - "CAMK2G", - "TRIM21", - "TRIM22" - ], - "module2_reactome_2022_filtered__Interferon Alpha/Beta Signaling R-HSA-909733": [ - "PTPN1", - "MX1", - "MX2", - "RSAD2", - "PTPN11", - "OAS1", - "TYK2", - "BST2", - "IRF1", - "IRF4", - "OAS2", - "IFI27", - "IRF5", - "OAS3", - "IRF2", - "IRF3", - "IRF8", - "IRF9", - "IRF6", - "IRF7", - "PTPN6", - "IFITM2", - "IFITM3", - "IFITM1", - "IFI6", - "HOXC13", - "ADAR", - "SOCS3", - "SOCS1", - "IP6K2", - "GBP2", - "KPNA1", - "EGR1", - "IFNA14", - "IFNA16", - "STAT1", - "IFNA17", - "STAT2", - "HLA-C", - "HLA-A", - "HLA-B", - "ISG15", - "HLA-G", - "HLA-E", - "HLA-F", - "ISG20", - "KPNB1", - "XAF1", - "IFNA10", - "IFNAR1", - "ABCE1", - "IFNAR2", - "IFNA6", - "IFNA5", - "IFNA8", - "IFNA7", - "RNASEL", - "IFNA2", - "IFNA1", - "IFNA4", - "IFI35", - "IFIT5", - "IFIT2", - "USP18", - "IFIT1", - "OASL", - "IFIT3", - "SAMHD1", - "PSMB8", - "IFNB1", - "IFNA21", - "JAK1" - ], - "module2_reactome_2022_filtered__Interferon Gamma Signaling R-HSA-877300": [ - "GBP7", - "PTPN1", - "GBP6", - "PTPN2", - "TRIM62", - "PTPN11", - "OAS1", - "MID1", - "VCAM1", - "PML", - "IRF1", - "IRF4", - "OAS2", - "IRF5", - "OAS3", - "IRF2", - "IFNG", - "IRF3", - "HLA-DRA", - "IRF8", - "TRIM14", - "IRF9", - "IRF6", - "IRF7", - "TRIM17", - "TRIM10", - "PTPN6", - "HLA-DQB2", - "NCAM1", - "SP100", - "TRIM2", - "SOCS3", - "SOCS1", - "TRIM8", - "TRIM5", - "TRIM3", - "TRIM48", - "GBP1", - "B2M", - "CIITA", - "GBP2", - "TRIM6-TRIM34", - "GBP5", - "HLA-DQA2", - "TRIM45", - "GBP4", - "HLA-DQA1", - "TRIM46", - "HLA-DRB5", - "IFNGR1", - "PRKCD", - "STAT1", - "BCL11B", - "IFNGR2", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "PIAS1", - "SMAD7", - "HLA-E", - "HLA-F", - "TRIM38", - "HLA-DRB4", - "HLA-DRB3", - "CD44", - "HLA-DRB1", - "TRIM35", - "CAMK2D", - "TRIM31", - "CAMK2A", - "PTAFR", - "CAMK2B", - "IFI30", - "ICAM1", - "OASL", - "MT2A", - "FCGR1A", - "TRIM29", - "SUMO1", - "HLA-DPA1", - "TRIM25", - "TRIM26", - "CAMK2G", - "TRIM21", - "TRIM22", - "JAK1", - "JAK2", - "TRIM68" - ], - "module2_reactome_2022_filtered__Interleukin-1 Family Signaling R-HSA-446652": [ - "MAP3K3", - "PTPN2", - "FBXW11", - "IL1R1", - "IL1R2", - "IL1F10", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "TOLLIP", - "PSMC2", - "PSMC3", - "PTPN9", - "PSMC1", - "PTPN7", - "PTPN5", - "SQSTM1", - "ALPK1", - "PTPN6", - "TP53", - "PTPN4", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "IL1RAP", - "NOD2", - "PSMB11", - "TANK", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "ALOX5", - "PSMD4", - "PSMD2", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "SMAD3", - "MAP2K1", - "IL36G", - "NFKB1", - "NFKB2", - "MYD88", - "SEM1", - "PSME4", - "TRAF6", - "IL1RAPL1", - "PSME2", - "PSME3", - "TAB3", - "PSME1", - "TAB2", - "TAB1", - "IL1RN", - "CUL1", - "AGER", - "IL18RAP", - "IL36A", - "IL36B", - "TBK1", - "UBC", - "UBB", - "IL36RN", - "PSMF1", - "MAP2K6", - "SKP1", - "PTPN18", - "GSDMD", - "IKBIP", - "IL13", - "PTPN11", - "IRAK4", - "PTPN12", - "IL18", - "PTPN13", - "PTPN14", - "IL1A", - "NFKBIB", - "IL1B", - "IL4", - "NFKBIA", - "TNIP2", - "PTPN20", - "IL18R1", - "IL18BP", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "HMGB1", - "PTPN23", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "IRAK3", - "NKIRAS2", - "IRAK1", - "RIPK2", - "CASP1", - "CTSG", - "RPS27A", - "IL33", - "IL37", - "CHUK", - "STAT3", - "S100B", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "SIGIRR", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PSMA8", - "IL1RL1", - "PSMB7", - "IKBKB", - "IL1RL2", - "PSMB8", - "PSMB5", - "MAPK8", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "IKBKG" - ], - "module2_reactome_2022_filtered__Interleukin-1 Signaling R-HSA-9020702": [ - "MAP3K3", - "FBXW11", - "IL1R1", - "IL1R2", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "TOLLIP", - "PSMC2", - "PSMC3", - "PSMC1", - "SQSTM1", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "IL1RAP", - "NOD2", - "PSMB11", - "TANK", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K1", - "NFKB1", - "NFKB2", - "MYD88", - "SEM1", - "PSME4", - "TRAF6", - "PSME2", - "PSME3", - "TAB3", - "PSME1", - "TAB2", - "TAB1", - "IL1RN", - "CUL1", - "AGER", - "UBC", - "UBB", - "PSMF1", - "MAP2K6", - "SKP1", - "IKBIP", - "IRAK4", - "IL1A", - "NFKBIB", - "IL1B", - "NFKBIA", - "TNIP2", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "HMGB1", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "IRAK3", - "NKIRAS2", - "IRAK1", - "RIPK2", - "RPS27A", - "CHUK", - "S100B", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PSMA8", - "PSMB7", - "IKBKB", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "IKBKG" - ], - "module2_reactome_2022_filtered__Interleukin-12 Family Signaling R-HSA-447115": [ - "HSPA9", - "ANXA2", - "IL10", - "MSN", - "TCP1", - "TYK2", - "CRLF1", - "MTAP", - "TOX4", - "PPIA", - "IL23A", - "GSTA2", - "IFNG", - "CAPZA1", - "HNRNPF", - "PDCD4", - "IL6ST", - "S100A9", - "RALA", - "EBI3", - "LMNB1", - "IL27RA", - "CA1", - "RAP1B", - "IL12RB2", - "IL12RB1", - "SERPINB2", - "STAT1", - "GSTO1", - "STAT3", - "STAT4", - "TALDO1", - "SOD1", - "CDC42", - "VAMP7", - "HNRNPA2B1", - "CANX", - "P4HB", - "PSME2", - "PKN2", - "SNRPA1", - "AURKAIP1", - "RPLP0", - "IL23R", - "IL27", - "PITPNA", - "BOLA2B", - "CNN2", - "VPS72", - "HNRNPDL", - "IL12B", - "IL12A", - "JAK1", - "JAK2" - ], - "module2_reactome_2022_filtered__Interleukin-17 Signaling R-HSA-448424": [ - "DUSP6", - "DUSP3", - "DUSP4", - "JUN", - "FBXW11", - "PPP2R1A", - "PPP2R5D", - "VRK3", - "DUSP7", - "MAPK14", - "MAPK11", - "MAPK10", - "TNIP2", - "MAPKAPK2", - "MAPKAPK3", - "UBE2N", - "NOD1", - "NOD2", - "IRAK2", - "IRAK1", - "RIPK2", - "MAP3K7", - "BTRC", - "MAP3K8", - "RPS27A", - "IL17RA", - "MAP2K4", - "MEF2A", - "MAP2K3", - "MEF2C", - "CHUK", - "KCNH8", - "MAP2K1", - "FOS", - "IL17RE", - "NFKB1", - "IL17RC", - "IL17RB", - "PPP2CA", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "UBE2V1", - "IL17F", - "TAB2", - "TAB1", - "UBA52", - "IL17C", - "IL25", - "CUL1", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "UBC", - "PPP2R1B", - "UBB", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "MAP2K6", - "SKP1", - "MAP2K7" - ], - "module2_reactome_2022_filtered__Interleukin-4 And Interleukin-13 Signaling R-HSA-6785807": [ - "TGFB1", - "IL4R", - "VCAM1", - "BATF", - "COL1A2", - "IL23A", - "VIM", - "CCM2", - "TP53", - "TNF", - "IL2RG", - "FOXO3", - "HSP90B1", - "FOXO1", - "MUC1", - "CCND1", - "ALOX5", - "MYC", - "AKT1", - "S1PR1", - "STAT6", - "LONP1", - "PIK3R1", - "BCL2L1", - "BIRC5", - "FSCN1", - "LCN2", - "NANOG", - "RNASEH2A", - "FGF13", - "IL23R", - "TWIST1", - "FASLG", - "RORC", - "GATA3", - "HIF1A", - "RORA", - "ICAM1", - "SOX2", - "NDN", - "HMOX1", - "IL12B", - "IL12A", - "IL6R", - "IL13RA1", - "IL13RA2", - "ANXA1", - "POU2F1", - "HSP90AA1", - "IL10", - "CCL22", - "MMP1", - "MMP2", - "HSPA8", - "IL13", - "MMP3", - "FN1", - "LIF", - "OPRM1", - "IL18", - "MMP9", - "TYK2", - "VEGFA", - "TNFRSF1B", - "IL1A", - "ZEB1", - "IL1B", - "IL4", - "IL6", - "IRF4", - "BCL6", - "BCL2", - "RHOU", - "FCER2", - "SOS1", - "CXCL8", - "OPRD1", - "MAOA", - "ALOX15", - "SOCS3", - "SOCS1", - "LBP", - "TIMP1", - "CD36", - "MCL1", - "SOCS5", - "NANOS2", - "STAT1", - "STAT3", - "FOS", - "POMC", - "SAA2", - "IL17F", - "ITGB2", - "ITGB1", - "CEBPD", - "CCL11", - "CDKN1A", - "F13A1", - "LAMA5", - "ITGAM", - "PTGS2", - "ITGAX", - "CCL2", - "JAK3", - "JAK1", - "JAK2" - ], - "module2_reactome_2022_filtered__Intra-Golgi And Retrograde Golgi-to-ER Traffic R-HSA-6811442": [ - "RAB30", - "DYNC1LI1", - "DYNC1LI2", - "ZW10", - "UBE2Z", - "KIF16B", - "CAPZA2", - "KIF4B", - "RINT1", - "CAPZA3", - "KIF4A", - "RAB36", - "CAPZA1", - "KIF20A", - "KIF20B", - "GOLGA8B", - "RAB1A", - "NBAS", - "RAB1B", - "RGPD1", - "STX16", - "USP6NL", - "KDELR1", - "STX18", - "TMF1", - "KDELR2", - "STX10", - "NAPG", - "KIF5A", - "RACGAP1", - "KIF5C", - "KIF5B", - "TGOLN2", - "STX6", - "VTI1A", - "ARFIP2", - "STX5", - "YKT6", - "IGF2R", - "NSF", - "COG8", - "NAPSA", - "TMED9", - "COG7", - "KIF22", - "SURF4", - "COG6", - "COG5", - "COG4", - "RAB39A", - "COG3", - "PLA2G4A", - "BET1L", - "COG2", - "KIF25", - "M6PR", - "COG1", - "KIF23", - "ARFGAP2", - "COPZ2", - "ARFGAP3", - "COPZ1", - "KIF27", - "ARFGAP1", - "KIF18A", - "KIF18B", - "KIF2B", - "KIF2A", - "KIFC2", - "KIFC1", - "KIF2C", - "VAMP3", - "VAMP4", - "COPA", - "SYS1", - "SCOC", - "COPB2", - "COPB1", - "GCC1", - "GCC2", - "PLA2G6", - "RAB41", - "GOLIM4", - "RHOBTB3", - "GOLGA4", - "RAB43", - "KIF3C", - "KIF3B", - "MAN2A2", - "GOLGA1", - "KIF3A", - "KIF21A", - "KIF21B", - "MAN2A1", - "KIFAP3", - "NAA30", - "PTGES2", - "GALNT2", - "NAA35", - "GALNT1", - "KIF6", - "RAB33B", - "CENPE", - "KIF9", - "ALPP", - "COPG1", - "PAFAH1B1", - "COPG2", - "NAA38", - "ACTR10", - "AKR1B10", - "PAFAH1B3", - "COPE", - "PAFAH1B2", - "ARF4", - "ARF5", - "ARF3", - "KIF11", - "KIF15", - "KIF12", - "BICD1", - "KIF19", - "ACTR1A", - "AGPAT3", - "BICD2", - "KLC1", - "KLC4", - "KLC3", - "KIF1A", - "VPS52", - "VPS51", - "VPS54", - "CAPZB", - "VPS53", - "TMED3", - "KIF1C", - "MAN1A2", - "TMED2", - "KIF1B", - "MAN1A1", - "RAB6B", - "SNAP29", - "TMED7", - "DYNC1I2", - "DYNC1I1", - "RAB6A", - "RIC1", - "DYNLL1", - "DYNLL2", - "ARCN1", - "KIF26A", - "KIF26B", - "RAB9A", - "TRIP11", - "VPS45", - "RAB9B", - "KDELR3", - "PLIN3", - "RAB18", - "SEC22B", - "DYNC1H1", - "RAB3GAP2", - "RAB3GAP1", - "DCTN6", - "DCTN3", - "GOSR1", - "DCTN2", - "DCTN5", - "DCTN4", - "GOSR2", - "DCTN1", - "RABEPK", - "CYTH4", - "CYTH3", - "ARFRP1", - "CUX1", - "KIF13B", - "MAN1C1", - "BNIP1", - "CYTH2", - "CYTH1" - ], - "module2_reactome_2022_filtered__Intracellular Signaling By Second Messengers R-HSA-9006925": [ - "EGF", - "BAD", - "GATAD2A", - "PPP2R1A", - "FRS2", - "EREG", - "PSMB9", - "GATAD2B", - "PML", - "PSMC6", - "RRAGB", - "RRAGA", - "PSMC4", - "RRAGD", - "PSMC5", - "RRAGC", - "PSMC2", - "PSMC3", - "PSMC1", - "TP53", - "CALM1", - "AHCYL1", - "RNF2", - "ADCY1", - "ADCY5", - "ADCY4", - "ADCY2", - "ADCY9", - "ADCY8", - "ADCY7", - "PSMB11", - "ADCY6", - "PSMB10", - "PSMD9", - "PREX2", - "MTA1", - "PSMD7", - "MTA2", - "PSMD8", - "PSMD5", - "PSMD6", - "AKT3", - "FLT3LG", - "PSMD3", - "PSMD4", - "AKT1", - "PIP4K2A", - "AKT2", - "PIP4K2B", - 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"LAMTOR4", - "CDKN1B", - "PHLPP1", - "PHLPP2", - "PIGU", - "CDKN1A", - "CAMKK1", - "CAMKK2", - "PSMA8", - "PPP2CB", - "PSMB7", - "PSMB8", - "RPTOR", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "RAC2", - "RICTOR", - "PDGFRB", - "PDGFRA", - "USP7", - "TSC2", - "DPEP3", - "OTUD3", - "FRK", - "CD86", - "KLB", - "KDM1A", - "RNMT", - "CD80", - "FOXO6", - "IL1RAP", - "ATN1", - "PRKX", - "FOXO4", - "EGFR", - "FOXO3", - "FOXO1", - "RBBP4", - "FGF20", - "RBBP7", - "FGF23", - "FGF22", - "KPNA2", - "NBEA", - "ESR1", - "ESR2", - "FGF18", - "FGF17", - "MYD88", - "CREB1", - "FGF16", - "SEM1", - "RHEB", - "GDNF", - "MKRN1", - "FGF19", - "TRAF6", - "FGF10", - "FGFR1", - "FGF13", - "FGFR4", - "FGFR3", - "FGFR2", - "MDM2", - "IRS1", - "IRS2", - "CLSTN1", - "FGF1", - "FGF3", - "FGF4", - "NR4A1", - "FGF5", - "FGF6", - "FGF7", - "FGF8", - "FGF9", - "SCMH1", - "UBC", - "SALL4", - "UBB", - "THEM4", - "SLC38A9", - "MAF1", - "KL", - "CBX8", - "CBX6", - "CBX4", - "CBX2", - "PPP2R5C", - "NRG1", - 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"SUPT16H", - "NUP214", - "GTF2F1", - "NUP210", - "GTF2F2", - "LOC124904439", - "NUP93", - "PPIA", - "NUP98", - "CHMP4C", - "CHMP4B", - "RAN", - "VPS28", - "NUP205", - "CCNK", - "CCNT2", - "RNMT", - "CCNT1", - "CCNH", - "GTF2B", - "GTF2E1", - "GTF2E2", - "NUP160", - "TAF1L", - "NUP85", - "MVB12B", - "MVB12A", - "NUP88", - "TAF9B", - "VTA1", - "ELOA2", - "VPS37D", - "NUP153", - "VPS37B", - "NUP155", - "VPS37C", - "PDCD6IP", - "SUPT5H", - "VPS37A", - "RNF103-CHMP3", - "CHMP2B", - "NMT2", - "CHMP2A", - "CHMP7", - "MNAT1", - "CHMP5", - "CHMP6", - "NMT1", - "TSG101", - "POM121", - "NELFA", - "NEDD4L", - "AAAS", - "TPR", - "UBC", - "POM121C", - "UBB", - "NUP62", - "NELFE", - "RCC1", - "NELFB", - "SEC13", - "NCBP2", - "SGIP1", - "NCBP1", - "NDC1", - "NUP133", - "CDK9", - "NUP50", - "CDK7", - "NUP54", - "UBAP1", - "NUP58", - "GTF2A1", - "GTF2A2", - "SEH1L", - "VPS4A", - "ELL", - "VPS4B", - "SUPT4H1", - "NUP42", - "NUP43", - "NELFCD", - "RPS27A", - "TBP", - "RANBP2", - "RNGTT", - "TAF15", - "TAF13", - 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"NUP188", - "CENPA", - "PHF8", - "TUBA1A", - "PSMA8", - "PPP2CB", - "PSMB7", - "XPO1", - "PSMB8", - "PSMB5", - "TUBA1C", - "PCM1", - "PSMB6", - "TUBA1B", - "PSMB3", - "PSMB4", - "ENSA", - "NUF2", - "PSMB1", - "H3C13", - "PSMB2", - "NINL", - "UBE2I", - "H2BC9", - "RAB2A", - "IST1", - "UBE2C", - "H2BC4", - "H2BC5", - "H2BC3", - "VRK1", - "VRK2", - "H2BC1", - "TUBA4B", - "TUBA4A", - "SGO2", - "TUBA8", - "CTDNEP1", - "MAU2", - "SGO1", - "UBE2S", - "BUB1B", - "TUBGCP6", - "CHMP4C", - "B9D2", - "CHMP4B", - "TUBGCP4", - "TUBGCP5", - "MAD2L1", - "RAB1A", - "RAB1B", - "ZWILCH", - "UBE2D1", - "PMF1-BGLAP", - "H2AB1", - "TUBA3C", - "NUP160", - "AURKB", - "LMNA", - "PPP1CC", - "TUBB8", - "TUBA3E", - "TUBA3D", - "TUBB6", - "TUBB3", - "PTTG1", - "CEP250", - "KMT5A", - "BANF1", - "CLASP1", - "KNTC1", - "PCNT", - "CLASP2", - "NEK9", - "H2AC7", - "ODF2", - "NEK6", - "NEK7", - "H2AC6", - "H2AC4", - "NUP153", - "TUBB8B", - "KIF23", - "NUP155", - "CKAP5", - "NDC80", - "KIF18A", - "RNF103-CHMP3", - "SEM1", - "KIF2B", - "CNEP1R1", - "H4C15", - "KIF2A", - "BIRC5", - "CHMP2B", - "CHMP2A", - "KIF2C", - "CHMP7", - "MAD1L1", - "CHMP6", - "TUBGCP2", - "TUBGCP3", - "PDS5B", - "PDS5A", - "CCNB2", - "CCNB1", - "NIPBL", - "TPR", - "UBC", - "UBB", - "PMEPA1", - "NDEL1", - "NEK2", - "HSP90AA1", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "H2BC17", - "SMC1A", - "PPP2R5A", - "H2BC13", - "H2BC14", - "NUP50", - "H2BC15", - "LPIN1", - "ESPL1", - "NUP54", - "H2BC11", - "H2BC12", - "CDK1", - "NUP58", - "LPIN2", - "LPIN3", - "PSMD11", - "PSMD10", - "YWHAG", - "PSMD13", - "SEH1L", - "PSMD12", - "YWHAE", - "VPS4A", - "PSMD14", - "CEP164", - "LMNB1", - "RAD21", - "SPAST", - "TNPO1", - "NUP42", - "NUP43", - "LBR", - "CEP290", - "CEP135", - "CEP131", - "HAUS5", - "HAUS4", - "HAUS7", - "HAUS6", - "HAUS1", - "LEMD3", - "HAUS3", - "LEMD2", - "HAUS2", - "RANGAP1", - "H3-4", - "KPNB1", - "PPP2R2D", - "ANAPC4", - "NUP35", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "NUP37", - "BLZF1", - "CENPS-CORT", - "ITGB3BP", - "NUP107", - "ERCC6L", - "DCTN3", - "DCTN2", - "DCTN1", - "KANSL1", - "PPP2R2A", - "HDAC8", - "H2BC26", - "H2BC21", - "HAUS8", - "PPP2R1B", - "SUMO1", - "FBXO5", - "MAPK3", - "CEP152", - "MAPK1", - "BUB3", - "SPDL1", - "BUB1" - ], - "module2_reactome_2022_filtered__MAP Kinase Activation R-HSA-450294": [ - "DUSP6", - "DUSP3", - "DUSP4", - "JUN", - "FBXW11", - "PPP2R1A", - "PPP2R5D", - "VRK3", - "DUSP7", - "MAPK14", - "MAPK11", - "MAPK10", - "TNIP2", - "MAPKAPK2", - "MAPKAPK3", - "UBE2N", - "NOD1", - "NOD2", - "IRAK2", - "IRAK1", - "RIPK2", - "MAP3K7", - "BTRC", - "MAP3K8", - "RPS27A", - "MAP2K4", - "MEF2A", - "MAP2K3", - "MEF2C", - "CHUK", - "KCNH8", - "MAP2K1", - "FOS", - "NFKB1", - "PPP2CA", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "UBE2V1", - "TAB2", - "TAB1", - "UBA52", - "CUL1", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "UBC", - "PPP2R1B", - "UBB", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "MAP2K6", - "SKP1", - "MAP2K7" - ], - "module2_reactome_2022_filtered__MAPK Family Signaling Cascades R-HSA-5683057": [ - "EGF", - "ACTN2", - "FRS3", - "PPP2R1A", - "FRS2", - "GRIN2D", - "EREG", - "PSMB9", - "USP17L2", - "MAPK12", - "PSMC6", - "PSMC4", - "PSMC5", - "WDR83", - "PSMC2", - "PSMC3", - "TEK", - "PSMC1", - "TLN1", - "CALM1", - "ARAF", - "IL2RG", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "FLT3LG", - "PSMD3", - "MYC", - "PSMD4", - "PSMD2", - "SHOC2", - "ICMT", - "PAQR3", - "PRKCA", - "IL17RD", - "PTK2", - "MOV10", - "BTC", - "CNKSR2", - "CNKSR1", - "PIK3R1", - "PSME4", - "PSME2", - "PSME3", - "GRB2", - "PRKCQ", - "PSME1", - "RGL2", - "RGL3", - "PHB1", - "PDGFB", - "PDGFA", - "PRKACG", - "GRIN2B", - "PSMF1", - "GOLGA7", - "PRKACB", - "ZNF367", - "DUSP5", - "DUSP6", - "NCOA3", - "DUSP4", - "JUN", - "LYPLA1", - "DUSP2", - "FN1", - "BRAF", - "DUSP9", - "TYK2", - "DUSP7", - "DUSP8", - "MMP10", - "APBB1IP", - "MRAS", - "KBTBD7", - "KITLG", - "AGO4", - "ARTN", - "PTPRA", - "AGO2", - "AGO3", - "RAPGEF2", - "AGO1", - "ARHGEF7", - "SPTBN4", - "RET", - "SPTBN5", - "CHURC1-FNTB", - "PEA15", - "RASAL1", - "IQGAP1", - "LRRC7", - "RASAL2", - "TGFA", - "RASAL3", - "AREG", - "KALRN", - "SPTA1", - "RAP1A", - "RAP1B", - "RCE1", - "RPS27A", - "SPTBN1", - "SPTBN2", - "MPP2", - "MPP3", - "SEPTIN7", - "GFRA2", - "GFRA1", - "GFRA4", - "RNASE3", - "GFRA3", - "RANBP9", - "RNASE1", - "DAB2IP", - "PPP2CA", - "PSMA6", - "EPGN", - "PSMA7", - "PSMA4", - "PSMA5", - "KIT", - "PSMA2", - "PSMA3", - "PSMA1", - "PKN1", - "LLGL1", - "LAMTOR3", - "LAMTOR2", - "PKN2", - "HBEGF", - "ZDHHC9", - "PSMA8", - "PPP2CB", - "PSMB7", - "XPO1", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "NEFL", - "HRAS", - "JAK3", - "SPTAN1", - "JAK1", - "JAK2", - "PDGFRB", - "PDGFRA", - "RBX1", - "PPP1CB", - "MAPKAPK5", - "GPRIN1", - "SPNS1", - "PTPN7", - "IL6ST", - "PTPN3", - "KLB", - "CSF2", - "RNMT", - "ITGA2B", - "PEBP1", - "EGFR", - "FOXO3", - "FOXO1", - "PPP1CC", - "NRAS", - "CCND3", - "FGF20", - "FGF23", - "FGF22", - "MAP2K2", - "MAP2K1", - "PSPN", - "FGF18", - "DLG1", - "FGF17", - "FGF16", - "SEM1", - "GDNF", - "CDC42EP5", - "FGF19", - "BCL2L1", - "CDC42EP3", - "FGF10", - "FGFR1", - "CDC42EP2", - "MAP3K11", - "FGF13", - "FGFR4", - "FGFR3", - "FGFR2", - "VCL", - "ARHGAP9", - "IRS1", - "IRS2", - "CUL3", - "ARL2", - "IL5RA", - "CDC14A", - "FGF1", - "CSF2RA", - "RASGRP3", - "CSF2RB", - "FGF3", - "FGF4", - "FGF5", - "RASGRP4", - "FGF6", - "FGF7", - "FGF8", - "RASGRP1", - "FGF9", - "UBC", - "FNTA", - "UBB", - "NRTN", - "IL6R", - "MARK3", - "RAG1", - "RAG2", - "KL", - "KSR2", - "ANGPT1", - "KSR1", - "MMP2", - "PPP2R5C", - "NRG1", - "PPP2R5B", - "PTPN11", - "NRG2", - "PPP2R5E", - "NRG3", - "PPP2R5D", - "ETV4", - "SPTB", - "IL3", - "PPP2R5A", - "IL2", - "IL5", - "PPP5C", - "IL6", - "NRG4", - "PIK3CB", - "RASA4", - "PIK3CA", - "IL3RA", - "RASA2", - "RASA3", - "CDK1", - "NF1", - "TNRC6A", - "TNRC6B", - "SOS1", - "TNRC6C", - "PSMD11", - "PSMD10", - "PSMD13", - "SHC3", - "PSMD12", - "RASGRF1", - "SHC1", - "YWHAB", - "NCAM1", - "PSMD14", - "SHC2", - "RASGRF2", - "PIK3R2", - "DUSP16", - "HSPB1", - "DUSP12", - "DNAJB1", - "DUSP10", - "RASGEF1A", - "ERBB4", - "ERBB2", - "ERBB3", - "RALGDS", - "FGA", - "PRKG2", - "FGB", - "FGG", - "VWF", - "CDC42", - "ABHD17C", - "ABHD17B", - "IL2RA", - "ABHD17A", - "IL2RB", - "UBA52", - "CAMK2D", - "SRC", - "FLT3", - "CAMK2A", - "CAMK2B", - "ITGB3", - "ARRB2", - "SPRED3", - "SPRED2", - "SYNGAP1", - "SPRED1", - "ARRB1", - "PPP2R1B", - "MAPK3", - "IGF2BP1", - "PDE6D", - "MAPK1", - "CSK", - "FYN", - "CAMK2G", - "MAPK6", - "MAPK4" - ], - "module2_reactome_2022_filtered__MAPK1/MAPK3 Signaling R-HSA-5684996": [ - "EGF", - "ACTN2", - "FRS3", - "PPP2R1A", - "FRS2", - "GRIN2D", - "EREG", - "PSMB9", - "USP17L2", - "MAPK12", - "PSMC6", - "PSMC4", - "PSMC5", - "WDR83", - "PSMC2", - "PSMC3", - "TEK", - "PSMC1", - "TLN1", - "CALM1", - "ARAF", - "IL2RG", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "FLT3LG", - "PSMD3", - "PSMD4", - "PSMD2", - "SHOC2", - "ICMT", - "PAQR3", - "IL17RD", - "PTK2", - "BTC", - "CNKSR2", - "CNKSR1", - "PIK3R1", - "PSME4", - "PSME2", - "PSME3", - "GRB2", - "PRKCQ", - "PSME1", - "RGL2", - "RGL3", - "PHB1", - "PDGFB", - "PDGFA", - "GRIN2B", - "PSMF1", - "GOLGA7", - "DUSP5", - "DUSP6", - "DUSP4", - "LYPLA1", - "DUSP2", - "FN1", - "BRAF", - "DUSP9", - "TYK2", - "DUSP7", - "DUSP8", - "APBB1IP", - "MRAS", - "KBTBD7", - "KITLG", - "ARTN", - "PTPRA", - "RAPGEF2", - "SPTBN4", - "RET", - "SPTBN5", - "CHURC1-FNTB", - "PEA15", - "RASAL1", - "IQGAP1", - "LRRC7", - "RASAL2", - "TGFA", - "RASAL3", - "AREG", - "SPTA1", - "RAP1A", - "RAP1B", - "RCE1", - "RPS27A", - "SPTBN1", - "SPTBN2", - "MPP2", - "MPP3", - "GFRA2", - "GFRA1", - "GFRA4", - "RNASE3", - "GFRA3", - "RANBP9", - "DAB2IP", - "PPP2CA", - "PSMA6", - "EPGN", - "PSMA7", - "PSMA4", - "PSMA5", - "KIT", - "PSMA2", - "PSMA3", - "PSMA1", - "LLGL1", - "LAMTOR3", - "LAMTOR2", - "HBEGF", - "ZDHHC9", - "PSMA8", - "PPP2CB", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "NEFL", - "HRAS", - "JAK3", - "SPTAN1", - "JAK1", - "JAK2", - "PDGFRB", - "PDGFRA", - "RBX1", - "PPP1CB", - "GPRIN1", - "SPNS1", - "PTPN7", - "IL6ST", - "PTPN3", - "KLB", - "CSF2", - "RNMT", - "ITGA2B", - "PEBP1", - "EGFR", - "PPP1CC", - "NRAS", - "FGF20", - "FGF23", - "FGF22", - "MAP2K2", - "MAP2K1", - "PSPN", - "FGF18", - "DLG1", - "FGF17", - "FGF16", - "SEM1", - "GDNF", - "FGF19", - "BCL2L1", - "FGF10", - "FGFR1", - "MAP3K11", - "FGF13", - "FGFR4", - "FGFR3", - "FGFR2", - "VCL", - "ARHGAP9", - "IRS1", - "IRS2", - "CUL3", - "ARL2", - "IL5RA", - "FGF1", - "CSF2RA", - "RASGRP3", - "CSF2RB", - "FGF3", - "FGF4", - "FGF5", - "RASGRP4", - "FGF6", - "FGF7", - "FGF8", - "RASGRP1", - "FGF9", - "UBC", - "FNTA", - "UBB", - "NRTN", - "IL6R", - "MARK3", - "KL", - "KSR2", - "ANGPT1", - "KSR1", - "PPP2R5C", - "NRG1", - "PPP2R5B", - "PTPN11", - "NRG2", - "PPP2R5E", - "NRG3", - "PPP2R5D", - "SPTB", - "IL3", - "PPP2R5A", - "IL2", - "IL5", - "PPP5C", - "IL6", - "NRG4", - "PIK3CB", - "RASA4", - "PIK3CA", - "IL3RA", - "RASA2", - "RASA3", - "CDK1", - "NF1", - "SOS1", - "PSMD11", - "PSMD10", - "PSMD13", - "SHC3", - "PSMD12", - "RASGRF1", - "SHC1", - "YWHAB", - "NCAM1", - "PSMD14", - "SHC2", - "RASGRF2", - "PIK3R2", - "DUSP16", - "DUSP12", - "DUSP10", - "RASGEF1A", - "ERBB4", - "ERBB2", - "ERBB3", - "RALGDS", - "FGA", - "PRKG2", - "FGB", - "FGG", - "VWF", - "ABHD17C", - "ABHD17B", - "IL2RA", - "ABHD17A", - "IL2RB", - "UBA52", - "CAMK2D", - "SRC", - "FLT3", - "CAMK2A", - "CAMK2B", - "ITGB3", - "ARRB2", - "SPRED3", - "SPRED2", - "SYNGAP1", - "SPRED1", - "ARRB1", - "PPP2R1B", - "MAPK3", - "PDE6D", - "MAPK1", - "CSK", - "FYN", - "CAMK2G" - ], - "module2_reactome_2022_filtered__MAPK6/MAPK4 Signaling R-HSA-5687128": [ - "NCOA3", - "JUN", - "MMP2", - "ETV4", - "PSMB9", - "MMP10", - "PSMC6", - "AGO4", - "PSMC4", - "MAPKAPK5", - "PSMC5", - "AGO2", - "PSMC2", - "AGO3", - "PSMC3", - "AGO1", - "CDK1", - "PSMC1", - "TNRC6A", - "TNRC6B", - "ARHGEF7", - "TNRC6C", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "FOXO3", - "HSPB1", - "KALRN", - "PSMB11", - "FOXO1", - "PSMB10", - "PSMD9", - "DNAJB1", - "PSMD7", - "CCND3", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "MYC", - "PSMD4", - "PSMD2", - "RPS27A", - "SEPTIN7", - "PRKCA", - "RNASE1", - "CDC42", - "MOV10", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "CDC42EP5", - "PSME4", - "PSMA1", - "CDC42EP3", - "PKN1", - "PSME2", - "CDC42EP2", - "PSME3", - "PKN2", - "PSME1", - "UBA52", - "CDC14A", - "PSMA8", - "PSMB7", - "XPO1", - "PRKACG", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "IGF2BP1", - "PSMF1", - "PRKACB", - "MAPK6", - "RAG1", - "ZNF367", - "MAPK4", - "RAG2" - ], - "module2_reactome_2022_filtered__MHC Class II Antigen Presentation R-HSA-2132295": [ - "SEC24A", - "CD74", - "SEC24C", - "LAG3", - "SEC24B", - "AP1S1", - "AP1B1", - "DYNC1LI1", - "DYNC1LI2", - "CAPZA2", - "KIF4B", - "CAPZA3", - "KIF4A", - "CAPZA1", - "KIF20A", - "SEC24D", - "KIF5A", - "RACGAP1", - "KIF5C", - "KIF5B", - "AP2S1", - "HLA-DOB", - "HLA-DOA", - "HLA-DRB5", - "KIF22", - "KIF23", - "KIF18A", - "KIF2B", - "KIF2A", - "LGMN", - "KIF2C", - "HLA-DRB4", - "HLA-DRB3", - "HLA-DRB1", - "OSBPL1A", - "IFI30", - "KIF3C", - "KIF3B", - "KIF3A", - "AP1S3", - "AP1S2", - "KIFAP3", - "SEC31A", - "AP2M1", - "SH3GL2", - "CTSB", - "CTSA", - "SEC13", - "DNM1", - "DNM2", - "DNM3", - "CENPE", - "HLA-DRA", - "ACTR10", - "RAB7A", - "HLA-DQB2", - "KIF11", - "SAR1B", - "CLTC", - "KIF15", - "CLTA", - "CTSV", - "ACTR1A", - "KLC1", - "CTSS", - "ACTR1B", - "KLC4", - "CTSO", - "KLC3", - "CTSL", - "CAPZB", - "CTSK", - "CTSH", - "AP1M1", - "CTSF", - "CTSE", - "CTSD", - "DYNC1I2", - "HLA-DQA2", - "SPTBN2", - "AP1M2", - "CTSC", - "DYNC1I1", - "HLA-DQA1", - "DYNLL1", - "AP2B1", - "DYNLL2", - "KIF26A", - "CANX", - "DYNC1H1", - "DCTN6", - "SEC23A", - "DCTN3", - "DCTN2", - "DCTN5", - "DCTN4", - "AP2A2", - "DCTN1", - "PRICKLE1", - 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], - "module2_reactome_2022_filtered__Mitotic G1 Phase And G1/S Transition R-HSA-453279": [ - "CDC25A", - "PPP2R1A", - "PSMB9", - "PSMC6", - "PSMC4", - "POLE2", - "PSMC5", - "POLE3", - "PSMC2", - "POLE4", - "PSMC3", - "PSMC1", - "PCNA", - "RB1", - "LIN9", - "CCNH", - "TYMS", - "PSMB10", - "PSMD9", - "PSMD7", - "CCND3", - "PSMD8", - "CCND2", - "PSMD5", - "CCND1", - "PSMD6", - "AKT3", - "PSMD3", - "E2F1", - "MYC", - "PSMD4", - "RBBP4", - "AKT1", - "E2F2", - "AKT2", - "E2F3", - "PSMD2", - "E2F4", - "E2F5", - "E2F6", - "DYRK1A", - "PTK6", - "DHFR", - "CCNA2", - "CCNA1", - "TFDP1", - "SEM1", - "TFDP2", - "PSME2", - "PSME3", - "PSME1", - "MNAT1", - "MAX", - "GMNN", - "CUL1", - "CCNB1", - "UBC", - "UBB", - "PSMF1", - "ABL1", - "MYBL2", - "SKP1", - "SKP2", - "LYN", - "CDT1", - "CDC7", - "CDC6", - "POLA1", - "POLA2", - "RBL1", - "CDK7", - "RBL2", - "CDK6", - "CDK4", - "CDK2", - "MCM4", - "CDK1", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "PSMD13", - "PSMD12", - 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"ACTR1A", - "SKA2", - "DSN1", - "RAD21", - "NUP43", - "CNTRL", - "CEP290", - "DYNC1I2", - "CEP57", - "DYNC1I1", - "RANBP2", - "CEP135", - "DYNLL1", - "CSNK2A2", - "NDE1", - "CEP131", - "HAUS5", - "HAUS4", - "HAUS7", - "HAUS6", - "DYNLL2", - "HAUS1", - "MZT1", - "TUBB4B", - "HAUS3", - "TUBB4A", - "HAUS2", - "PPP2CA", - "RANGAP1", - "SPC24", - "NEDD1", - "CLIP1", - "NCAPD2", - "TAOK1", - "DYNC1H1", - "NUP37", - "SPC25", - "CENPS-CORT", - "ITGB3BP", - "MAPRE1", - "NUP107", - "ERCC6L", - "DCTN3", - "DCTN2", - "ALMS1", - "MIS12", - "DCTN1", - "KANSL1", - "CENPA", - "TUBA1A", - "HDAC8", - "PPP2CB", - "XPO1", - "PCM1", - "HAUS8", - "PPP2R1B", - "NUF2", - "CEP152", - "BUB3", - "NINL", - "SPDL1", - "BUB1" - ], - "module2_reactome_2022_filtered__Mitotic Prophase R-HSA-68875": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "RAB2A", - "H2BC4", - "H2BC5", - "NUP214", - "H2BC3", - "VRK1", - "PPP2R1A", - "VRK2", - "H2BC1", - "H2BC12L", - "NUP210", - "CTDNEP1", - "NUP93", - "NUP98", - "RAB1A", - "NUP205", - "RAB1B", - "RB1", - "H2AB1", - "NUP160", - "LMNA", - "NUP85", - "KMT5A", - "BANF1", - "NUP88", - "NEK9", - "H2AC7", - "PLK1", - "NCAPH2", - "NEK6", - "NEK7", - "H2AC6", - "H2AC4", - "NUP153", - "PRKCB", - "NUP155", - "PRKCA", - "MASTL", - "CNEP1R1", - "H2AC20", - "H4C15", - "GORASP2", - "GORASP1", - "MCPH1", - "SET", - "POM121", - "NCAPG2", - "NUMA1", - "ARPP19", - "SMC4", - "AAAS", - "H2AC19", - "TMPO", - "CCNB2", - "H2AC14", - "CCNB1", - "TPR", - "POM121C", - "GOLGA2", - "NUP62", - "SEC13", - "NDC1", - "NUP133", - "H2BC17", - "RAB33B", - "H2BC13", - "H2BC14", - "NUP50", - "H2BC15", - "H2AJ", - "LPIN1", - "NUP54", - "H2BC11", - "H2BC12", - "CDK1", - "NUP58", - "LPIN2", - "LPIN3", - "SEH1L", - "LMNB1", - "NUP42", - "NUP43", - "EMD", - "H3-3B", - "H3C7", - "RANBP2", - "RNASE7", - "LEMD3", - "LEMD2", - "PPP2CA", - "H3-4", - "PPP2R2D", - "NUP35", - "USO1", - "NCAPD3", - "NUP37", - "BLZF1", - "H2AX", - "NUP107", - "NUP188", - "PHF8", - "PPP2CB", - "H2BC26", - "H2BC21", - "PPP2R1B", - "ENSA", - "H3C13", - "MAPK3", - "MAPK1" - ], - "module2_reactome_2022_filtered__Mitotic Spindle Checkpoint R-HSA-69618": [ - "KNL1", - "UBE2C", - "PPP2R1A", - "CDC20", - "DYNC1LI1", - "SGO2", - "DYNC1LI2", - "ZW10", - "SGO1", - "NUP98", - "UBE2S", - "BUB1B", - "CDC16", - "B9D2", - "MAD2L1", - "ZWILCH", - "UBE2D1", - "PMF1-BGLAP", - "NUP160", - "AURKB", - "PPP1CC", - "CDC23", - "CDC26", - "CDC27", - "NUP85", - "CLASP1", - "KNTC1", - "CLASP2", - "PLK1", - "CKAP5", - "NDC80", - "RPS27", - "KIF18A", - "KIF2B", - "KIF2A", - "BIRC5", - "KIF2C", - "MAD1L1", - "ANAPC15", - "ANAPC16", - "CDCA8", - "ANAPC10", - "ANAPC11", - "NDEL1", - "RCC2", - "CENPU", - "NUDC", - "SEC13", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "ZWINT", - "PPP2R5A", - "CENPE", - "CENPF", - "CENPH", - "CENPI", - "INCENP", - "CENPK", - "CENPL", - "CENPM", - "PAFAH1B1", - "CENPN", - "CENPO", - "CENPP", - "CENPQ", - "CENPT", - "SKA1", - "SEH1L", - "AHCTF1", - "SKA2", - "DSN1", - "NUP43", - "DYNC1I2", - "DYNC1I1", - "RANBP2", - "DYNLL1", - "NDE1", - "UBE2E1", - "DYNLL2", - "PPP2CA", - "RANGAP1", - "SPC24", - "CLIP1", - "ANAPC4", - "ANAPC5", - "TAOK1", - "ANAPC7", - "ANAPC1", - "ANAPC2", - "DYNC1H1", - "NUP37", - "SPC25", - "CENPS-CORT", - "ITGB3BP", - "MAPRE1", - "NUP107", - "ERCC6L", - "MIS12", - "KANSL1", - "CENPA", - "PPP2CB", - "XPO1", - "PPP2R1B", - "NUF2", - "BUB3", - "SPDL1", - "BUB1" - ], - "module2_reactome_2022_filtered__Muscle Contraction R-HSA-397014": [ - "ACTN3", - "ACTN2", - "CACNA2D2", - "DES", - "MYL5", - "MYL6", - "MYL7", - "MYL1", - "MYL2", - "SCN10A", - "MYL3", - "MYL4", - "VIM", - "ENTHD1", - "ORAI2", - "ORAI1", - "MYL9", - "TLN1", - "CALM1", - "AHCYL1", - "CES1", - "NPRL2", - "AKAP9", - "MYL12B", - "MYL10", - "MYL11", - "NKX2-5", - "WWTR1", - "DMPK", - "TNNC2", - "PRKCA", - "MYL12A", - "TNNT1", - "TNNT2", - "TNNT3", - "SCN2B", - "SCN2A", - "GATA4", - "MYLK", - "SCN1A", - "SCN1B", - "FXYD6-FXYD2", - "ANXA1", - "MYBPC2", - "ANXA2", - "MYBPC3", - "ANXA6", - "MYBPC1", - "FXYD1", - "MMP12", - "KAT2B", - "PXDN", - "FXYD4", - "FXYD3", - "FXYD2", - "FXYD7", - "CLIC2", - "TMOD2", - "TMOD1", - "NANOS1", - "TMOD4", - "TMOD3", - "SORBS1", - "SORBS3", - "FKBP1B", - "KCNQ1", - "PKN1", - "TCAP", - "PKN2", - "RYR2", - "RYR3", - "RYR1", - "KCNA5", - "SLC8A1", - "SLC8A2", - "SLC8A3", - "SCN11A", - "DMD", - "CORIN", - "GUCY1A2", - "GUCY1A1", - "TPM4", - "TPM3", - "TPM2", - "ATP1B3", - "ATP1B2", - "HIPK2", - "ATP1B1", - "HIPK1", - "STIM1", - "SCN8A", - "ATP1A4", - "ATP1A3", - "ATP1A2", - "SLN", - "SCN7A", - "TPM1", - "MYH11", - "KCNH2", - "KCND1", - "KCND2", - "KCND3", - "CACNB2", - "FGF14", - "FGF13", - "FGF12", - "CACNB1", - "FGF11", - "VCL", - "TRIM72", - "KCNE1", - "KCNE2", - "KCNE3", - "GUCY1B1", - "KCNE4", - "KCNE5", - "CACNA1C", - "CACNA1G", - "MYL6B", - "ACTG2", - "CACNA1H", - "NPPC", - "CALD1", - "SCN9A", - "NPPA", - "ITGA1", - "TRDN", - "KCNIP1", - "KCNJ11", - "KCNIP2", - "KCNJ12", - "ABCC9", - "CAV3", - "KCNIP3", - "KCNIP4", - "KCNJ14", - "NEB", - "ACTA2", - "MYH3", - "SCN4B", - "KCNK1", - "SCN4A", - "KCNK2", - "KCNK3", - "MYH6", - "KCNK4", - "KCNK5", - "MYH8", - "KCNK6", - "KCNK7", - "KCNK9", - "NPR1", - "ITPR2", - "ITPR3", - "LMOD1", - "ITPR1", - "TRPC1", - "SCN3A", - "SCN3B", - "KCNK10", - "KCNK12", - "KCNK13", - "KCNK15", - "ATP2B4", - "KCNK16", - "ATP2B3", - "KCNK17", - "KCNK18", - "ATP2B1", - "TBX5", - "ASPH", - "PDE5A", - "RANGRF", - "CAMK2D", - "ITGB5", - "CAMK2A", - "CAMK2B", - "DYSF", - "SRI", - "ATP2A3", - "ATP2A2", - "ATP2A1", - "CACNG7", - "PLN", - "TTR", - "CACNG8", - "ALDH2", - "SCN5A", - "TNNI2", - "TNNI3", - "CAMK2G", - "CACNG4", - "KCNJ2", - "CACNG6", - "KCNJ4", - "TNNI1" - ], - "module2_reactome_2022_filtered__MyD88 Cascade Initiated On Plasma Membrane R-HSA-975871": [ - "DUSP6", - "IKBIP", - "DUSP3", - "MAP3K1", - "DUSP4", - "JUN", - "FBXW11", - "IRAK4", - "PPP2R1A", - "PPP2R5D", - "VRK3", - "DUSP7", - "MAPK14", - "MAPK11", - "NFKBIB", - "NFKBIA", - "ECSIT", - "MAPK10", - "TNIP2", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TLR5", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "HMGB1", - "NOD2", - "NLRC5", - "HOXC13", - "TANK", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "RIPK2", - "MAP3K7", - "BTRC", - "MAP3K8", - "RPS27A", - "S100A12", - "N4BP1", - "MAP2K4", - "MEF2A", - "MAP2K3", - "MEF2C", - "CHUK", - "KCNH8", - "MAP2K1", - "FOS", - "S100B", - "NFKB1", - "NFKB2", - "PPP2CA", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "PELI2", - "SAA2", - "PELI1", - "TAB3", - "UBE2V1", - "TAB2", - "TLR10", - "TAB1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "CUL1", - "AGER", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "UBC", - "PPP2R1B", - "UBB", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "MAP2K6", - "SKP1", - "MAP2K7" - ], - "module2_reactome_2022_filtered__MyD88 Dependent Cascade Initiated On Endosome R-HSA-975155": [ - "MAP3K1", - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "CUL1", - "AGER", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "IRAK4", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "NFKBIA", - "ECSIT", - "TNIP2", - "TLR9", - "IRF7", - "TLR7", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "RIPK2", - "RPS27A", - "MEF2A", - "MEF2C", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7" - ], - "module2_reactome_2022_filtered__MyD88-independent TLR4 Cascade R-HSA-166166": [ - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "UBE2D3", - "TIFA", - "UBE2D1", - "NOD1", - "UBE2D2", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "TRAF3", - "NFKB1", - "NFKB2", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "BIRC2", - "BIRC3", - "CUL1", - "AGER", - "TBK1", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "RIPK3", - "PTPN11", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "NFKBIA", - "TNIP2", - "IRF3", - "IRF7", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "FADD", - "RIPK2", - "RIPK1", - "RPS27A", - "MEF2A", - "MEF2C", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "SARM1", - "SAA2", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "IKBKE" - ], - "module2_reactome_2022_filtered__MyD88:MAL(TIRAP) Cascade Initiated On Plasma Membrane R-HSA-166058": [ - "MAP3K1", - "FBXW11", - "S100A1", - "PPP2R1A", - "VRK3", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "S100A9", - "TP53", - "S100A8", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "TIRAP", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "BTK", - "TAB3", - "TAB2", - "TAB1", - "CUL1", - "AGER", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "IRAK4", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "TLR2", - "NFKBIA", - "TLR1", - "ECSIT", - "TNIP2", - "TLR6", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "SOCS1", - "IRAK2", - "NKIRAS1", - "IRAK3", - "NKIRAS2", - "IRAK1", - "RIPK2", - "CD36", - "RPS27A", - "FGA", - "FGB", - "MEF2A", - "MEF2C", - "FGG", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "SIGIRR", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7" - ], - "module2_reactome_2022_filtered__NCAM Signaling For Neurite Out-Growth R-HSA-375165": [ - "SPTB", - "ARTN", - "PTPRA", - "CNTN2", - "COL9A2", - "COL9A1", - "ENTHD1", - "COL9A3", - "AGRN", - "SOS1", - "SPTBN4", - "SPTBN5", - "NCAM1", - "NRAS", - "SPTA1", - "SPTBN1", - "SPTBN2", - "PSPN", - "ST8SIA2", - "PRNP", - "ST8SIA4", - "GFRA2", - 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"NUP210", - "CTDNEP1", - "NUP50", - "NUP93", - "LPIN1", - "NUP98", - "NUP54", - "CDK1", - "NUP58", - "LPIN2", - "LPIN3", - "NUP205", - "SEH1L", - "LMNB1", - "NUP160", - "LMNA", - "NUP42", - "NUP43", - "NUP85", - "BANF1", - "EMD", - "NUP88", - "NEK9", - "RANBP2", - "PLK1", - "NEK6", - "NEK7", - "RNASE7", - "NUP153", - "PRKCB", - "NUP155", - "PRKCA", - "LEMD3", - "LEMD2", - "CNEP1R1", - "NUP35", - "NUP37", - "POM121", - "NUP107", - "NUP188", - "AAAS", - "TMPO", - "CCNB2", - "CCNB1", - "TPR", - "POM121C", - "NUP62" - ], - "module2_reactome_2022_filtered__Nuclear Events (Kinase And Transcription Factor Activation) R-HSA-198725": [ - "DUSP6", - "DUSP3", - "DUSP4", - "TCF12", - "PPP2R1A", - "PPP2R5D", - "VRK3", - "DNM2", - "DUSP7", - "MAPK14", - "MAPK11", - "TF", - "ID1", - "CDK5", - "ID3", - "MAPKAPK2", - "ID2", - "ID4", - "SGK1", - "TPH1", - "RRAD", - "CHD4", - "ASCL1", - "EGR1", - "EGR2", - "MEF2A", - "EGR3", - "JUND", - "MEF2D", - "EGR4", - "MEF2C", - "KCNH8", - "FOS", - "F3", - "PPP2CA", - "ARC", - "FOSL1", - "CREB1", - "GDNF", - "REST", - "VGF", - "FOSB", - "CDK5R1", - "RNASEH2A", - "CDK5R2", - "TRIB1", - "NAB1", - "NAB2", - "SRF", - "PPP2CB", - "RPS6KA5", - "LYL1", - "RPS6KA1", - "PPP2R1B", - "EP300", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "MAPK7", - "SH3GL3" - ], - "module2_reactome_2022_filtered__Nuclear Receptor Transcription Pathway R-HSA-383280": [ - "VDR", - "NRBP1", - "NR5A2", - "NR5A1", - "NCOR1", - "NCOR2", - "RARB", - "RARA", - "NR1I2", - "NR1I3", - "HNF4G", - "NR2C1", - "AREG", - "NR2C2", - "RXRA", - "RXRB", - "NR2C2AP", - "NRBF2", - "RXRG", - "ESRRB", - "MBD4", - "ESRRA", - "PPARA", - "NR1H3", - "NR1H2", - "NR1D1", - "NR1H4", - "NR2F1", - "NR0B2", - "ESRRG", - "NR0B1", - "NR1D2", - "ESR1", - "NR2F6", - "ESR2", - "NR4A3", - "NR6A1", - "NR4A2", - "HNF1A", - "PPARG", - "PGR", - "PPARD", - "THRB", - "RARG", - "THRA", - "NR2E1", - "NR2E3", - "RORB", - "RORC", - "NR3C2", - "NR3C1", - "RORA", - "NR4A1" - ], - "module2_reactome_2022_filtered__Nuclear Events Mediated By NFE2L2 R-HSA-9759194": [ - "GSR", - "TXNRD1", - "UBL4A", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "GSTA3", - "PSMC2", - "PSMC3", - "GSTA1", - "PSMC1", - "SQSTM1", - "NFE2L2", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "SLC7A11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "MYC", - "PSMD4", - "PSMD2", - "PHGDH", - "BTRC", - "RPS27A", - "NQO1", - "CREBBP", - "IDH1", - "CDKN2A", - "PCDHB16", - "TALDO1", - "PSMA6", - "PSMA7", - "PSMA4", - "GCLC", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "MAFG", - "PSMA1", - "PSME2", - "PSME3", - "MAFK", - "PSME1", - "UBA52", - "GCLM", - "TKT", - "ATF4", - "NOTCH1", - "SRXN1", - "CUL1", - "PSMB7", - "BRIP1", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "EP300", - "PSMB1", - "PRDX1", - "PSMB2", - "HMOX1", - "PSMF1", - "SKP1" - ], - "module2_reactome_2022_filtered__Nucleotide Excision Repair R-HSA-5696398": [ - "ISY1-RAB43", - "UBE2I", - "USP7", - "RBX1", - "COPS5", - "COPS4", - "COPS6", - "POLE2", - "COPS3", - "POLE3", - "COPS2", - "POLE4", - "COPS8", - "ZNF830", - "PPIE", - "UBE2N", - "PCNA", - "CCNH", - "GPS1", - "RUVBL1", - "YY1", - "TFPT", - "POTEF", - "HMGN1", - "RNF111", - "AQR", - "ACTL6A", - "USP45", - "INO80", - "XRCC1", - "PIAS1", - "PIAS3", - "MNAT1", - "INO80B", - "INO80D", - "INO80C", - "INO80E", - "UVSSA", - "XPA", - "COPS7A", - "XPC", - "UBC", - "UBB", - "SLC19A1", - "RFC4", - "RFC5", - "ACTR8", - "RFC2", - "MCRS1", - "RFC3", - "ACTR5", - "RAD23B", - "RAD23A", - "CUL4B", - "CUL4A", - "CDK7", - "PRPF19", - "CETN2", - "ELL", - "RPS27A", - "PARP2", - "PARP1", - "CHD1L", - "GTF2H1", - "RPA3", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "DDB2", - "XAB2", - "DDB1", - "ERCC1", - "ERCC4", - "POLR1A", - "ERCC5", - "POLR1B", - "ERCC2", - "ERCC3", - "ERCC8", - "ERCC6", - "TCEA1", - "UBE2V2", - "UBA52", - "POLD4", - "POLE", - "POLR2A", - "POLR2B", - "SUMO2", - "POLD1", - "POLR2C", - "SUMO1", - "EP300", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "POLK", - "POLR2F", - "LRIG1", - "POLR2G", - "POLR2H", - "LRIG3", - "POLR2I", - "NFRKB", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__Nucleotide-binding Domain, Leucine Rich Repeat Containing NLR Signaling Pathways R-HSA-168643": [ - "CARD9", - "MAPK13", - "MAPK14", - "MAPK11", - "MAPK12", - "CYLD", - "BCL2", - "UBE2N", - "APP", - "RELA", - "MEFV", - "TNFAIP3", - "NOD1", - "NLRC4", - "NOD2", - "HOXC13", - "PYCARD", - "CASP8", - "CASP9", - "PANX1", - "PSTPIP1", - "IRAK2", - "CASP4", - "IRAK1", - "CASP2", - "NLRP3", - "NLRP1", - "RIPK2", - "CASP1", - "MAP3K7", - "CHUK", - "ITCH", - "NFKB1", - "NFKB2", - "P2RX7", - "AIM2", - "TRAF6", - "BCL2L1", - "SUGT1", - "TXNIP", - "TAB3", - "UBE2V1", - "TAB2", - "TAB1", - "BIRC2", - "BIRC3", - "TXN2", - "HSP90AB1", - "IKBKB", - "HMOX1", - "AAMP", - "IKBKG", - "MAP2K6" - ], - "module2_reactome_2022_filtered__O-linked Glycosylation R-HSA-5173105": [ - "B3GALNT2", - "MUC3B", - "POC1B-GALNT4", - "SEMA5B", - "GALNT12", - "GALNT14", - "SEMA5A", - "GALNT18", - "GALNT11", - "GALNT10", - "MUC1", - "MUC2", - "C1GALT1C1", - "DAG1", - "MUCL1", - "MUC4", - "ST3GAL1", - "ST3GAL2", - "MUC6", - "ST3GAL3", - "MUC7", - "ST3GAL4", - "MUC17", - "ST6GAL1", - "MUC19", - "MUC20", - "ST6GALNAC4", - "MUC21", - "CHST4", - "SPON2", - "SPON1", - "B3GLCT", - "CFP", - "B3GNTL1", - "MUC13", - "MUC15", - "MUC16", - "MUC12", - "GALNT8", - "SBSPON", - "GALNT6", - "GALNT5", - "GALNT3", - "GALNT2", - "GALNT1", - "ADAMTS20", - "MUC5AC", - "B4GAT1", - "GALNTL6", - "GALNTL5", - "ST6GALNAC2", - "ST6GALNAC3", - "POMT1", - "POMT2", - "ADAMTS13", - "ADAMTS12", - "ADAMTS10", - "ADAMTS17", - "ADAMTS16", - "ADAMTS15", - "ADAMTS14", - "ADAMTS19", - "ADAMTS18", - "GALNT9", - "MUC5B", - "C1GALT1", - "POMGNT2", - "POMGNT1", - "B3GNT9", - "B3GNT8", - "A4GNT", - "B3GNT7", - "B3GNT6", - "B3GNT5", - "B3GNT4", - "B3GNT3", - "POMK", - "B3GNT2", - "B4GALT5", - "B4GALT6", - "GCNT1", - "THBS1", - "THSD1", - "THBS2", - "THSD4", - "ADAMTS5", - "ADAMTSL2", - "ADAMTS6", - "ADAMTSL1", - "ADAMTS3", - "ADAMTS4", - "ADAMTS1", - "ADAMTS2", - "ADAMTSL5", - "POFUT2", - "ADAMTSL4", - "ADAMTSL3", - "GCNT3", - "GCNT4", - "THSD7A", - "THSD7B", - "ADAMTS9", - "GCNT7", - "LARGE2", - "ADAMTS7", - "ADAMTS8", - "LARGE1" - ], - "module2_reactome_2022_filtered__O-linked Glycosylation Of Mucins R-HSA-913709": [ - "GALNT8", - "GALNT6", - "GALNT5", - "GALNT3", - "GALNT2", - "GALNT1", - "MUC5AC", - "MUC3B", - "GALNTL6", - "GALNTL5", - "POC1B-GALNT4", - "GALNT12", - "ST6GALNAC2", - "GALNT14", - "ST6GALNAC3", - "GALNT18", - "GALNT11", - "GALNT10", - "MUC1", - "MUC2", - "C1GALT1C1", - "MUCL1", - "MUC4", - "ST3GAL1", - "ST3GAL2", - "MUC6", - "ST3GAL3", - "GALNT9", - "MUC7", - "ST3GAL4", - "MUC5B", - "MUC17", - "ST6GAL1", - "C1GALT1", - "MUC19", - "B3GNT9", - "B3GNT8", - "A4GNT", - "B3GNT7", - "B3GNT6", - "B3GNT5", - "B3GNT4", - "B3GNT3", - "B3GNT2", - "MUC20", - "ST6GALNAC4", - "MUC21", - "B4GALT5", - "B4GALT6", - "CHST4", - "GCNT1", - "B3GNTL1", - "GCNT3", - "MUC13", - "GCNT4", - "MUC15", - "MUC16", - "GCNT7", - "MUC12" - ], - "module2_reactome_2022_filtered__Olfactory Signaling Pathway R-HSA-381753": [ - "OR11G2", - "OR2L8", - "OR1J4", - "OR2L5", - "OR1J2", - "OR2L3", - "OR1J1", - "OR2L2", - "OR4P4", - "OR4K13", - "OR6T1", - "OR4K17", - "OR4K15", - "OR4K14", - "OR13J1", - "OR1I1", - "OR2K2", - "ADCY8", - "OR10AC1", - "OR6S1", - "OR5M11", - "OR2I1P", - "OR5M10", - "OR10D3", - "OR4M2B", - "OR8B12", - "OR14K1", - "OR10C1", - "OR2J3", - "OR4N5", - "OR2J2", - "OR4N4", - "OR2J1", - "OR4N2", - "OR5P3", - "OR5P2", - "OR13H1", - "OR14J1", - "OR7A10", - "OR8U9", - "OR1G1", - "OR8U8", - "OR4M1", - "OR6Q1", - "OR8U3", - "OR8U1", - "OR56B1", - "OR56B4", - "OR7A17", - "OR52A1", - "OR1N2", - "OR1N1", - "OR5V1", - "OR6X1", - "OR52A5", - "OR10H2", - "OR10H1", - "OR10H4", - "OR10H3", - "OR10H5", - "OR1M1", - "OR4S2", - "OR2AG1", - "OR4S1", - "OR2AG2", - "OR10G3", - "OR10G2", - "OR1L8", - "OR10G4", - "OR1L6", - "OR1L4", - "OR1L3", - "OR1L1", - "OR5T3", - "OR5T2", - "OR5T1", - "OR6V1", - "OR10G7", - "OR10G6", - "OR10G9", - "OR10G8", - "OR11H1", - "OR11H2", - "OR11H4", - "OR2M7", - "OR2M5", - "OR2M4", - "OR2M3", - "OR1K1", - "OR2M2", - "OR4Q3", - "OR4Q2", - "OR2AE1", - "OR11H7", - "OR11H6", - "OR14A16", - "OR5AS1", - "OR2T8", - "OR2T7", - "OR2T6", - "OR13C8", - "OR2T5", - "OR51T1", - "OR2T4", - "OR2T3", - "OR13C9", - "OR2T2", - "OR2L13", - "OR2T1", - "OR4X2", - "OR4X1", - "OR9A4", - "OR9A2", - "OR13C2", - "OR13C4", - "OR13C3", - "OR13C5", - "OR5AR1", - "OR1Q1", - "OR2S2", - "OR51S1", - "OR4A47", - "OR14C36", - "OR5B21", - "REEP1", - "LHX2", - "OR13A1", - "CNGB1", - "OR7E24", - "OR5W2", - "OR51Q1", - "OR6Y1", - "OR5AP2", - "OR5B12", - "OR5B17", - "OR6C70", - "OR14I1", - "OR6C76", - "OR6C75", - "OR13G1", - "OR6C74", - "OR56A1", - "OR7A5", - "OR10A3", - "OR10A2", - "OR10A5", - "OR56A5", - "OR10A4", - "OR56A4", - "OR10A7", - "OR56A3", - "OR10A6", - "OR6C65", - "OR13F1", - "OR2W3", - "OR8B8", - "OR2W1", - "CNGA2", - "OR12D2", - "OR8B4", - "CNGA4", - "OR6C68", - "OR8B3", - "OR12D3", - "OR8B2", - "OR5AU1", - "OR51V1", - "OR2V2", - "OR2V1", - "OR11A1", - "OR8A1", - "OR52W1", - "OR1S2", - "OR1S1", - "OR13D1", - "OR8H3", - "OR8H2", - "OR8H1", - "EBF1", - "OR52M1", - "OR10V1", - "OR4C13", - "OR4C12", - "OR4C11", - "OR6C6", - "OR2AT4", - "OR6C4", - "OR4C16", - "OR5A2", - "OR6C3", - "OR8G5", - "OR4C15", - "OR5A1", - "OR5AK2", - "OR6C2", - "OR6C1", - "OR8G1", - "OR9I1", - "OR52L1", - "OR6B3", - "OR7D4", - "OR6B2", - "OR2A25", - "OR6B1", - "OR7D2", - "OR51I2", - "OR51I1", - "OR52K2", - "OR52K1", - "OR10T2", - "OR2A12", - "OR9G9", - "OR4D11", - "OR2Z1", - "OR4D10", - "OR6A2", - "OR7C2", - "OR9G4", - "OR2A14", - "OR7C1", - "OR52J3", - "OR10R2", - "OR10S1", - "OR2Y1", - "OR8D4", - "OR8D2", - "OR8D1", - "OR4D2", - "OR4D1", - "OR5F1", - "GNG13", - "OR10Z1", - "OR52R1", - "OR4A15", - "OR4C6", - "OR4C5", - "OR3A3", - "OR4A16", - "OR14A2", - "OR3A2", - "OR4C3", - "OR3A1", - "OR8K5", - "OR8K3", - "OR8K1", - "OR4C46", - "OR4C45", - "OR5AN1", - "OR4B1", - "OR6F1", - "OR8J3", - "OR8J2", - "OR8J1", - "OR51M1", - "ANO2", - "OR10X1", - "OR4A5", - "OR5C1", - "OR7G3", - "OR7G2", - "OR7G1", - "OR8I2", - "OR9K2", - "OR51L1", - "OR52N2", - "OR52N1", - "OR52N5", - "OR52N4", - "OR10W1", - "OR5D18", - "OR5AL1", - "OR5D13", - "OR5B3", - "OR5D16", - "OR5B2", - "OR5D14", - "OR52E1", - "OR2D3", - "OR1B1", - "OR2D2", - "OR52E5", - "OR52E4", - "OR5J2", - "OR52E2", - "OR5AC2", - "OR52E8", - "OR52E6", - "OR1A2", - "OR2C3", - "OR1A1", - "OR6K6", - "OR2C1", - "OR51B2", - "OR6K3", - "OR5I1", - "OR6K2", - "OR52D1", - "OR9Q2", - "OR9Q1", - "OR2AK2", - "OR51B6", - "OR51B5", - "OR51B4", - "OR2B6", - "OR4F6", - "OR2B3", - "OR5H6", - "OR10K2", - "OR2B2", - "OR4F3", - "OR51A2", - "OR5H2", - "OR5H1", - "OR6J1", - "OR10K1", - "GNAL", - "OR2AJ1", - "GNB1", - "OR51A4", - "OR2A7", - "OR2A5", - "OR2A4", - "OR51A7", - "OR2A2", - "OR10J1", - "OR2A1", - "OR10J4", - "OR4E2", - "OR4E1", - "OR52B2", - "OR10J5", - "OR11L1", - "OR52B6", - "OR4D9", - "OR4D6", - "OR4D5", - "OR51G2", - "OR51G1", - "OR52I2", - "OR1F1", - 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"PSMB10", - "ORC6", - "PSMD9", - "ORC5", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "ORC4", - "PSMD3", - "ORC3", - "PSMD4", - "PSMD2", - "RPS27A", - "PSMA6", - "CCNA2", - "PSMA7", - "CCNA1", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "SLC25A15", - "MCM8", - "CUL1", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "SKP1", - "SKP2" - ], - "module2_reactome_2022_filtered__Organelle Biogenesis And Maintenance R-HSA-1852241": [ - "DYNC2H1", - "BBIP1", - "IFT81", - "PPP2R1A", - "IFT80", - "PRKAB1", - "PRKAB2", - "MAPK14", - "MAPK11", - "MAPK12", - "IFT88", - "CCP110", - "CEP83", - "TFAM", - "CEP89", - "CALM1", - "PRKAA2", - "LIN9", - "SSNA1", - "IFT172", - "AKAP9", - "CSNK1D", - "TMEM67", - "IFT74", - "TBL1X", - "CEP97", - "PPARGC1A", - "RAB11FIP3", - "PPARGC1B", - "CREBBP", - "DMAC2L", - "PLK4", - "PLK1", - "APOOL", - "RAB3IP", - 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"MEF2C", - "PPARA", - "ATP5PD", - "MTX2", - "SEPTIN2", - "NDE1", - "ATP5PB", - "IFT140", - "WDR19", - "DYNLL2", - "RAB11A", - "TUBB4B", - "TUBB4A", - "LZTFL1", - "ATAT1", - "NEDD1", - "TTC8", - "TRIP11", - "ATP5PO", - "DYNC1H1", - "MAPRE1", - "DYNC2I2", - "TRAF3IP1", - "ALMS1", - "PRKAG2", - "PRKAG3", - "PRKAG1", - "TUBA1A", - "ATP5F1A", - "ATP5F1B", - "SAMM50", - "ATP5F1C", - "PCM1", - "TCTN3", - "TCTN2", - "TCTN1", - "NUDT19", - "RAB8A", - "RHO", - "MTX1", - "CCT8", - "NINL", - "DYNC2I1", - "CCT5", - "RPGRIP1L", - "TUBA4A", - "TBL1XR1", - "TTC30A", - "TTC30B", - "B9D2", - "B9D1", - "CRTC3", - "CRTC1", - "CRTC2", - "RXRA", - "ASAP1", - "NPHP1", - "CEP250", - "CLASP1", - "NPHP4", - "ACSS2", - "PCNT", - "ESRRA", - "ODF2", - "IDH2", - "TTC21B", - "KIF24", - "POLRMT", - "CYS1", - "CKAP5", - "IFT20", - "CREB1", - "IFT22", - "TMEM11", - "GDNF", - "DNAJC11", - "DYNLRB1", - "DYNLRB2", - "POLG2", - "CNGB1", - "IFT27", - "ARL6", - "ATP5MC3", - "ARL3", - "MCHR1", - "ATP5MC1", - "ATP5MC2", - "KIF3C", - "KIF3B", - "KIF3A", - "NEK2", - "MARK4", - "HSPA9", - "MKKS", - "HSP90AA1", - "IQCB1", - "SSTR3", - "AHI1", - "IFT43", - "TMEM216", - "CEP162", - "CARM1", - "CDK1", - "CYCS", - "IFT46", - "TTC26", - "YWHAG", - "YWHAE", - "CEP164", - "CHD9", - "NRF1", - "TNPO1", - "DYNC2LI1", - "CNGA2", - "TWNK", - "CNGA4", - "CEP290", - "CEP135", - "DYNLT2", - "SMOX", - "CEP131", - "DYNLT5", - "HAUS5", - "IMMT", - "HAUS4", - "SCLT1", - "HAUS7", - "HAUS6", - "HAUS1", - "HAUS3", - "HAUS2", - "SSBP1", - "CLUAP1", - "HDAC6", - "HDAC3", - "PKD1", - "SMARCD3", - "ALAS1", - "DCTN3", - "DCTN2", - "PERM1", - "DCTN1", - "CHCHD6", - "CHCHD3", - "ARL13B", - "HAUS8", - "IFT52", - "CEP152", - "PDE6D", - "IFT57" - ], - "module2_reactome_2022_filtered__Oxidative Stress Induced Senescence R-HSA-2559580": [ - "H2AZ2", - "CBX8", - "CBX6", - "H2AZ1", - "H2BC9", - "JUN", - "CBX4", - "RING1", - "H2BC4", - "CBX2", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "MAPK14", - "H2BC13", - "MAPK11", - "H2BC14", - "H2BC15", - "H2AJ", - "MAPK10", - "AGO4", - "CDK6", - "MAPKAPK5", - "CDK4", - "H2BC11", - "MAPKAPK2", - "AGO3", - "H2BC12", - "MAPKAPK3", - "AGO1", - "TNIK", - "TNRC6A", - "TNRC6B", - "TP53", - "TNRC6C", - "RNF2", - "H2AB1", - "E2F1", - "RBBP4", - "E2F2", - "E2F3", - "RBBP7", - "RPS27A", - "H3-3B", - "MAP3K5", - "H3C7", - "KDM6B", - "MAP2K4", - "PHC1", - "SUZ12", - "CDKN2C", - "EED", - "PHC3", - "CDKN2D", - "MAP2K3", - "PHC2", - "CDKN2A", - "H2AC7", - "CDKN2B", - "H2AC6", - "FOS", - "MINK1", - "H2AC4", - "MOV10", - "TFDP1", - "TFDP2", - "H2AC20", - "H4C15", - "COMMD3-BMI1", - "MDM4", - "UBA52", - "EZH2", - "MDM2", - "TXN2", - "H2AX", - "H2AC19", - "H2AC14", - "H2BC26", - "MAPK9", - "MAPK8", - "SCMH1", - "UBC", - "H2BC21", - "UBB", - "H3C13", - "MAPK3", - "IFNB1", - "MAPK1", - "MAP2K6", - "MAP4K4", - "MAP2K7" - ], - "module2_reactome_2022_filtered__Oxygen-dependent Proline Hydroxylation Of Hypoxia-inducible Factor Alpha R-HSA-1234176": [ - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - 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"PSMD9", - "PSMD7", - "WNT11", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "DVL2", - "PSMD4", - "DVL3", - "AP2S1", - "PSMD2", - "WNT1", - "RPS27A", - "WNT4", - "FZD2", - "FZD1", - "PRKCG", - "FZD4", - "FZD6", - "FZD8", - "FZD7", - "AP2B1", - "FZD9", - "PRKCB", - "PRKCA", - "RNASE1", - "PSMA6", - "VANGL2", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "DAAM1", - "PSME1", - "UBA52", - "AP2A2", - "ARRB2", - "PRICKLE1", - "AP2A1", - "RORA", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "RAC2", - "AP2M1" - ], - "module2_reactome_2022_filtered__PI Metabolism R-HSA-1483255": [ - "PLEKHA2", - "PLEKHA3", - "PLEKHA1", - "PLEKHA6", - "OCRL", - "PLEKHA4", - "PLEKHA5", - "PTPN13", - "BMX", - "PLEKHA8", - "RUFY1", - "PIK3CB", - "PIK3CA", - "PIKFYVE", - "PI4KB", - "PI4KA", - "PIK3C3", - "PI4K2B", - "PI4K2A", - "MTMR1", - "MTMR2", - "MTMR3", - "ARF3", - 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"RPS6KA2", - "MAPK3", - "NEFL", - "HRAS", - "MAPK1", - "CAMK2G", - "PRKACB", - "GIT1" - ], - "module2_reactome_2022_filtered__Post-translational Modification: Synthesis Of GPI-anchored Proteins R-HSA-163125": [ - "GP2", - "LSAMP", - "DPM1", - "PLET1", - "DPM2", - "DPM3", - "PIGC", - "BST1", - "PIGF", - "ALPP", - "RTN4RL1", - "XPNPEP2", - "CEACAM7", - "NRN1L", - "RTN4RL2", - "PIGB", - "CEACAM5", - "PIGA", - "ALPL", - "LY6E", - "MELTF", - "PIGL", - "CNTN3", - "LY6D", - "PIGK", - "SPACA4", - "CNTN4", - "PIGN", - "CNTN5", - "PIGM", - "PIGH", - "LY6H", - "MDGA2", - "PIGG", - "ULBP2", - "CPM", - "ALPG", - "NRN1", - "PGAP1", - "GPAA1", - "THY1", - "OTOA", - "LY6G6C", - "LY6G6F", - "OPCML", - "TECTA", - "TECTB", - "NTNG2", - "GPLD1", - "CD52", - "NEGR1", - "LOC124905743", - "IZUMO1R", - "PLAUR", - "PRSS41", - "RAET1G", - "VNN1", - "VNN2", - "CD109", - "LYPD6B", - "RAET1L", - "RECK", - "FOLR2", - "SPRN", - "PIGT", - "PIGS", - "LY6K", - "MDGA1", - "PIGV", - "MSLN", - "PIGU", - "PIGP", - "PIGO", - 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"H2BC14", - "H2BC15", - "KAT2B", - "ELF3", - "H2AJ", - "KAT2A", - "AGO4", - "AGO2", - "H2BC11", - "AGO3", - "H2BC12", - "AGO1", - "TNRC6A", - "TNRC6B", - "TP53", - "TNRC6C", - "MAML3", - "MAML2", - "MAML1", - "H2AB1", - "CCND1", - "E2F1", - "E2F3", - "H3-3B", - "H3C7", - "CREBBP", - "PRKCI", - "H2AC7", - "H2AC6", - "H2AC4", - "MOV10", - "SNW1", - "TFDP1", - "TFDP2", - "H2AC20", - "H4C15", - "NOTCH3", - "H2AX", - "NOTCH4", - "MAMLD1", - "NOTCH1", - "NOTCH2", - "H2AC19", - "RBPJ", - "H2AC14", - "H2BC26", - "H2BC21", - "EP300", - "H3C13" - ], - "module2_reactome_2022_filtered__Processing Of Capped Intron-Containing Pre-mRNA R-HSA-72203": [ - "ISY1-RAB43", - "GPKOW", - "NUP214", - "UPF3B", - "NUP210", - "CRNKL1", - "SRRM1", - "SRRM2", - "CCAR1", - "HNRNPM", - "PHF5A", - "NUP93", - "DDX39A", - "HNRNPK", - "DDX39B", - "HNRNPL", - "NUP98", - "SARNP", - "ZC3H11A", - "CD2BP2", - "HNRNPF", - "HNRNPC", - "HNRNPD", - "SF3B5", - "NUP205", - "SF3B6", - "SF3B3", - "SF3B4", - "PRCC", - "WBP4", - "HNRNPU", - "HNRNPR", - "MAGOH", - "NXF1", - "NXF2", - "SNRPD3", - "SNRPD2", - "NUP85", - "PABPN1", - "DHX38", - "NUP88", - "SF3B1", - "SF3B2", - "AQR", - "PRPF38A", - "TFIP11", - "SNW1", - "RNPS1", - "SF1", - "SUGP1", - "POM121", - "CSTF3", - "PCBP1", - "AAAS", - "GLE1", - "POM121C", - "PCBP2", - "SNRNP70", - "DHX15", - "NUP62", - "CSTF2", - "DHX16", - "CSTF1", - "ZRSR2", - "SEC13", - "NDC1", - "PRPF40A", - "WDR33", - "MAGOHB", - "PRPF19", - "SLU7", - "SLBP", - "DHX9", - "RBM8A", - "PQBP1", - "PCF11", - "RANBP2", - "SF3A2", - "SF3A3", - "METTL3", - "RNASE7", - "THOC2", - "THOC1", - "THOC3", - "THOC6", - "THOC5", - "THOC7", - "GCFC2", - "XAB2", - "NUDT21", - "HNRNPUL1", - "HNRNPA2B1", - "HNRNPH1", - "HNRNPH2", - "PRPF31", - "SF3A1", - "FYTTD1", - "NUP188", - "SRSF10", - "SRSF11", - "CPSF6", - "PPIL1", - "SNRPN", - "CPSF7", - "PPIL3", - "CPSF2", - "CPSF1", - "CPSF4", - "ALYREF", - "CPSF3", - "CWC15", - "GTF2F1", - "CHERP", - "GTF2F2", - "WBP11", - "SNRPA", - "SRSF3", - "SRSF4", - "SNRNP27", - "SNRPF", - "SRSF5", - "PPIH", - "SNRPG", - "SRSF6", - "PPIL4", - "SNRNP25", - "SRSF7", - "CHTOP", - "SNRPE", - "PPIE", - "PPIL6", - "SNRPB", - "SRSF9", - "SNRPC", - "PDCD7", - "SRRT", - "SRSF1", - "SRSF2", - "CWC25", - "CASC3", - "CWC27", - "NUP160", - "EFTUD2", - "CWC22", - "TXNL4A", - "ZCRB1", - "BUD31", - "LSM2", - "NUP153", - "NUP155", - "LSM6", - "LSM5", - "LSM4", - "LSM3", - "PRPF6", - "LSM8", - "PRPF8", - "LSM7", - "PRPF4", - "PRPF3", - "SNRNP200", - "DDX5", - "TPR", - "ZMAT5", - "PAPOLA", - "RBM17", - "FUS", - "NCBP2", - "HSPA8", - "NCBP1", - "CDC5L", - "NUP133", - "NUP50", - "DNAJC8", - "NUP54", - "SYF2", - "NUP58", - "RBM22", - "SEH1L", - "USP39", - "DDX46", - "DDX42", - "RNPC3", - "ELAVL1", - "ELAVL2", - "SNU13", - "PPWD1", - "U2AF1L4", - "PTBP1", - "METTL14", - "NUP42", - "PLRG1", - "NUP43", - "SYMPK", - "EIF4E", - "CTNNBL1", - "BCAS2", - "WTAP", - "CDC40", - "U2SURP", - "SNRNP40", - "NUP35", - "RBM42", - "SNRNP48", - "SNRPA1", - "RBMX", - "NUP37", - "EIF4A3", - "COLEC12", - "LUZP4", - 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"PPP2R1A", - "CDC20", - "DYNC1LI1", - "SGO2", - "DYNC1LI2", - "ZW10", - "SGO1", - "NUP98", - "BUB1B", - "B9D2", - "PFN2", - "PFN1", - "MAD2L1", - "ZWILCH", - "PMF1-BGLAP", - "NUP160", - "AURKB", - "PPP1CC", - "NUP85", - "CLASP1", - "POTEF", - "KNTC1", - "CLASP2", - "PLK1", - "CKAP5", - "NDC80", - "DIAPH1", - "RPS27", - "DIAPH2", - "KIF18A", - "DIAPH3", - "KIF2B", - "KIF2A", - "BIRC5", - "KIF2C", - "MAD1L1", - "ACTG1", - "CDCA8", - "NDEL1", - "RCC2", - "SRGAP2", - "CENPU", - "NUDC", - "RHOA", - "SEC13", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "RHOD", - "ZWINT", - "PPP2R5A", - "RHOB", - "RHOC", - "CENPE", - "CENPF", - "CENPH", - "CENPI", - "INCENP", - "CENPK", - "CENPL", - "CENPM", - "PAFAH1B1", - "CENPN", - "CENPO", - "CENPP", - "CENPQ", - "CENPT", - "SKA1", - "SEH1L", - "AHCTF1", - "SKA2", - "DSN1", - "SCAI", - "NUP43", - "DVL2", - "DVL3", - "DYNC1I2", - "DYNC1I1", - "RANBP2", - "DYNLL1", - "NDE1", - "DYNLL2", - "RNASE1", - "CDC42", - "PPP2CA", - "RANGAP1", - 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"PKN1", - "PKN3", - "PKN2", - "H2AX", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "H3C13" - ], - "module2_reactome_2022_filtered__RHOA GTPase Cycle R-HSA-8980692": [ - "CAVIN1", - "STARD8", - "ARHGAP29", - "ARHGAP28", - "MACO1", - "ARHGAP26", - "TEX2", - "DHDDS", - "ARHGAP24", - "DEF6", - "ARHGAP32", - "OBSCN", - "ARHGAP31", - "DEPDC1B", - "ARHGAP30", - "NGEF", - "TMEM87A", - "ROCK1", - "ROCK2", - "ARHGAP39", - "ARHGAP35", - "ARHGAP45", - "SLK", - "ARHGAP44", - "PREX2", - "ARHGAP42", - "DDRGK1", - "ARHGAP40", - "RACGAP1", - "PREX1", - "EMC3", - "STOM", - "STX5", - "YKT6", - "ARHGEF40", - "ARHGEF12", - "ARHGEF11", - "ACBD5", - "PRPF38B", - "ARHGEF15", - "ATP6AP1", - "MCAM", - "STARD13", - "ARHGEF18", - "ARHGEF17", - "ARHGEF19", - "DIAPH1", - "VANGL1", - "ARHGAP10", - "DIAPH3", - "PIK3R1", - "ARHGEF10", - "VAMP3", - "ARHGAP8", - "ARHGEF25", - "RTKN", - "ARHGAP9", - "TRIO", - "TFRC", - "FAM13A", - "ARHGEF28", - "ARHGAP18", - "ARHGAP1", - "ARHGAP6", - "ARHGAP4", - "AAAS", - "ARHGAP5", - "ABR", - "ARHGAP23", - "STK10", - "TMPO", - "ARHGAP22", - "ARHGAP21", - "ARHGAP20", - "FLOT2", - "HMOX2", - "FLOT1", - "SRGAP1", - "RHOA", - "JUP", - "CCDC115", - "CAV1", - "ARAP3", - "ARAP1", - "ARAP2", - "GMIP", - "VAV1", - "VAV2", - "VAV3", - "ANLN", - "ARHGEF4", - "ARHGEF5", - "ARHGEF2", - "ARHGEF3", - "DOCK2", - "ARHGEF7", - "ARHGEF1", - "SNAP23", - "RASGRF2", - "IQGAP1", - "PIK3R2", - "IQGAP3", - "ARHGEF10L", - "KALRN", - "SCFD1", - "MCF2L", - "C1QBP", - "RHPN2", - "RHPN1", - "LBR", - "ECT2", - "PLEKHG4", - "PLEKHG5", - "BCAP31", - "PLEKHG3", - "MCF2", - "PLEKHG6", - "KTN1", - "ERBIN", - "MYO9A", - "MYO9B", - "FARP1", - "TIAM1", - "TJP2", - "FMNL3", - "VAPB", - "DLC1", - "PKN1", - "PKN3", - "DAAM1", - "PKN2", - "ARHGAP11B", - "ABCD3", - "ARHGAP11A", - "PRKCSH", - "SOWAHC", - "FAF2", - "ACTC1", - "AKAP13", - "LMAN1", - "ARHGDIA", - "PGRMC2", - "STBD1", - "ARHGDIB", - "TAGAP", - "OPHN1", - "PCDH7" - ], - "module2_reactome_2022_filtered__RHOB GTPase Cycle R-HSA-9013026": [ - "CAVIN1", - 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"ARHGEF5", - "CCDC187", - "ROCK1", - "ROCK2", - "ARHGEF1", - "IQGAP1", - "ARHGAP39", - "IQGAP3", - "ARHGEF10L", - "ARHGAP35", - "SLK", - "MCF2L", - "RACGAP1", - "PREX1", - "C1QBP", - "STOM", - "LBR", - "STX5", - "ARHGEF40", - "ARHGEF12", - "ARHGEF11", - "ACBD5", - "MCAM", - "MCF2", - "STARD13", - "ARHGEF17", - "ERBIN", - "MYO9B", - "DIAPH1", - "TJP2", - "VANGL1", - "DIAPH3", - "FMNL2", - "FMNL3", - "PIK3R1", - "VAPB", - "DLC1", - "PKN1", - "PKN3", - "DAAM1", - "PKN2", - "ARHGEF10", - "VAMP3", - "ABCD3", - "ARHGEF25", - "RTKN", - "TFRC", - "ARHGEF28", - "ARHGAP18", - "ARHGAP1", - "ARHGAP5", - "ABR", - "AKAP13", - "LMAN1", - "STK10", - "TMPO", - "ARHGDIA", - "ARHGAP21", - "OPHN1", - "FLOT2", - "FLOT1" - ], - "module2_reactome_2022_filtered__RHOD GTPase Cycle R-HSA-9013405": [ - "ANKFY1", - "ACTN1", - "CAV1", - "VRK2", - "RHOD", - "ARHGAP26", - "BUB1B-PAK6", - "GOLGA8R", - "ARHGAP32", - "DEPDC1B", - "FILIP1", - "PLXNB1", - "STEAP3", - "RAB7A", - "WHAMM", - "CPNE8", - "LMNB1", - "MOSPD2", - "ARHGAP39", - "PIK3R2", - "ARHGAP35", - "TOR1AIP1", - "HINT2", - "RACGAP1", - "CAPZB", - "EMD", - "LBR", - "MCAM", - "LEMD3", - "ARHGAP12", - "DIAPH1", - "DIAPH2", - "VANGL1", - "DIAPH3", - "ESYT1", - "PIK3R1", - "VAPB", - "VAMP3", - "DBN1", - "ARHGAP1", - "ARHGAP17", - "ADD3", - "ARHGAP5", - "SLC4A7", - "LMAN1", - "TMPO", - "ARHGAP21", - "PGRMC2", - "AKAP12", - "STBD1", - "EFHD2", - "PLXNA1" - ], - "module2_reactome_2022_filtered__RHOG GTPase Cycle R-HSA-9013408": [ - "CAV1", - "VRK2", - "RHOG", - "HSPE1", - "VAV1", - "VAV2", - "VAV3", - "LETM1", - "ARHGAP32", - "DEPDC1B", - "ELMO2", - "ARHGEF5", - "DSG2", - "NDUFS3", - "DOCK3", - "EPHA2", - "DOCK2", - "DOCK1", - "RAB7A", - "CYFIP1", - "ANKLE2", - "SHMT2", - "GARRE1", - "ARHGAP39", - "IQGAP2", - "KALRN", - "ARHGAP35", - "MCF2L", - "PREX1", - "EMD", - "LBR", - "STX5", - "YKT6", - "PAK4", - "PLEKHG3", - "ARHGEF16", - "MCAM", - "MCF2", - "NDUFA5", - "KTN1", - "ERBIN", - "LEMD3", - "MPP7", - "ARFGAP3", - "CDC42", - "VANGL1", - "DIAPH3", - "ESYT1", - "PIK3R1", - "VAPB", - "LAMTOR1", - "MAP3K11", - "CDC42EP1", - "PKN2", - "VAMP3", - "ITGB1", - "DOCK5", - "DOCK4", - "PRKCSH", - "TRIO", - "ARHGEF26", - "TFRC", - "ITSN1", - "ARHGAP1", - "ARHGDIG", - "ARHGAP5", - "LMAN1", - "TMPO", - "ARHGDIA", - "ARHGAP21", - "PGRMC2", - "STBD1", - "ARHGDIB", - "OPHN1" - ], - "module2_reactome_2022_filtered__RHOJ GTPase Cycle R-HSA-9013409": [ - "FNBP1", - "JUP", - "CAV1", - "OCRL", - "SCRIB", - "CDC42BPB", - "ARHGAP26", - "CDC42BPA", - "NIPSNAP2", - "ARHGAP32", - "DEPDC1B", - "RHOJ", - "STEAP3", - "RAB7A", - "ARHGEF7", - "CPNE8", - "SNAP23", - "SLC1A5", - "PIK3R2", - "IQGAP3", - "FNBP1L", - "ARHGAP35", - "PREX1", - "STOM", - "PAK4", - "WWP2", - "MPP7", - "CDC42", - "VANGL1", - "DIAPH3", - "FMNL3", - "PIK3R1", - "LAMTOR1", - "PKN1", - "CDC42EP1", - "PKN2", - "VAMP3", - "TRIO", - "TFRC", - "WIPF2", - "DOCK8", - "WAS", - "WASL", - "ARHGAP1", - "ARHGAP5", - "SLC4A7", - "TMPO", - "ARHGAP21", - "SYDE1", - "GJA1", - "ARL13B", - "OPHN1", - "GIT2", - "GIT1" - ], - "module2_reactome_2022_filtered__RHOQ GTPase Cycle R-HSA-9013406": [ - "FNBP1", - "JUP", - "CAV1", - "SCRIB", - "CDC42BPB", - "ARHGAP26", - "CDC42BPA", - "ARHGAP33", - "RHOQ", - "ARHGAP32", - "OBSCN", - "DEPDC1B", - "ARHGEF9", - "STEAP3", - "RAB7A", - "ARHGEF7", - "CPNE8", - "SNAP23", - "IQGAP1", - "SLC1A5", - "IQGAP3", - "ARHGAP35", - "PREX1", - "STOM", - "PAK4", - "PLEKHG3", - "WWP2", - "MPP7", - "CDC42", - "VANGL1", - "DIAPH3", - "DLC1", - "GFOD1", - "CDC42EP4", - "CDC42EP3", - "LAMTOR1", - "PKN1", - "CDC42EP2", - "CDC42EP1", - "GOPC", - "PKN2", - "VAMP3", - "CFTR", - "TRIP10", - "TFRC", - "ITSN1", - "WASL", - "ARHGAP1", - "ARHGAP17", - "ARHGAP5", - "SLC4A7", - "ARHGAP21", - "SYDE1", - "GJA1", - "ARL13B", - "OPHN1", - "SRGAP2", - "GIT2", - "GIT1" - ], - "module2_reactome_2022_filtered__RNA Polymerase I Promoter Clearance R-HSA-73854": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "CBX3", - "H2BC4", - "H2BC5", - "GATAD2A", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "GATAD2B", - "H2BC13", - "H2BC14", - "H2BC15", - "KAT2B", - "CDK7", - "H2AJ", - "KAT2A", - "H2BC11", - "H2BC12", - "DPEP3", - "DPEP2", - "RRN3", - "CCNH", - "CHD4", - "H2AB1", - "CHD3", - "MTA1", - "MTA2", - "TAF1C", - "TAF1D", - "TAF1A", - "RBBP4", - "TAF1B", - "RBBP7", - "MTA3", - "H3-3B", - "H3C7", - "TBP", - "H2AC7", - "H2AC6", - "GTF2H1", - "H2AC4", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "POLR1A", - "H2AC20", - "H4C15", - "POLR1B", - "ERCC2", - "ERCC3", - "POLR1D", - "POLR1E", - "POLR1F", - "ERCC6", - "POLR1G", - "POLR1H", - "MNAT1", - "H2AX", - "HDAC1", - "HDAC2", - "EHMT2", - "TTF1", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "H3C13", - "POLR2E", - "MAPK3", - "POLR2F", - "UBTF", - "POLR2H", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__RNA Polymerase I Promoter Escape R-HSA-73772": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "CBX3", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "H2BC13", - "H2BC14", - "H2BC15", - "CDK7", - "H2AJ", - "H2BC11", - "H2BC12", - "RRN3", - "CCNH", - "H2AB1", - "TAF1C", - "TAF1D", - "TAF1A", - "TAF1B", - "H3-3B", - "H3C7", - "TBP", - "H2AC7", - "H2AC6", - "GTF2H1", - "H2AC4", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "POLR1A", - "H2AC20", - "H4C15", - "POLR1B", - "ERCC2", - "ERCC3", - "POLR1D", - "POLR1E", - "POLR1F", - "POLR1G", - "POLR1H", - "MNAT1", - "H2AX", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "H3C13", - "POLR2E", - "POLR2F", - "UBTF", - "POLR2H", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__RNA Polymerase I Transcription R-HSA-73864": [ - "H2AZ2", - "CAVIN1", - "H2AZ1", - "H2BC9", - "CBX3", - "H2BC4", - "H2BC5", - "GATAD2A", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "GATAD2B", - "H2BC13", - "H2BC14", - "H2BC15", - "KAT2B", - "CDK7", - "H2AJ", - "KAT2A", - "H2BC11", - "H2BC12", - "DPEP3", - "DPEP2", - "RRN3", - "CCNH", - "CHD4", - "H2AB1", - "CHD3", - "MTA1", - "MTA2", - "TAF1C", - "TAF1D", - "TAF1A", - "RBBP4", - "TAF1B", - "RBBP7", - "MTA3", - "H3-3B", - "H3C7", - "TBP", - "H2AC7", - "H2AC6", - "GTF2H1", - "H2AC4", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "POLR1A", - "H2AC20", - "H4C15", - "POLR1B", - "ERCC2", - "ERCC3", - "POLR1D", - "POLR1E", - "POLR1F", - "ERCC6", - "POLR1G", - "POLR1H", - "MNAT1", - "H2AX", - "HDAC1", - "HDAC2", - "EHMT2", - "TTF1", - "H2AC19", - "H2AC14", - "H2BC26", - "H2BC21", - "H3C13", - "POLR2E", - "MAPK3", - "POLR2F", - "UBTF", - "POLR2H", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__RNA Polymerase II Pre-transcription Events R-HSA-674695": [ - "SUPT16H", - "NCBP2", - "NCBP1", - "GTF2F1", - "GTF2F2", - "CDK9", - "SKIC8", - "CDK7", - "GTF2A1", - "GTF2A2", - "SUPT6H", - "CCNK", - "CCNT2", - "CCNT1", - "ELL", - "CCNH", - "GTF2B", - "IWS1", - "MLLT1", - "SUPT4H1", - "RTF1", - "MLLT3", - "AFF4", - "GTF2E1", - "CDC73", - "GTF2E2", - "TAF1L", - "NELFCD", - "LEO1", - "DMAP1", - "TAF9B", - "TBP", - "TAF15", - "TAF13", - "EAF1", - "TAF11", - "TAF12", - "ELOA2", - "GTF2H1", - "TAF10", - "GTF2H2", - "SSRP1", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "SUPT5H", - "TAF1", - "ERCC2", - "ERCC3", - "TAF9", - "TAF7", - "TCEA1", - "TAF6", - "TAF5", - "MNAT1", - "TAF4", - "PAF1", - "TAF3", - "TAF2", - "CTDP1", - "NELFA", - "CTR9", - "POLR2A", - "TAF7L", - "POLR2B", - "POLR2C", - "POLR2D", - "POLR2E", - "ELOA", - "NELFE", - "POLR2F", - "ELOB", - "POLR2G", - "ELOC", - "POLR2H", - "NELFB", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__RNA Polymerase II Transcription Termination R-HSA-73856": [ - "CPSF6", - "CPSF7", - "CPSF2", - "NCBP2", - "CPSF1", - "CPSF4", - "ALYREF", - "CPSF3", - "NCBP1", - "UPF3B", - "SRRM1", - "WDR33", - "MAGOHB", - "DDX39A", - "DDX39B", - "SARNP", - "ZC3H11A", - "SRSF3", - "SRSF4", - "SNRPF", - "SRSF5", - "SNRPG", - "SRSF6", - "SLU7", - "SRSF7", - "CHTOP", - "SNRPE", - "SNRPB", - "SRSF9", - "SLBP", - "ZNF473", - "SRSF1", - "RBM8A", - "SRSF2", - "CASC3", - "U2AF1L4", - "LSM11", - "MAGOH", - "LSM10", - "PCF11", - "SNRPD3", - "PABPN1", - "DHX38", - "SYMPK", - "THOC2", - "THOC1", - "THOC3", - "THOC6", - "CDC40", - "THOC5", - "THOC7", - "NUDT21", - "RNPS1", - "FYTTD1", - "EIF4A3", - "COLEC12", - "LUZP4", - "POLDIP3", - "CSTF3", - "U2AF2", - "U2AF1", - "CSTF2T", - "FIP1L1", - "CSTF2", - "CSTF1", - "PAPOLA", - "SRSF11" - ], - "module2_reactome_2022_filtered__RNA Polymerase II Transcribes snRNA Genes R-HSA-6807505": [ - "POU2F1", - "POU2F2", - "NCBP2", - "NCBP1", - "GTF2F1", - "GTF2F2", - "CDK9", - "CDK7", - "SSU72", - "DAND5", - "GTF2A1", - "INTS12", - "GTF2A2", - "INTS13", - "INTS14", - "CCNK", - "CCNT2", - "CCNT1", - "ELL", - "INTS10", - "SRRT", - "GTF2B", - "INTS11", - "SUPT4H1", - "ZC3H8", - "GTF2E1", - "RPAP2", - "GTF2E2", - "PCF11", - "SNAPC2", - "SNAPC3", - "SNAPC4", - "SNAPC5", - "RPRD1B", - "SNAPC1", - "TBP", - "ZNF143", - "INTS2", - "RPRD1A", - "TAF13", - "INTS1", - "INTS4", - "NABP1", - "TAF11", - "INTS3", - "RPRD2", - "ELL2", - "ELL3", - "SUPT5H", - "INTS6", - "TAF9", - "INTS5", - "NABP2", - "TAF8", - "INTS8", - "INTS7", - "TAF6", - "TAF5", - "INTS9", - "ICE2", - "ICE1", - "PHAX", - "POLR2A", - "POLR2B", - "POLR2C", - "POLR2D", - "POLR2E", - "POLR2F", - "POLR2G", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__RUNX1 Regulates Genes Involved In Megakaryocyte Differentiation And Platelet Function R-HSA-8936459": [ - "H2AZ2", - "H2AZ1", - "H2BC9", - "SETD1A", - "H2BC4", - "H2BC5", - "SETD1B", - "RUNX1", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "H2BC13", - "H2BC14", - "H2BC15", - "KAT2B", - "H2AJ", - "AGO4", - "H2BC11", - "AGO3", - "H2BC12", - "AGO1", - "MYL9", - "PF4", - "TNRC6A", - "TNRC6B", - "TNRC6C", - "ITGA2B", - "H2AB1", - "RBBP5", - "H3-3B", - "H3C7", - "NFE2", - "PRMT6", - "H2AC7", - "H2AC6", - "H2AC4", - "PRMT1", - "GP1BA", - "MOV10", - "NR4A3", - "ZFPM1", - "H2AC20", - "H4C15", - "PRKCQ", - "H2AX", - "KMT2E", - "HDAC1", - "KMT2B", - "CBFB", - "KMT2A", - "KMT2D", - "KMT2C", - "WDR5", - "DPY30", - "THBS1", - "GATA1", - "H2AC19", - "H2AC14", - "H2BC26", - "ASH2L", - "H2BC21", - "SIN3B", - "EP300", - "SIN3A", - "H3C13" - ], - "module2_reactome_2022_filtered__RUNX1 Regulates Transcription Of Genes Involved In Differentiation Of HSCs R-HSA-8939236": [ - "H2AZ2", - "LMO2", - "LMO1", - "H2AZ1", - "H2BC9", - "H2BC4", - "H2BC5", - "RUNX1", - "H2BC3", - "H2BC1", - "H2BC12L", - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "YAP1", - "CCNH", - "H2AB1", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "MYB", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "H2AC7", - "H2AC6", - "H2AC4", - "TP73", - "SEM1", - "H2AC20", - "H4C15", - "PSME4", - "PSME2", - "PSME3", - "PSME1", - "MNAT1", - "SPI1", - "CBFB", - "KMT2A", - "GATA3", - "GATA2", - "GATA1", - "H2AC19", - "H2AC14", - "UBC", - "UBB", - "PSMF1", - "ABL1", - "TCF12", - "H2BC17", - "H2BC13", - "H2BC14", - "H2BC15", - "CDK7", - "H2AJ", - "H2BC11", - "TAL1", - "H2BC12", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "RPS27A", - "H3-3B", - "H3C7", - "TCF7L1", - "ITCH", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "UBA52", - "H2AX", - "LDB2", - "PSMA8", - "PSMB7", - "PSMB8", - "H2BC26", - "PSMB5", - "PSMB6", - "PSMB3", - "H2BC21", - "PSMB4", - "PSMB1", - "H3C13", - "PSMB2" - ], - "module2_reactome_2022_filtered__Rab Regulation Of Trafficking R-HSA-9007101": [ - "ANKRD27", - "DENND4C", - "CCZ1B", - "DENND4B", - "TRAPPC11", - "TRAPPC10", - "SYTL1", - "TSC2", - "TSC1", - "GAPVD1", - "MADD", - "RAB31", - "RAB32", - "RAB38", - "RAB35", - "TBC1D20", - "TBC1D25", - "DENND4A", - "TBC1D24", - "ULK1", - "RIN2", - "RIN3", - "OPTN", - "RIN1", - "RAB1A", - "RAB1B", - "RGPD1", - "GGA1", - "GGA3", - "GGA2", - "RAB21", - "AKT3", - "MPZ", - "AKT1", - "TBC1D14", - "TRAPPC13", - "AKT2", - "TBC1D15", - "TRAPPC12", - "DENND5B", - "DENND5A", - "TBC1D13", - "TBC1D16", - "TBC1D17", - "TRAPPC2L", - "TRAPPC3", - "DENND2D", - "TRAPPC4", - "GABARAPL2", - "TRAPPC1", - "TRAPPC2", - "DENND2A", - "HPS1", - "TRAPPC8", - "DENND2C", - "RAB3IP", - "TRAPPC5", - "DENND2B", - "RAB39A", - "RAB27A", - "RAB39B", - "HPS4", - "RAB27B", - "TRAPPC9", - "POLGARF", - "MON1A", - "RAB5A", - "RAB5B", - "MON1B", - "GABARAP", - "RAB3A", - "RINL", - "RAB43", - "SBF2", - "RAB4A", - "SBF1", - "RAB3IL1", - "RAB33A", - "RAB33B", - "RABEP1", - "RAB7A", - "RAB7B", - "RABGAP1", - "DENND1C", - "RAB5C", - "YWHAE", - "DENND1B", - "DENND1A", - "MAP1LC3B", - "DENND3", - "RAB6B", - "RAB6A", - "RIC1", - "RAB11A", - "RAB12", - "TBC1D2", - "TRAPPC6B", - "RAB10", - "TRAPPC6A", - "RAB13", - "TBC1D7", - "ALS2", - "RAB14", - "DENND6A", - "RAB9A", - "RAB9B", - "DENND6B", - "RAB18", - "ARF6", - "RAB3GAP2", - "RAB3GAP1", - "GDI2", - "GDI1", - "TBC1D3I", - "ALS2CL", - "RABGEF1", - "RAB8A", - "CHML", - "TBC1D10A", - "RAB8B", - "TBC1D10B", - "TBC1D10C" - ], - "module2_reactome_2022_filtered__Recruitment And ATM-mediated Phosphorylation Of Repair And Signal Proteins At DNA Double Strand Breaks R-HSA-5693565": [ - "KDM4A", - "KDM4B", - "UBE2I", - "H2BC9", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "SMARCA5", - "H2BC13", - "H2BC14", - "PPP5C", - "H2BC15", - "ARTN", - "H2BC11", - "H2BC12", - "ATM", - "MRE11", - "TP53", - "UBE2N", - "UBXN1", - "ABRAXAS1", - "BRCA1", - "RNF8", - "BABAM2", - "BABAM1", - "HERC2", - "NSD2", - "UIMC1", - "CHEK2", - "APBB1", - "RPS27A", - "PIAS4", - "EYA1", - "EYA2", - "EYA3", - "EYA4", - "BAZ1B", - "RAD50", - "CNKSR3", - "H3-4", - "RNF168", - "H4C15", - "UBE2V2", - "UBA52", - "MDC1", - "H2AX", - "BRCC3", - "H2BC26", - "MAPK8", - "UBC", - "H2BC21", - "KAT5", - "SUMO1", - "UBB", - "TP53BP1", - "ABL1", - "BARD1" - ], - "module2_reactome_2022_filtered__Recruitment Of NuMA To Mitotic Centrosomes R-HSA-380320": [ - "HSP90AA1", - "OFD1", - "TUBB", - "PPP2R1A", - "TUBA4A", - "MZT2B", - "MZT2A", - "NME7", - "CENPJ", - "CCP110", - "PAFAH1B1", - "TUBGCP6", - "CDK1", - "CEP41", - "TUBGCP4", - "TUBGCP5", - "CEP43", - "SFI1", - "SDCCAG8", - "YWHAG", - "YWHAE", - "CETN2", - "SSNA1", - "CEP164", - "AKAP9", - "ACTR1A", - "CSNK1D", - "CNTRL", - "CEP250", - "CLASP1", - "CEP290", - "PCNT", - "DYNC1I2", - "CEP57", - "PLK4", - "CEP135", - "DYNLL1", - "ODF2", - "PLK1", - "NDE1", - "CEP131", - "HAUS5", - "HAUS4", - "CSNK1E", - "HAUS7", - "HAUS6", - "PRKCA", - "HAUS1", - "TUBG2", - "MZT1", - "TUBB4B", - "TUBG1", - "CKAP5", - "HAUS3", - "TUBB4A", - "HAUS2", - "NEDD1", - "CEP63", - "DYNC1H1", - "MAPRE1", - "DCTN3", - "TUBGCP2", - "DCTN2", - "TUBGCP3", - "NUMA1", - "ALMS1", - "DCTN1", - "TUBA1A", - "PCM1", - "HAUS8", - "CEP70", - "PRKAR2B", - "CDK5RAP2", - "CEP72", - "CEP152", - "NEK2", - "CEP76", - "NINL", - "CEP192", - "CEP78" - ], - "module2_reactome_2022_filtered__Regulated Necrosis R-HSA-5218859": [ - "GSDME", - "GSDMD", - "HSP90AA1", - "RIPK3", - "CFLAR", - "IL18", - "GNL3", - "IL1A", - "IL1B", - "IRF1", - "IRF2", - "BAX", - "CHMP4C", - "CHMP4B", - "CYCS", - "OGT", - "TP53", - "TRADD", - "HMGB1", - "TANK", - "CASP8", - "CASP4", - "CASP5", - "CASP3", - "FADD", - "CASP1", - "RIPK1", - "RPS27A", - "ELANE", - "RRM1", - "GZMB", - "TNFRSF10B", - "TNFRSF10A", - "PDCD6IP", - "SDCBP", - "RNF103-CHMP3", - "CDC37", - "FASN", - "XIAP", - "CHMP2B", - "CHMP2A", - "PELI1", - "STUB1", - "CHMP7", - "UBA52", - "BIRC2", - "BIRC3", - "CHMP6", - "FASLG", - "UBC", - "UBB", - "FLOT2", - "BAK1", - "FLOT1", - "MLKL", - "TNFSF10", - "TP63" - ], - "module2_reactome_2022_filtered__Regulation Of APC/C Activators Between G1/S And Early Anaphase R-HSA-176408": [ - "UBE2C", - "PSMB9", - "CDC20", - "PSMC6", - "PSMC4", - "UBE2S", - "PSMC5", - "BUB1B", - "CDC16", - "PSMC2", - "PSMC3", - "CDK2", - "CDK1", - "PSMC1", - "MAD2L1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "UBE2D1", - "PSMB11", - "PSMB10", - "PSMD9", - "CDC23", - "PSMD7", - "FZR1", - "PSMD8", - "PSMD5", - "CDC26", - "PSMD6", - "CDC27", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "RPS27A", - "PLK1", - "UBE2E1", - "PSMA6", - "CCNA2", - "PSMA7", - "CCNA1", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "ANAPC4", - "PSME2", - "ANAPC5", - "PSME3", - "ANAPC7", - "PSME1", - "UBA52", - "ANAPC1", - "ANAPC2", - "ANAPC15", - "ANAPC16", - "CUL1", - "ANAPC10", - "ANAPC11", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "CCNB1", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "FBXO5", - "PSMF1", - "BUB3", - "SKP1" - ], - "module2_reactome_2022_filtered__Regulation Of Apoptosis R-HSA-169911": [ - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "OMA1", - "PSMB11", - "PSMB10", - "PSMD9", - "MED12", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "PSMA6", - "PSMA7", - "ARHGAP10", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PKN2", - "PSME1", - "UBA52", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Regulation Of HSF1-mediated Heat Shock Response R-HSA-3371453": [ - "HSPA9", - "SEC13", - "HSPA6", - "HSPA1L", - "HSPA5", - "HSPA8", - "NDC1", - "NUP214", - "HSPA2", - "HSPA4", - "NUP133", - "NUP210", - "SIRT1", - "NUP50", - "NUP93", - "TNFRSF21", - "DNAJC2", - "NUP98", - "DNAJC7", - "NUP54", - "MAPKAPK2", - "HSPA1A", - "ATM", - "NUP58", - "FKBP4", - "HSPA1B", - "NUP205", - "GSK3B", - "SEH1L", - "SERPINA2", - "YWHAE", - "HIKESHI", - "NUP160", - "CCAR2", - "DNAJB1", - "HSF1", - "NUP42", - "DNAJB6", - "NUP43", - "NUP85", - "NUP88", - "RANBP2", - "GML", - "ST13", - "RNASE7", - "NUP153", - "RPA3", - "HSBP1", - "HSPA12A", - "NUP155", - "HSPA12B", - "POLR1A", - "POLR1B", - "NUP35", - "COL4A6", - "RLN1", - "NUP37", - "HSPA4L", - "POM121", - "NUP107", - "RPS19BP1", - "MRPL18", - "CRYBA4", - "NUP188", - "HSPA14", - "DEDD2", - "HSPA13", - "AAAS", - "HSPH1", - "BAG5", - "BAG3", - "TPR", - "BAG4", - "POM121C", - "UBB", - "BAG1", - "BAG2", - "NUP62", - "MAPK3", - "MAPK1", - "SERPINH1" - ], - "module2_reactome_2022_filtered__Regulation Of IGF Transport And Uptake By IGFBPs R-HSA-381426": [ - "CHGB", - "APLP2", - "SERPIND1", - "SHISA5", - "GOLM1", - "EVA1A", - "DMTF1", - "MELTF", - "SPARCL1", - "MXRA8", - "SCG2", - "SCG3", - "APP", - "IGFALS", - "CSF1", - "ITIH2", - "C4A", - "HSP90B1", - "CDH2", - "TGOLN2", - "GPC3", - "APOL1", - "FGF23", - "IGFBP2", - "IGFBP1", - "IGFBP6", - "TMEM132A", - "WFS1", - "IGFBP5", - "IGFBP4", - "IGFBP3", - "PAPPA", - "IGF1", - "IGF2", - "PDIA6", - "CKAP4", - "PROC", - "BPIFB2", - "SDC2", - "MFGE8", - "FBN1", - "MATN3", - "CHRDL1", - "LTBP1", - "TTF2", - "C3", - "LGALS1", - "APOE", - "IGFBP7", - "MEN1", - "APOB", - "MMP1", - "MMP2", - "FUCA2", - "MEPE", - "AMBN", - "FN1", - "APOA5", - "LAMB1", - "CP", - "KLK13", - "BMP4", - "DNAJC3", - "TF", - "VCAN", - "IL6", - "ENAM", - "RCN1", - "ALB", - "APOA1", - "FAM20C", - "VWA1", - "CALU", - "HRC", - "FAM20A", - "KLKB1", - "PAPPA2", - "GAS6", - "SERPINA1", - "KLK2", - 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"CEP135", - "DYNLL1", - "BORA", - "ODF2", - "PLK1", - "NDE1", - "CEP131", - "HAUS5", - "HAUS4", - "CSNK1E", - "HAUS7", - "HAUS6", - "PRKCA", - "HAUS1", - "TUBB4B", - "TUBG1", - "CKAP5", - "HAUS3", - "TUBB4A", - "HAUS2", - "NEDD1", - "CEP63", - "UBA52", - "DYNC1H1", - "MAPRE1", - "DCTN3", - "DCTN2", - "ALMS1", - "CUL1", - "DCTN1", - "TUBA1A", - "CCNB2", - "CCNB1", - "PCM1", - "UBC", - "HAUS8", - "UBB", - "CEP70", - "PRKAR2B", - "CDK5RAP2", - "CEP72", - "CEP152", - "NEK2", - "RAB8A", - "CEP76", - "NINL", - "CEP192", - "CEP78", - "SKP1" - ], - "module2_reactome_2022_filtered__Regulation Of PTEN Gene Transcription R-HSA-8943724": [ - "CBX8", - "CBX6", - "JUN", - "CBX4", - "RING1", - "CBX2", - "GATAD2A", - "GATAD2B", - "RRAGB", - "RRAGA", - "RRAGD", - "RRAGC", - "DPEP3", - "TP53", - "RNF2", - "KDM1A", - "ATN1", - "CHD4", - "CHD3", - "MTA1", - "MTA2", - "MECOM", - "RBBP4", - "RBBP7", - "MTA3", - "EGR1", - "PHC1", - "SUZ12", - "EED", - "PHC3", - "PHC2", - "MTOR", - "RHEB", - "GDNF", - "REST", - "COMMD3-BMI1", - "LAMTOR1", - "SNAI2", - "PPARG", - "LAMTOR3", - "LAMTOR2", - "SNAI1", - "LAMTOR5", - "LAMTOR4", - "EZH2", - "RCOR1", - "HDAC5", - "HDAC3", - "HDAC1", - "HDAC2", - "NR2E1", - "PTEN", - "HDAC7", - "RPTOR", - "SCMH1", - "SALL4", - "MLST8", - "MAPK3", - "MAPK1", - "SLC38A9", - "MAF1" - ], - "module2_reactome_2022_filtered__Regulation Of PTEN Stability And Activity R-HSA-8948751": [ - "PSMB9", - "RNF146", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "OTUD3", - "FRK", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "TNKS", - "PSMB11", - "PSMB10", - "PSMD9", - "PREX2", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "AKT3", - "PSMD3", - "PSMD4", - "AKT1", - "AKT2", - "PSMD2", - "RPS27A", - "CSNK2A2", - "WWP2", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "CSNK2B", - "MKRN1", - "PSME4", - "XIAP", - "NEDD4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "STUB1", - "UBA52", - "USP13", - "PTEN", - "TNKS2", - "PSMA8", - 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"FBXW7", - "SMURF1", - "PSMB9", - "RBX1", - "RUNX2", - "PSMC6", - "BMP2", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "DLX5", - "DLX6", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "MSX2", - "PPARGC1A", - "PPARGC1B", - "RPS27A", - "ESRRA", - "STAT1", - "WWP1", - "ESR1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "STUB1", - "UBA52", - "CBFB", - "CUL1", - "TWIST1", - "NR3C1", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "HIVEP3", - "SKP1", - "NKX3-2", - "SKP2" - ], - "module2_reactome_2022_filtered__Regulation Of RUNX3 Expression And Activity R-HSA-8941858": [ - "TGFB1", - "SMURF2", - "SMURF1", - "PSMB9", - "RUNX3", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "CDKN2A", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "MDM2", - "SRC", - "CBFB", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "EP300", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Regulation Of TP53 Activity R-HSA-5633007": [ - "SUPT16H", - "USP7", - "BRPF3", - "GATAD2A", - "HUS1", - "PPP2R1A", - "HIPK2", - "POU4F1", - "PRKAB1", - "HIPK1", - "POU4F2", - "PRKAB2", - "GATAD2B", - "MAPK14", - "PML", - "MAPK11", - "MAPKAPK5", - "KAT6A", - "DPEP3", - "DNA2", - "MRE11", - "RHNO1", - "TP53", - "PRKAA2", - "RNF34", - "TP53RK", - "AURKA", - "PRDM1", - "PRKAA1", - "TAF1L", - "AURKB", - "MTA2", - "CHEK1", - "AKT3", - "CHEK2", - "RBBP4", - "AKT1", - "KMT5A", - "PIP4K2A", - "USP2", - "AKT2", - "PIP4K2B", - "PIP4K2C", - "RBBP7", - "RBBP8", - "RFFL", - "TAF9B", - "DAXX", - "PLK3", - "TOP3A", - "NUAK1", - "TP73", - "CCNA2", - "CCNA1", - "TPX2", - "CSNK2B", - "RAD17", - "MDM4", - "RAD9A", - "MDM2", - "RAD9B", - "BLM", - "DYRK2", - "MEAF6", - "BRIP1", - "STK11", - "PDZD2", - "UBC", - "KAT5", - "UBB", - "MLST8", - "TP53BP2", - "BANP", - "MAP2K6", - "BARD1", - "TP63", - "ZNF385A", - "RFC4", - "RFC5", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "PPP2R5C", - "ARTN", - "JMY", - "CDK5", - "CCNG1", - "CDK2", - "ATM", - "CDK1", - "SGK1", - "L3MBTL1", - "PHF20", - "SERPINA2", - "RRAD", - "MAPKAP1", - "BRCA1", - "CHD4", - "CHD3", - "TOPBP1", - "SMYD2", - "RPS27A", - "ATRIP", - "TBP", - "TAF15", - "BRD1", - "CDKN2A", - "CSNK2A2", - "TAF13", - "PDPK1", - "PRMT5", - "TAF11", - "TAF12", - "TP53INP1", - "RPA3", - "TAF10", - "SSRP1", - "MTOR", - "RAD50", - "PPP2CA", - "TAF1", - "POLR1A", - "POLR1B", - "TTC5", - "TAF9", - "TAF7", - "TAF6", - "CDK5R1", - "TAF5", - "UBA52", - "TAF4", - "TAF3", - "TAF2", - "PRR5", - "PPP1R13B", - "HDAC1", - "SETD9", - "HDAC2", - "EHMT1", - "PRKAG2", - "EHMT2", - "PRKAG3", - "NOC2L", - "PRKAG1", - "ING5", - "PIP4P1", - "PPP2CB", - "ING3", - "TAF7L", - "WRN", - "EXO1", - "PPP1R13L", - "PPP2R1B", - "EP300", - "RICTOR", - "BRD7" - ], - "module2_reactome_2022_filtered__Regulation Of TP53 Activity Thru Phosphorylation R-HSA-6804756": [ - "RFC4", - "RFC5", - "SUPT16H", - "RFC2", - "RMI1", - "RFC3", - "RMI2", - "HUS1", - "HIPK2", - "PRKAB1", - "HIPK1", - "PRKAB2", - "MAPK14", - "MAPK11", - "ARTN", - "CDK5", - "MAPKAPK5", - "CDK2", - "ATM", - "DNA2", - "MRE11", - "RHNO1", - "TP53", - "PRKAA2", - "SERPINA2", - "RRAD", - "TP53RK", - "AURKA", - "BRCA1", - "PRKAA1", - "TAF1L", - "AURKB", - "CHEK1", - "CHEK2", - "TOPBP1", - "RBBP8", - "RPS27A", - "ATRIP", - "TAF9B", - "TBP", - "TAF15", - "CSNK2A2", - "TAF13", - "PLK3", - "TAF11", - "TOP3A", - "TAF12", - "TP53INP1", - "RPA3", - "TAF10", - "SSRP1", - "NUAK1", - "RAD50", - "TAF1", - "CCNA2", - "CCNA1", - "TPX2", - "POLR1A", - "POLR1B", - "CSNK2B", - "RAD17", - "TAF9", - "MDM4", - "TAF7", - "TAF6", - "CDK5R1", - "TAF5", - "UBA52", - "TAF4", - "RAD9A", - "TAF3", - "MDM2", - "RAD9B", - "TAF2", - "BLM", - "DYRK2", - "PRKAG2", - "PRKAG3", - "NOC2L", - "PRKAG1", - "BRIP1", - "STK11", - "TAF7L", - "WRN", - "EXO1", - "PDZD2", - "UBC", - "KAT5", - "UBB", - "BARD1" - ], - "module2_reactome_2022_filtered__Regulation Of Actin Dynamics For Phagocytic Cup Formation R-HSA-2029482": [ - "ACTR3", - "ACTR2", - "HSP90AA1", - "LIMK1", - "BAIAP2", - "VAV1", - "VAV2", - "VAV3", - "MYH2", - "FCGR2A", - "ABI1", - "ABI2", - "ELMO1", - "ELMO2", - "MYH9", - "NF2", - "CD247", - "DOCK1", - "ARPC1A", - "CYFIP2", - "CYFIP1", - "ARPC1B", - "BRK1", - "ARPC4-TTLL3", - "TARP", - "NCKIPSD", - "POTEF", - "WASF1", - "WASF2", - "WASF3", - "NCKAP1L", - "SYK", - "MYO10", - "LOC124905743", - "MYO5A", - "ARPC5", - "MYO9B", - "RNASE1", - "CDC42", - "PTK2", - "IGLL5", - "MYO1E", - "ARPC3", - "ARPC2", - "PKN1", - "BTK", - "CRK", - "GRB2", - "HSP90AB1", - "WIPF1", - "WIPF2", - "NCKAP1", - "WIPF3", - "ACTG1", - "WAS", - "WASL", - "VPS72", - "FCGR1A", - "MAPK3", - "MAPK1", - "ABL1", - "NCK1" - ], - "module2_reactome_2022_filtered__Regulation Of Cholesterol Biosynthesis By SREBP (SREBF) R-HSA-1655829": [ - "NCOA2", - "SEC24A", - "SEC24C", - "NCOA1", - "SEC24B", - "NCOA6", - "HMGCS1", - "LMBR1", - "SCD", - "TBL1XR1", - "CARM1", - "DHCR7", - "DAND5", - "SEC24D", - "RAN", - "IDI1", - "LIN9", - "SAR1B", - "CHD9", - "ACACB", - "ACACA", - "RXRA", - "PMVK", - "SC5D", - "SCAP", - "TBL1X", - "FDFT1", - "FDPS", - "MBD4", - "MBTPS1", - "SREBF1", - "CREBBP", - "MBTPS2", - "SREBF2", - "PPARA", - "CYP51A1", - "NFYB", - "ELOVL6", - "NFYC", - "GGPS1", - "SQLE", - "KPNB1", - "FASN", - "GPAM", - "MVD", - "MVK", - "SMARCD3", - "SEC23A", - "INSIG2", - "INSIG1", - "HMGCR", - "PKMYT1", - "TM7SF2", - "HELZ2", - "NFYA" - ], - "module2_reactome_2022_filtered__Regulation Of Expression Of SLITs And ROBOs R-HSA-9010553": [ - "RPL22", - "RPS8", - "RPL21", - "RPS9", - "RPS6", - "RPL23", - "RPS7", - "UPF3B", - "UPF3A", - "ISL1", - "RPSA", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "FAU", - "RPL26", - "PSMC1", - "RPL28", - "RPL22L1", - "RPL27", - "RPL29", - "RPL26L1", - "RPL11", - "RPL12", - "CASC3", - "RPS4Y2", - "RPS4Y1", - "PSMB11", - "PSMB10", - "MAGOH", - "PSMD9", - "PSMD7", - "RPL36AL", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "RPL15", - "SLIT2", - "DAG1", - "PSMD2", - "RPL14", - "RPS5", - "HOXA2", - "RPS2", - "RPS3", - "SLIT3", - "RPL19", - "RPL18", - "RPL41", - "RPL39L", - "RPL3L", - "RPS27", - "RPS29", - "RPS28", - "SEM1", - "LHX4", - "LHX3", - "RPL27A", - "LHX2", - "PSME4", - "EIF4G1", - "PSME2", - "PSME3", - "RNPS1", - "RPS21", - "PSME1", - "RPS20", - "RPS23", - "RPS25", - "RSL24D1", - "LHX9", - "RPS24", - "RPL5", - "RPL6", - "RPL3", - "RPL31", - "RPL30", - "RPL4", - "RPL32", - "RPL35", - "RPL34", - "CUL2", - "MSI1", - "MRPL13", - "RPL9", - "GSPT2", - "RPL7", - "GSPT1", - 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"MND1", - "RPA3", - "MLH3", - "RAD50", - "H3-4", - "RAD51", - "POLR1A", - "POLR1B", - "ZP2", - "ZP4", - "ZP3", - "H2AX", - "SYCP3", - "SYCP2", - "SPO11", - "SYCP1", - "SYNE1", - "ADAM21", - "ADAM20", - "SYNE2", - "H2BC26", - "H2BC21", - "H3C13" - ], - "module2_reactome_2022_filtered__Resolution Of Sister Chromatid Cohesion R-HSA-2500257": [ - "KNL1", - "PPP2R1A", - "CDC20", - "DYNC1LI1", - "SGO2", - "DYNC1LI2", - "ZW10", - "SGO1", - "NUP98", - "BUB1B", - "B9D2", - "MAD2L1", - "ZWILCH", - "PMF1-BGLAP", - "NUP160", - "AURKB", - "PPP1CC", - "NUP85", - "CLASP1", - "KNTC1", - "CLASP2", - "PLK1", - "WAPL", - "CKAP5", - "NDC80", - "RPS27", - "KIF18A", - "STAG2", - "KIF2B", - "KIF2A", - "BIRC5", - "KIF2C", - "MAD1L1", - "CDCA5", - "CDCA8", - "PDS5B", - "PDS5A", - "SMC3", - "CCNB2", - "CCNB1", - "PMEPA1", - "NDEL1", - "RCC2", - "CENPU", - "NUDC", - "SEC13", - "PPP2R5C", - "PPP2R5B", - "PPP2R5E", - "NUP133", - "PPP2R5D", - "SMC1A", - "ZWINT", - "PPP2R5A", - "CENPE", - "CENPF", - "CENPH", - "CENPI", - 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"NDUFB7", - "CCNI", - "NDUFB6", - "NDUFB5", - "NDUFB10", - "NDUFB4", - "NDUFB3", - "UQCR10", - "ETFDH", - "NDUFB2", - "NDUFB1", - "UQCR11", - "COX6A1", - "UQCRFS1", - "COX4I1", - "NDUFV3", - "NDUFV2", - "NDUFV1", - "COX8A", - "NDUFA9", - "NDUFA8", - "TIMMDC1", - "NDUFA7", - "SURF1", - "NDUFA6", - "NUBPL", - "NDUFA5", - "NDUFA3", - "NDUFA2", - "NDUFA1", - "COX7A2L", - "COQ10B", - "COX6B1", - "COQ10A", - "NDUFAF7", - "NDUFAB1", - "NDUFAF5", - "NDUFAF6", - "NDUFAF3", - "NDUFAF4", - "NDUFAF1", - "NDUFAF2", - "COX5A", - "NDUFA12", - "COX16", - "NDUFA13", - "UQCRB", - "COX19", - "NDUFA10", - "COX18", - "NDUFA11", - "ETFA", - "TACO1", - "ETFB", - "COX5B", - "TMEM186", - "UQCRH", - "COX14" - ], - "module2_reactome_2022_filtered__Respiratory Electron Transport, ATP Synthesis By Chemiosmotic Coupling, Heat Production By Uncoupling Proteins R-HSA-163200": [ - "TRAP1", - "SDHD", - "SDHB", - "SDHA", - "SCO2", - "UQCRQ", - "SCO1", - "TMEM126B", - "UQCRC2", - "UQCRC1", - "COX20", - "PM20D1", - "NDUFB11", - 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"IGF2", - "PRKCA", - "SCCPDH", - "SERPING1", - "TAGLN2", - "FERMT3", - "VCL", - "SPARC", - "TEX264", - "MAGED2", - "LGALS3BP", - "PDGFB", - "A1BG", - "PDGFA", - "ISLR", - "VPS72", - "APOH", - "CYB5R1", - "ABCC4", - "SRGN", - "HSPA5", - "ANXA5", - "VEGFC", - "FN1", - "VEGFD", - "LHFPL2", - "VEGFA", - "VEGFB", - "TF", - "MMRN1", - "PIKFYVE", - "ALB", - "APOA1", - "CALU", - "PECAM1", - "PCYOX1L", - "GAS6", - "HRG", - "SOS1", - "SERPINA3", - "SERPINA4", - "SERPINA1", - "ENDOD1", - "CTSW", - "KNG1", - "TIMP3", - "QSOX1", - "PHACTR2", - "TIMP1", - "CD36", - "FGA", - "CAP1", - "CHID1", - "FGB", - "LEFTY2", - "AHSG", - "FGG", - "VWF", - "F5", - "SGPP2", - "SOD1", - "F8", - "CD109", - "SFTPA2", - "ECM1", - "CD63", - "ORM2", - "ORM1", - "WDR1", - "PLEK", - "F13A1", - "ITGB3", - "TOR4A", - "OLA1", - "THBS1", - "PLG", - "TTR", - "TMSB4X", - "FAM3C", - "CDC37L1", - "PCDH7" - ], - "module2_reactome_2022_filtered__Ribosomal Scanning And Start Codon Recognition R-HSA-72702": [ - "RPS8", - "RPS9", - "EIF1AX", - "RPS6", - "RPS7", - "RPS3A", - "RPSA", - "FAU", - "EIF4B", - "RPS27L", - "RPS4Y2", - "RPS4Y1", - "RPS5", - "RPS2", - "EIF4H", - "RPS3", - "EIF4E", - "RPS15A", - "RPS27A", - "EIF3A", - "EIF2S3", - "EIF2S2", - "EIF2S1", - "RPS27", - "EIF5", - "RPS29", - "RPS28", - "EIF3L", - "EIF3M", - "EIF3J", - "EIF3K", - "EIF3H", - "EIF4G1", - "EIF3I", - "EIF3F", - "RPS21", - "EIF3G", - "RPS20", - "EIF3D", - "RPS23", - "EIF3E", - "EIF3B", - "RPS25", - "EIF3C", - "RPS24", - "EIF4A2", - "EIF4A1", - "RPS16", - "RPS15", - "RPS4X", - "RPS17", - "RPS19", - "RPS10", - "RPS12", - "RPS11", - "RPS14", - "RPS13" - ], - "module2_reactome_2022_filtered__S Phase R-HSA-69242": [ - "GINS2", - "GINS3", - "GINS1", - "ESCO2", - "UBE2C", - "ESCO1", - "GINS4", - "CDC25A", - "CDC25B", - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "UBE2S", - "POLE2", - "PSMC5", - "CDC16", - "POLE3", - "PSMC2", - "POLE4", - "PSMC3", - "PSMC1", - "DNA2", - "PCNA", - "RB1", - "LIN9", - "CCNH", - "UBE2D1", - "PSMB11", - "PSMB10", - "PSMD9", - "CDC23", - "PSMD7", - "FZR1", - "PSMD8", - "PSMD5", - "CCND1", - "CDC26", - "PSMD6", - "AKT3", - "CDC27", - "PSMD3", - "E2F1", - "MYC", - "PSMD4", - "RBBP4", - "AKT1", - "AKT2", - "PSMD2", - "E2F4", - "E2F5", - "PTK6", - "WAPL", - "CCNA2", - "CCNA1", - "STAG2", - "TFDP1", - "SEM1", - "TFDP2", - "PSME4", - "PSME2", - "PSME3", - "PSME1", - "MNAT1", - "ANAPC15", - "MAX", - "ANAPC16", - "GMNN", - "CDCA5", - "CUL1", - "PDS5B", - "ANAPC10", - "PDS5A", - "ANAPC11", - "SMC3", - "UBC", - "UBB", - "PMEPA1", - "PSMF1", - "SLC19A1", - "SKP1", - "SKP2", - "CDT1", - "RFC4", - "RFC5", - "RFC2", - "RFC3", - "CDC6", - "SMC1A", - "POLA1", - "POLA2", - "CDK7", - "RBL2", - "CDK4", - "CDK2", - "MCM4", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "CKS1B", - "ORC6", - "ORC5", - "RAD21", - "ORC4", - "ORC3", - "LIN52", - "RPS27A", - "LIN54", - "CABLES1", - "RPA3", - "UBE2E1", - "PSMA6", - "PSMA7", - "WEE1", - "PSMA4", - "PSMA5", - "POLR1A", - "PSMA2", - "CCNE2", - "POLR1B", - "PSMA3", - "CCNE1", - "PSMA1", - "ANAPC4", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "SLC25A15", - "FEN1", - "CDKN1B", - "MCM8", - "CDKN1A", - "LIN37", - "PRIM2", - "PRIM1", - "PSMA8", - "POLD4", - "PSMB7", - "PSMB8", - "CDC45", - "POLE", - "PSMB5", - "PSMB6", - "PSMB3", - "POLD1", - "PSMB4", - "POLD2", - "PSMB1", - "POLD3", - "PSMB2", - "LRIG1" - ], - "module2_reactome_2022_filtered__SARS-CoV Infections R-HSA-9679506": [ - "IL1R1", - "CNBP", - "NUP214", - "GATAD2A", - "MOGS", - "NUP210", - "ATG14", - "GATAD2B", - "NUP93", - "NUP98", - "NUP205", - "RB1", - "KEAP1", - "NOD1", - "NOD2", - "RPS4Y2", - "RPS4Y1", - "MTA1", - "MTA2", - "SNRPD3", - "SNRPD2", - "AKT3", - "NUP85", - "AKT1", - "AKT2", - "AP2S1", - "VPS11", - "NUP88", - "B2M", - "MTA3", - "ST3GAL1", - "VPS16", - "ST3GAL2", - "ST3GAL3", - "VPS18", - "ST3GAL4", - "IL17RA", - "SIGMAR1", - "CREBBP", - "ST6GAL1", - "EDEM2", - "TOMM70", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "PARP16", - "HLA-E", - "IL17RC", - "SRPK1", - "HLA-F", - "PARP14", - "SRPK2", - "ACE2", - "RPS27", - "HSPG2", - "RPS29", - "PARP10", - "RPS28", - "SDC1", - "BTK", - "SDC2", - "RPS21", - "GRB2", - "RPS20", - "RPS23", - "RPS25", - "RCOR1", - "RPS24", - "POM121", - "PATJ", - "AAAS", - "LARP1", - "RPS16", - "CD79B", - "RPS15", - "CD79A", - "GJA1", - "RPS17", - "TBK1", - "POM121C", - "RPS19", - "NUP62", - "RPS10", - "PLCG2", - "GOLGA7", - "RPS12", - "RPS11", - "RPS14", - "RPS13", - "FXYD6-FXYD2", - "CSNK1A1", - "SEC13", - "PTGES3", - "SFTPD", - "NDC1", - "TUBB", - "TYK2", - "VAV1", - "VEGFA", - "FXYD1", - "TLR2", - "TLR1", - "SIKE1", - "FXYD4", - "FXYD3", - "FXYD2", - "TLR9", - "FXYD7", - "TLR8", - "TLR7", - "PALS1", - "MOGAT2", - "RPN1", - "SDC3", - "CHURC1-FNTB", - "MOGAT1", - "RPN2", - "SDC4", - "TMPRSS2", - "RPS27L", - "HOXC13", - "MAP1LC3B", - "MAGT1", - "CTSL", - "TRIM4", - "SUDS3", - "RIPK2", - "RIPK1", - "RPS15A", - "HAVCR1", - "RPS27A", - "BRD4", - "PARP6", - "RANBP2", - "PARP4", - "IFNGR1", - "STAT1", - "IFNGR2", - "STAT2", - "SYK", - "AP2B1", - "RNASE7", - "PRMT1", - "ZDHHC11", - "STING1", - "PARP8", - "PARP9", - "IGLL5", - "FKBP1A", - "TJP1", - "NLRP12", - "CANX", - "UBE2V1", - "IL17F", - "CRB3", - "IFNA6", - "NRP1", - "IFNA5", - "IFNA8", - "IFNA7", - "IFNA2", - "ZDHHC20", - "IFNA1", - "IFNA4", - "AP2A2", - "ZDHHC8", - "AP2A1", - "NUP188", - "ZDHHC9", - "ZDHHC2", - "ZDHHC3", - "ZDHHC5", - "IFIH1", - "IKBKB", - "RNF135", - "MGAT5", - "IFNB1", - "IKBKG", - "JAK3", - "IKBKE", - "JAK1", - "JAK2", - "SEC24A", - "TUFM", - "UVRAG", - "ZBP1", - "SEC24C", - "UBE2I", - "SEC24B", - "RPS8", - "CRBN", - "RPS9", - "RPS6", - "RPS7", - "ATP1B3", - "ATP1B2", - "ATP1B1", - "RPSA", - "RBX1", - "SAP30", - "DPEP3", - "FAU", - "CHMP4C", - "ISCU", - "CHMP4B", - "SNRPF", - "SNRPG", - "PDCD1", - "FKBP4", - "PTPN6", - "SEC24D", - "SNRPE", - "SNRPB", - "AGRN", - "UBE2N", - "KDM1A", - "ROCK1", - "MASP1", - "ROCK2", - "GEMIN2", - "TUSC3", - "GEMIN4", - "ARID4A", - "ARID4B", - "ATP1A4", - "ATP1A3", - "ATP1A2", - "COMT", - "NUP160", - "CYSLTR1", - "GPC2", - "GPC1", - "RBBP4", - "GPC4", - "NLRP3", - "SAP18", - "GPC3", - "RPS5", - "S1PR1", - "GPC6", - "RBBP7", - "RPS2", - "GPC5", - "RPS3", - "MAP3K7", - "KPNA2", - "IFNA14", - "IFNA16", - "ZCRB1", - "IFNA17", - "NUP153", - "TRAF3", - "ISG15", - "NUP155", - "DDOST", - "RNF103-CHMP3", - "MAVS", - "TRAF6", - "CHMP2B", - "GEMIN5", - "CHMP2A", - "GEMIN6", - "STT3A", - "GEMIN7", - "TAB3", - "GEMIN8", - "TAB2", - "CHMP7", - "ST6GALNAC4", - "TAB1", - "IFNA10", - "IFNAR1", - "RIGI", - "CHMP6", - "IFNAR2", - "DDX5", - "VCP", - "SH3KBP1", - "CUL3", - "PHF21A", - "TPR", - "UBC", - "FNTA", - "UBB", - "G3BP1", - "G3BP2", - "MAN2A1", - "IL6R", - "IFNA21", - "AP2M1", - "HSP90AA1", - "REXO2", - "ITGA4", - "CAV1", - "GALNT1", - "PTPN11", - "NUP133", - "RPS3A", - "NUP50", - "NUP54", - "BRMS1L", - "IRF3", - "NUP58", - "IRF7", - "PIK3C3", - "NFE2L2", - "SOS1", - "YWHAG", - "GSK3B", - "SEH1L", - "ST6GALNAC2", - "GSK3A", - "ST6GALNAC3", - "YWHAE", - "SAR1B", - "YWHAB", - "PIK3R4", - "CHD4", - "CHD3", - "FUT8", - "IRAK2", - "NUP42", - "TKFC", - "NUP43", - "IRAK1", - "YWHAQ", - "BLNK", - "SMN1", - "YWHAH", - "CHUK", - "PDPK1", - "VPS33B", - "VPS33A", - "YWHAZ", - "VPS41", - "IMPDH2", - "MGAT4A", - "REST", - "DAD1", - "MGAT4B", - "MGAT4C", - "MAN1B1", - "NUP35", - "VPS45", - "MASP2", - "UBA52", - "NUP37", - "BECN1", - "ITGB1", - "NUP107", - "SEC23A", - "HDAC1", - "HSP90AB1", - "PRKCSH", - "FURIN", - "HDAC2", - "DDX20", - "NR3C1", - "RPS4X", - "GANAB", - "SUMO1", - "SAP30L", - "TRIM25", - "VHL", - "HMG20B", - "MBL2", - "NCK1", - "VPS39" - ], - "module2_reactome_2022_filtered__SARS-CoV-1 Infection R-HSA-9678108": [ - "UVRAG", - "UBE2I", - "CAV1", - "GALNT1", - "MOGS", - "CHMP4C", - "CHMP4B", - "PIK3C3", - "PALS1", - "RB1", - "GSK3B", - "ST6GALNAC2", - "GSK3A", - "ST6GALNAC3", - "MOGAT1", - "TMPRSS2", - "PIK3R4", - "HOXC13", - "MAP1LC3B", - "CTSL", - "ST3GAL1", - "RPS27A", - "ST3GAL2", - "ST3GAL3", - "ST3GAL4", - "PARP6", - "PARP4", - "ZCRB1", - "ST6GAL1", - "PARP16", - "PARP8", - "PARP14", - "PARP9", - "ACE2", - "RNF103-CHMP3", - "PARP10", - "CANX", - "CHMP2B", - "CHMP2A", - "CHMP7", - "ST6GALNAC4", - "UBA52", - "CHMP6", - "BECN1", - "DDX5", - "VCP", - "PRKCSH", - "GANAB", - "UBC", - "SUMO1", - "UBB", - "VHL" - ], - "module2_reactome_2022_filtered__SARS-CoV-2 Infection R-HSA-9694516": [ - "CNBP", - "NUP214", - "MOGS", - "NUP210", - "ATG14", - "NUP93", - "NUP98", - "NUP205", - "RB1", - "NOD1", - "NOD2", - "RPS4Y2", - "RPS4Y1", - "SNRPD3", - "SNRPD2", - "AKT3", - "NUP85", - "AKT1", - "AKT2", - "VPS11", - "NUP88", - "B2M", - "ST3GAL1", - "VPS16", - "ST3GAL2", - "ST3GAL3", - "VPS18", - "ST3GAL4", - "IL17RA", - "CREBBP", - "ST6GAL1", - "EDEM2", - "TOMM70", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "PARP16", - "HLA-E", - "IL17RC", - "SRPK1", - "HLA-F", - "PARP14", - "SRPK2", - "ACE2", - "RPS27", - "HSPG2", - "RPS29", - "PARP10", - "RPS28", - "SDC1", - "SDC2", - "RPS21", - "RPS20", - "RPS23", - "RPS25", - "RPS24", - "POM121", - "PATJ", - "AAAS", - "LARP1", - "RPS16", - "RPS15", - "GJA1", - "RPS17", - "TBK1", - "POM121C", - "RPS19", - "NUP62", - "RPS10", - "GOLGA7", - "RPS12", - "RPS11", - "RPS14", - "RPS13", - "CSNK1A1", - "SEC13", - "SFTPD", - "NDC1", - "TYK2", - "TLR2", - "TLR1", - "SIKE1", - "TLR8", - "TLR7", - "PALS1", - "MOGAT2", - "RPN1", - "SDC3", - "MOGAT1", - "RPN2", - "SDC4", - "TMPRSS2", - "RPS27L", - "HOXC13", - "MAP1LC3B", - "MAGT1", - "CTSL", - "TRIM4", - "RIPK2", - "RPS15A", - "HAVCR1", - "RPS27A", - "PARP6", - "RANBP2", - "PARP4", - "STAT1", - "STAT2", - "RNASE7", - "PRMT1", - "ZDHHC11", - "STING1", - "PARP8", - "PARP9", - "TJP1", - "NLRP12", - "CANX", - "UBE2V1", - "IL17F", - "CRB3", - "IFNA6", - "NRP1", - "IFNA5", - "IFNA8", - "IFNA7", - "IFNA2", - "ZDHHC20", - "IFNA1", - "IFNA4", - "ZDHHC8", - "NUP188", - "ZDHHC9", - "ZDHHC2", - "ZDHHC3", - "ZDHHC5", - "IFIH1", - "IKBKB", - "RNF135", - "MGAT5", - "IFNB1", - "IKBKG", - "IKBKE", - "JAK1", - "SEC24A", - "TUFM", - "UVRAG", - "SEC24C", - "UBE2I", - "SEC24B", - "RPS8", - "RPS9", - "RPS6", - "RPS7", - "RPSA", - "FAU", - "CHMP4C", - "ISCU", - "CHMP4B", - "SNRPF", - "SNRPG", - "PTPN6", - "SEC24D", - "SNRPE", - "SNRPB", - "AGRN", - "UBE2N", - "MASP1", - "GEMIN2", - "TUSC3", - "GEMIN4", - "NUP160", - "GPC2", - "GPC1", - "GPC4", - "NLRP3", - "GPC3", - "RPS5", - "GPC6", - "RPS2", - "GPC5", - "RPS3", - "MAP3K7", - "KPNA2", - "IFNA14", - "IFNA16", - "ZCRB1", - "IFNA17", - "NUP153", - "TRAF3", - "ISG15", - "NUP155", - "DDOST", - "RNF103-CHMP3", - "MAVS", - "TRAF6", - "CHMP2B", - "GEMIN5", - "CHMP2A", - "GEMIN6", - "STT3A", - "GEMIN7", - "TAB3", - "GEMIN8", - "TAB2", - "CHMP7", - "ST6GALNAC4", - "TAB1", - "IFNA10", - "IFNAR1", - "RIGI", - "CHMP6", - "IFNAR2", - "DDX5", - "VCP", - "TPR", - "UBC", - "UBB", - "G3BP1", - "G3BP2", - "MAN2A1", - "IFNA21", - "HSP90AA1", - "REXO2", - "CAV1", - "GALNT1", - "PTPN11", - "NUP133", - "RPS3A", - "NUP50", - "NUP54", - "IRF3", - "NUP58", - "IRF7", - "PIK3C3", - "YWHAG", - "GSK3B", - "SEH1L", - "ST6GALNAC2", - "GSK3A", - "ST6GALNAC3", - 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"NUP155", - "HLA-G", - "HLA-E", - "IL17RC", - "HLA-F", - "MAVS", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "IFNA10", - "IFNAR1", - "RIGI", - "IFNAR2", - "POM121", - "AAAS", - "LARP1", - "TBK1", - "TPR", - "POM121C", - "G3BP1", - "G3BP2", - "NUP62", - "IFNA21", - "HSP90AA1", - "SEC13", - "SFTPD", - "NDC1", - "PTPN11", - "NUP133", - "TYK2", - "TLR2", - "NUP50", - "TLR1", - "SIKE1", - "NUP54", - "IRF3", - "NUP58", - "TLR8", - "IRF7", - "PIK3C3", - "TLR7", - "SEH1L", - "SAR1B", - "PIK3R4", - "HOXC13", - "IRAK2", - "NUP42", - "TKFC", - "NUP43", - "IRAK1", - "TRIM4", - "RIPK2", - "RANBP2", - "STAT1", - "CHUK", - "STAT2", - "RNASE7", - "STING1", - "NLRP12", - "NUP35", - "UBE2V1", - "IL17F", - "MASP2", - "NUP37", - "BECN1", - "IFNA6", - "IFNA5", - "IFNA8", - "NUP107", - "IFNA7", - "SEC23A", - "HSP90AB1", - "IFNA2", - "IFNA1", - "IFNA4", - "NUP188", - "IFIH1", - "IKBKB", - "RNF135", - "TRIM25", - "IFNB1", - "IKBKG", - "IKBKE", - "JAK1", - "MBL2" - ], - "module2_reactome_2022_filtered__SARS-CoV-2-host Interactions R-HSA-9705683": [ - "CNBP", - "NUP214", - "NUP210", - "ATG14", - "NUP93", - "NUP98", - "NUP205", - "NOD1", - "NOD2", - "RPS4Y2", - "RPS4Y1", - "SNRPD3", - "SNRPD2", - "AKT3", - "NUP85", - "AKT1", - "AKT2", - "VPS11", - "NUP88", - "B2M", - "VPS16", - "VPS18", - "IL17RA", - "CREBBP", - "TOMM70", - "HLA-C", - "HLA-A", - "HLA-B", - "HLA-G", - "HLA-E", - "IL17RC", - "HLA-F", - "RPS27", - "RPS29", - "RPS28", - "RPS21", - "RPS20", - "RPS23", - "RPS25", - "RPS24", - "POM121", - "PATJ", - "AAAS", - "LARP1", - "RPS16", - "RPS15", - "GJA1", - "RPS17", - "TBK1", - "POM121C", - "RPS19", - "NUP62", - "RPS10", - "RPS12", - "RPS11", - "RPS14", - "RPS13", - "SEC13", - "SFTPD", - "NDC1", - "TYK2", - "TLR2", - "TLR1", - "SIKE1", - "TLR8", - "TLR7", - "PALS1", - "RPS27L", - "HOXC13", - "MAP1LC3B", - "TRIM4", - "RIPK2", - "RPS15A", - "RPS27A", - "RANBP2", - "STAT1", - "STAT2", - "RNASE7", - "STING1", - "TJP1", - "NLRP12", - "UBE2V1", - "IL17F", - "CRB3", - "IFNA6", - "IFNA5", - "IFNA8", - "IFNA7", - "IFNA2", - "IFNA1", - "IFNA4", - 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"VPS41", - "NUP35", - "VPS45", - "MASP2", - "NUP37", - "BECN1", - "NUP107", - "SEC23A", - "HSP90AB1", - "DDX20", - "RPS4X", - "TRIM25", - "MBL2", - "VPS39" - ], - "module2_reactome_2022_filtered__SCF(Skp2)-mediated Degradation Of P27/P21 R-HSA-187577": [ - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "CDK4", - "PSMC2", - "PSMC3", - "CDK2", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "CKS1B", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "CCND1", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "PTK6", - "PSMA6", - "CCNA2", - "PSMA7", - "CCNA1", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "CCNE2", - "PSMA3", - "CCNE1", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "CDKN1B", - "CDKN1A", - "CUL1", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1", - "SKP1", - "SKP2" - ], - "module2_reactome_2022_filtered__SCF-beta-TrCP Mediated Degradation Of Emi1 R-HSA-174113": [ - 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"PDE6A" - ], - "module2_reactome_2022_filtered__Signaling By B Cell Receptor (BCR) R-HSA-983705": [ - "FBXW11", - "PSMB9", - "PPIA", - "PSMC6", - "STIM1", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "ORAI2", - "PSMC1", - "ORAI1", - "PTPN6", - "CALM1", - "AHCYL1", - "RELA", - "PSMB11", - "PSMB10", - "PSMD9", - "NRAS", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "MAP3K7", - "BTRC", - "CARD11", - "PRKCB", - "NFKB1", - "SEM1", - "PIK3R1", - "PSME4", - "PSME2", - "BTK", - "PSME3", - "GRB2", - "PSME1", - "BLK", - "CLSTN1", - "SH3KBP1", - "BCL10", - "CUL1", - "RASGRP3", - "DAPP1", - "CD79B", - "PPP3R1", - "CD79A", - "RASGRP1", - "UBC", - "UBB", - "CD19", - "PSMF1", - "PLCG2", - "SKP1", - "LYN", - "NFATC3", - "NFATC2", - "NFATC1", - "VAV1", - "NFKBIB", - "NFKBIA", - "REL", - "NFKBIE", - "CD22", - "SOS1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "ITPR2", - "ITPR3", - "ITPR1", - "PPP3CA", - "PPP3CB", - "BLNK", - "TRPC1", - "RPS27A", - "CHUK", - "SYK", - "IGLL5", - "PSMA6", - "FKBP1A", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "UBA52", - "PIK3AP1", - "PSMA8", - "MALT1", - "PSMB7", - "IKBKB", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "HRAS", - "FYN", - "IKBKG", - "NCK1" - ], - "module2_reactome_2022_filtered__Sphingolipid Metabolism R-HSA-428157": [ - "CTSA", - "B3GALNT1", - "EGFL7", - "SPHK2", - "SPHK1", - "SUMF1", - "SUMF2", - "SPNS2", - "DEGS2", - "GLB1L", - "DEGS1", - "ASAH2", - "SAMD8", - "SGMS2", - "CERK", - "SGMS1", - "ASAH1", - "NEU3", - "STS", - "NEU4", - "UGCG", - "UGT8", - "SGPL1", - "KDSR", - "ORMDL3", - "NEU2", - "SPTSSA", - "B4GALNT1", - "SPTSSB", - "ARSA", - "CERS4", - "ARSB", - "CERS6", - "ORMDL1", - "ORMDL2", - "SGPP2", - "SGPP1", - "ACER3", - "FA2H", - "ACER2", - "ALDH3A2", - "GALC", - "ACER1", - "GLB1", - "NAT8", - "VAPA", - "ESYT1", - "VAPB", - "CPTP", - "PRKD1", - "ESYT3", - "PRKD3", - "ESYT2", - "PRKD2", - "SFTPA2", - "CERS1", - "PLPP3", - "CERS2", - 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"CDC6", - "POLA1", - "POLA2", - "CDK2", - "MCM4", - "MCM5", - "MCM6", - "MCM7", - "MCM3", - "PSMD11", - "SLC25A2", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "ORC6", - "ORC5", - "ORC4", - "ORC3", - "RPS27A", - "RPA3", - "UBE2E1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "POLR1A", - "PSMA2", - "CCNE2", - "POLR1B", - "PSMA3", - "CCNE1", - "PSMA1", - "ANAPC4", - "ANAPC5", - "ANAPC7", - "UBA52", - "ANAPC1", - "ANAPC2", - "SLC25A15", - "FEN1", - "MCM8", - "PRIM2", - "PRIM1", - "PSMA8", - "POLD4", - "PSMB7", - "PSMB8", - "CDC45", - "POLE", - "PSMB5", - "PSMB6", - "PSMB3", - "POLD1", - "PSMB4", - "POLD2", - "PSMB1", - "POLD3", - "PSMB2", - "LRIG1" - ], - "module2_reactome_2022_filtered__Synthesis Of PIPs At Plasma Membrane R-HSA-1660499": [ - "PLEKHA2", - "PLEKHA3", - "PLEKHA1", - "PLEKHA6", - "OCRL", - "PLEKHA4", - "PLEKHA5", - "PTPN13", - "BMX", - "PLEKHA8", - "RUFY1", - "PIK3CB", - "PIK3CA", - "PI4K2B", - "PI4K2A", - "MTMR1", - "MTMR2", - "MTMR3", - "MTMR9", - "PIK3R5", - "PIK3R3", - "INPPL1", - "PIK3R2", - "MTMR6", - "MTM1", - "PIK3R6", - "PIP4K2A", - "PIP4K2B", - "PIP4K2C", - "INPP4B", - "INPP4A", - "PIK3R1", - "SYNJ2", - "SYNJ1", - "RAB14", - "RAB5A", - "CLSTN1", - "MTMR14", - "PTEN", - "PIK3C2G", - "PIK3C2B", - "PIK3C2A", - "PIK3CG", - "INPP5D", - "INPP5K", - "PIP5K1A", - "PIP5K1B", - "PIP5K1C", - "INPP5J", - "SBF2", - "RAB4A" - ], - "module2_reactome_2022_filtered__Synthesis Of Substrates In N-glycan Biosythesis R-HSA-446219": [ - "CTSA", - "EGFL7", - "GFPT1", - "SRD5A3", - "SLC35A1", - "GFPT2", - "DOLPP1", - "RENBP", - "DHDDS", - "DPM1", - "DPM2", - "DPM3", - "SLPI", - "ST6GALNAC1", - "ST6GALNAC2", - "ST6GALNAC3", - "CMAS", - "UAP1", - "FUOM", - "NEU3", - "NEU4", - "GMDS", - "GMPPA", - "GMPPB", - "NEU2", - "GNPNAT1", - "ST3GAL5", - "ST8SIA5", - "ST3GAL6", - "ST8SIA6", - "ST3GAL1", - "ST3GAL2", - "ST3GAL3", - "ST3GAL4", - "NUS1", - "ST6GAL2", - "PMM2", - "SLC35C1", - "ST8SIA1", - "PMM1", - "ST6GAL1", - "ST8SIA2", - "ALG5", - "ST8SIA3", - "ST8SIA4", - "DOLK", - "GLB1", - "NAGK", - "FCSK", - "MVD", - "GNE", - "ST6GALNAC4", - "ST6GALNAC5", - "ST6GALNAC6", - "NPL", - "NANP", - "NANS", - "AMDHD2", - "FPGT", - "PGM3", - "GFUS", - "NUDT14", - "SLC17A5" - ], - "module2_reactome_2022_filtered__TCF Dependent Signaling In Response To WNT R-HSA-201681": [ - "H2AZ2", - "H2AZ1", - "PPP2R1A", - "H2BC12L", - "SOX13", - "DKK1", - "DKK2", - "PSMB9", - "RUNX3", - "SMARCA4", - "DKK4", - "PSMC6", - "SFRP2", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "LEF1", - "TRRAP", - "HECW1", - "LRP6", - "LRP5", - "SFRP1", - "PSMB11", - "AMER1", - "PSMB10", - "PSMD9", - "FRAT1", - "FRAT2", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "MYC", - "PSMD4", - "AKT1", - "AKT2", - "PSMD2", - "BTRC", - "FZD2", - "CREBBP", - "FZD1", - "FZD4", - "FZD6", - "FZD8", - "CSNK1E", - "CCDC88C", - "H2AC20", - "CSNK2B", - "PSME4", - "PSME2", - "PSME3", - "PSME1", - "CTBP1", - "H2AC19", - "SOX3", - "SOX4", - "H2AC14", - "SOX2", - "KAT5", - "SOX17", - "PIP5K1B", - "PSMF1", - "SOX9", - "SOX7", - "SOX6", - "CSNK1A1", - "WNT9A", - "RNF146", - "H2AJ", - "WNT8B", - "WNT8A", - "TERT", - "RPS27A", - "H3-3B", - "H3C7", - "CSNK2A2", - "PPP2CA", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "PSMA2", - "PSMA3", - "XIAP", - "PSMA1", - "LGR6", - "LGR5", - "LGR4", - "H2AX", - "CTNNB1", - "PSMA8", - "PPP2CB", - "PSMB7", - "XPO1", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "ASH2L", - "PSMB4", - "EP300", - "PSMB1", - "H3C13", - "PSMB2", - "RSPO3", - "RSPO4", - "RSPO1", - "RSPO2", - "RNF43", - "TLE4", - "RYK", - "TLE3", - "USP8", - "H2BC9", - "TLE2", - "TLE1", - "CTNNBIP1", - "H2BC4", - "WNT5A", - "H2BC5", - "H2BC3", - "H2BC1", - "RBX1", - "H2AB1", - "PYGO1", - "RUVBL1", - "RBBP5", - "WIF1", - "LEO1", - "H2AC7", - "WNT3A", - "H2AC6", - "KREMEN2", - "H2AC4", - "KREMEN1", - "PROC", - "PYGO2", - "SEM1", - "H4C15", - "TCF7", - "KMT2D", - "CUL3", - "KLHL12", - "UBC", - "UBB", - "BCL9L", - "MEN1", - "SMURF2", - "CAV1", - "AXIN2", - "PPP2R5C", - "AXIN1", - "PPP2R5B", - "PPP2R5E", - "PPP2R5D", - "H2BC17", - "PPP2R5A", - "H2BC13", - "H2BC14", - "BCL9", - "H2BC15", - "H2BC11", - "H2BC12", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "TNKS", - "USP34", - "CHD8", - "CBY1", - "CDC73", - "DVL2", - "DVL3", - "WNT1", - "WNT3", - "DACT1", - "WNT4", - "TCF7L2", - "TCF7L1", - "YWHAZ", - "H3-4", - "UBA52", - "CSNK1G2", - "HDAC1", - "CXXC4", - "TNKS2", - "H2BC26", - "ZNRF3", - "H2BC21", - "PPP2R1B", - "SRY", - "SOST" - ], - "module2_reactome_2022_filtered__TCR Signaling R-HSA-202403": [ - "FBXW11", - "PSMB9", - "PSMC6", - "CD4", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "SPNS1", - "UBE2N", - "RELA", - "UBE2D1", - "UBE2D2", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "GRAP2", - "PSMD2", - "MAP3K7", - "BTRC", - "FYB1", - "CARD11", - "HLA-DRB5", - "NFKB1", - "VASP", - "SEM1", - "PIK3R1", - "PSME4", - "TRAF6", - "PSME2", - "PSME3", - "PRKCQ", - "PSME1", - "TAB2", - "HLA-DRB4", - "HLA-DRB3", - "HLA-DRB1", - "BCL10", - "CUL1", - "INPP5D", - "UBC", - "UBB", - "PLCG1", - "PSMF1", - "PLCG2", - "SKP1", - "ENAH", - "NFKBIA", - "PTPRC", - "PIK3CB", - "PIK3CA", - "HLA-DRA", - "LCP2", - "CD247", - "ITK", - "ARHGEF7", - "HLA-DQB2", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PTPN22", - "PIK3R2", - "CD3E", - "PTPRJ", - "TARP", - "CD3D", - "RIPK2", - "RPS27A", - "HLA-DQA2", - "HLA-DQA1", - "PAG1", - "CHUK", - "PDPK1", - "PSMA6", - "PSMA7", - "CDC34", - "LCK", - "PSMA4", - "ZAP70", - "PSMA5", - "PSMA2", - "PSMA3", - "PSMA1", - "PKN1", - "UBE2V1", - "EVL", - "PKN2", - "UBA52", - "CD101", - "PTEN", - "WAS", - "PSMA8", - "MALT1", - "PSMB7", - "IKBKB", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2", - "HLA-DPA1", - "TRAT1", - "CSK", - "IKBKG", - "NCK1" - ], - "module2_reactome_2022_filtered__TNFR2 Non-Canonical NF-kB Pathway R-HSA-5668541": [ - "FBXW11", - "CD70", - "PSMB9", - "EDAR", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "TNFSF4", - "PSMC1", - "TNFSF9", - "TNFSF8", - "UBE2M", - "TNF", - "PSMB11", - "RELB", - "TANK", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "BTRC", - "EDARADD", - "TNFSF18", - "TNFSF15", - "TNFSF13", - "TNFSF14", - "TRAF3", - "NFKB2", - "SEM1", - "PSME4", - "PSME2", - "PSME3", - "UBA3", - "PSME1", - "MAP3K14", - "BIRC2", - "BIRC3", - "TNFRSF6B", - "CUL1", - "FASLG", - "EDA2R", - "UBC", - "UBB", - "TNFRSF8", - "TNFSF11", - "TNFRSF9", - "TNFSF12", - "PSMF1", - "TNFRSF4", - "SKP1", - "TNFRSF12A", - "TNFRSF1A", - "TNFRSF1B", - "CD27", - "TNFRSF25", - "EDA", - "PSMD11", - "PSMD10", - "PSMD13", - "CD40", - "PSMD12", - "TNFRSF11A", - "PSMD14", - "TNFRSF11B", - "TNFRSF17", - "TNFRSF18", - "RPS27A", - "TNFRSF14", - "CHUK", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "LTBR", - "PSMA2", - "PSMA3", - "PSMA1", - "LTB", - "LTA", - "CD40LG", - "UBA52", - "TNFRSF13C", - "TNFRSF13B", - "PSMA8", - "TNFSF13B", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "PSMB4", - "PSMB1", - "PSMB2" - ], - "module2_reactome_2022_filtered__TP53 Regulates Metabolic Genes R-HSA-5628897": [ - "GPX2", - "REXO2", - "TIGAR", - "GSR", - "TXNRD1", - "UBL4A", - "TSC2", - "TSC1", - "PRKAB1", - "COX6C", - "PRKAB2", - "LRPPRC", - "GNPDA1", - "RRAGB", - "RRAGA", - "AGO4", - "RRAGD", - "SCO2", - "RRAGC", - "RRM2B", - "AGO2", - "AGO3", - "SCO1", - "AGO1", - "CYCS", - "COX20", - "TNRC6A", - "TNRC6B", - "TP53", - "TNRC6C", - "YWHAG", - "COX7C", - "GLS2", - "PRKAA2", - "COX7B", - "YWHAE", - "YWHAB", - "PRKAA1", - "COX6A1", - "AKT3", - "YWHAQ", - "AKT1", - "AKT2", - "COX4I1", - "YWHAH", - "COX8A", - "SURF1", - "YWHAZ", - "COX7A2L", - "MTOR", - "COX6B1", - "MOV10", - "RHEB", - "DDIT4", - "LAMTOR1", - "LAMTOR3", - "LAMTOR2", - "LAMTOR5", - "LAMTOR4", - "TXN2", - "COX5A", - "COX16", - "COX19", - "COX18", - "PRKAG2", - "PRKAG3", - "PTEN", - "TACO1", - "PRKAG1", - "COX5B", - "RPTOR", - "SESN1", - "PRDX5", - "PRDX2", - "SESN2", - "MLST8", - "PRDX1", - "SESN3", - "COX14", - "SLC38A9", - "TP63" - ], - "module2_reactome_2022_filtered__TP53 Regulates Transcription Of DNA Repair Genes R-HSA-6796648": [ - "JUN", - "SUPT16H", - "GTF2F1", - "MSH2", - "GTF2F2", - "CDK9", - "CDK7", - "RAD51D", - "FANCD2", - "ATM", - "CDK12", - "CDK13", - "TP53", - "CCNK", - "SERPINA2", - "CCNT2", - "CCNT1", - "ELL", - "CCNH", - "SUPT4H1", - "BRCA1", - "CHEK1", - "NELFCD", - "PMS2", - "FANCI", - "MLH1", - "ELOA2", - "FOS", - "GTF2H1", - "GTF2H2", - "SSRP1", - "FANCC", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "SUPT5H", - "DDB2", - "GDNF", - "ERCC2", - "ERCC3", - "TCEA1", - "MNAT1", - "MDC1", - "CTDP1", - "NELFA", - "POLR2A", - "POLR2B", - "POLR2C", - "POLR2D", - "POLR2E", - "ELOA", - "NELFE", - "POLR2F", - "ELOB", - "POLR2G", - "ELOC", - "POLR2H", - "NELFB", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__TRAF6 Mediated Induction Of NFkB And MAP Kinases Upon TLR7/8 Or 9 Activation R-HSA-975138": [ - "MAP3K1", - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "CUL1", - "AGER", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "IRAK4", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "NFKBIA", - "ECSIT", - "TNIP2", - "TLR9", - "TLR7", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "RIPK2", - "RPS27A", - "MEF2A", - "MEF2C", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7" - ], - "module2_reactome_2022_filtered__Telomere Maintenance R-HSA-157579": [ - "H2AZ2", - "ACD", - "CTC1", - "RFC4", - "ANKRD28", - "H2AZ1", - "H2BC9", - "RFC5", - "RFC2", - "RFC3", - "H2BC4", - "H2BC5", - "H2BC3", - "H2BC1", - "H2BC12L", - "H2BC17", - "H2BC13", - "POLA1", - "WRAP53", - "H2BC14", - "POLA2", - "H2BC15", - "SHQ1", - "H2AJ", - "DKC1", - "H2BC11", - "H2BC12", - "CDK2", - "DNA2", - "POT1", - "TERF2IP", - "NHP2", - "PCNA", - "PIF1", - "H2AB1", - "RTEL1", - "PPP6C", - "RUVBL1", - "TERT", - "PPP6R3", - "RUVBL2", - "GAR1", - "H3-3B", - "DAXX", - "H2AC7", - "STN1", - "ATRX", - "TINF2", - "H2AC6", - "H2AC4", - "RPA3", - "TERF1", - "TERF2", - "H3-4", - "CCNA2", - "CCNA1", - "POLR1A", - "H2AC20", - "H4C15", - "POLR1B", - "NOP10", - "FEN1", - "H2AX", - "BLM", - "DSCC1", - "PRIM2", - "PRIM1", - "CHTF18", - "H2AC19", - "POLD4", - "CHTF8", - "H2AC14", - "H2BC26", - "POLR2A", - "WRN", - "POLR2B", - "H2BC21", - "POLD1", - "POLR2C", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "POLR2F", - "LRIG1", - "POLR2G", - "POLR2H", - "POLR2I", - "POLR2J", - "POLR2K", - "SLC19A1", - "POLR2L" - ], - "module2_reactome_2022_filtered__Citric Acid (TCA) Cycle And Respiratory Electron Transport R-HSA-1428517": [ - "SLC16A1", - "PDHX", - "TRAP1", - "ADHFE1", - "SDHD", - "SDHB", - "SDHA", - "GSTZ1", - "SCO2", - "UQCRQ", - "SCO1", - "TMEM126B", - "UQCRC2", - "UQCRC1", - "SUCLG2", - "SUCLG1", - "COX20", - "PM20D1", - "NDUFB11", - "CCNI", - "NDUFB10", - "DLST", - "PDHB", - "UCP2", - "UQCR10", - "UCP1", - "UQCR11", - "COX6A1", - "PDPR", - "RXRA", - "BSG", - "UCP3", - "SLC16A8", - "NDUFV3", - "SLC16A3", - "NDUFV2", - "LDLR", - "NDUFV1", - "COX8A", - "DMAC2L", - "SURF1", - "IDH2", - "COX7A2L", - "COQ10B", - "COQ10A", - "PLPP6", - "NDUFAF7", - "NDUFAF5", - "NDUFAF6", - "NDUFAF3", - "NDUFAF4", - "NDUFAF1", - "NDUFAF2", - "LDHAL6A", - "LDHAL6B", - "OGDH", - "COX16", - "UQCRB", - "COX19", - "COX18", - "ATP5MC3", - "TACO1", - "TMEM186", - "ATP5MC1", - "ATP5MC2", - "UQCRH", - "MPC1", - "MPC2", - "PDK4", - "COX14", - "PDK3", - "PDK2", - "ACAD9", - "PDHA2", - "COA1", - "PCDHGA5", - "PDHA1", - "NDUFC2", - "NDUFC1", - "ATP5F1D", - "ATP5F1E", - "COX6C", - "CS", - "LRPPRC", - "ECSIT", - "NDUFS8", - "NDUFS7", - "NDUFS6", - "NDUFS5", - "HAGH", - "NDUFS4", - "NDUFS3", - "ACO2", - "CYCS", - "NDUFS2", - "NDUFS1", - "ATP5ME", - "COX7C", - "NDUFB9", - "COX7B", - "NDUFB8", - "NDUFB7", - "NDUFB6", - "NDUFB5", - "GLO1", - "NDUFB4", - "NDUFB3", - "ETFDH", - "NDUFB2", - "NDUFB1", - "LDHD", - "LDHC", - "LDHB", - "NNT", - "LDHA", - "UQCRFS1", - "COX4I1", - "D2HGDH", - "ATP5MG", - "ATP5MF", - "FAHD1", - "NDUFA9", - "ATP5PF", - "NDUFA8", - "TIMMDC1", - "NDUFA7", - "ATP5PD", - "IDH3G", - "MDH2", - "NDUFA6", - "NUBPL", - "NDUFA5", - "ATP5PB", - "PDPK1", - "NDUFA3", - "NDUFA2", - "PCDHB16", - "NDUFA1", - "PCDHB11", - "COX6B1", - "PDP2", - "SUCLA2", - "NDUFAB1", - "ATP5PO", - "L2HGDH", - "VDAC1", - "PPARD", - "SLC25A14", - "COX5A", - "NDUFA12", - "NDUFA13", - "NDUFA10", - "NDUFA11", - "ETFA", - "ETFB", - "COX5B", - "ATP5F1A", - "SLC25A27", - "ATP5F1B", - "ATP5F1C", - "DLAT", - "IDH3A", - "IDH3B" - ], - "module2_reactome_2022_filtered__Role Of GTSE1 In G2/M Progression After G2 Checkpoint R-HSA-8852276": [ - "HSP90AA1", - "FKBPL", - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "CDK1", - "PSMC1", - "TP53", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "GTSE1", - "PLK1", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "MAPRE1", - "HSP90AB1", - "CDKN1A", - "PSMA8", - "PSMB7", - "PSMB8", - "CCNB2", - "PSMB5", - "CCNB1", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Toll Like Receptor 3 (TLR3) Cascade R-HSA-168164": [ - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "UBE2D3", - "TIFA", - "UBE2D1", - "NOD1", - "UBE2D2", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "TRAF3", - "NFKB1", - "NFKB2", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "BIRC2", - "BIRC3", - "CUL1", - "AGER", - "TBK1", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "RIPK3", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "NFKBIA", - "TNIP2", - "IRF3", - "IRF7", - "TLR3", - "HMGB1", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "FADD", - "RIPK2", - "RIPK1", - "RPS27A", - "MEF2A", - "MEF2C", - "CHUK", - "KCNH8", - "FOS", - "S100B", - "PPP2CA", - "SARM1", - "SAA2", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "IKBKE" - ], - "module2_reactome_2022_filtered__Toll Like Receptor 4 (TLR4) Cascade R-HSA-166016": [ - "MAP3K1", - "FBXW11", - "S100A1", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "S100A9", - "TP53", - "PTPN4", - "S100A8", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "UBE2D3", - "TIFA", - "UBE2D1", - "NOD1", - "UBE2D2", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "LY86", - "TIRAP", - "TRAF3", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "BTK", - "TAB3", - "TAB2", - "TAB1", - "BIRC2", - "BIRC3", - "CUL1", - "AGER", - "TBK1", - "UBC", - "UBB", - "PLCG2", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "RIPK3", - "PTPN11", - "IRAK4", - "PPP2R5D", - "DNM1", - "DNM2", - "DUSP7", - "DNM3", - "NFKBIB", - "TLR2", - "NFKBIA", - "TLR1", - "ECSIT", - "TNIP2", - "IRF3", - "IRF7", - "TLR6", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "SOCS1", - "IRAK2", - "NKIRAS1", - "IRAK3", - "NKIRAS2", - "IRAK1", - "LBP", - "FADD", - "RIPK2", - "RIPK1", - "CD36", - "RPS27A", - "FGA", - "FGB", - "MEF2A", - "MEF2C", - "FGG", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "CD180", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "SARM1", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "BPI", - "UBA52", - "SIGIRR", - "ITGB2", - "USP14", - "LRRC14", - "PELI3", - "ITGAM", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "IKBKE" - ], - "module2_reactome_2022_filtered__Toll Like Receptor 7/8 (TLR7/8) Cascade R-HSA-168181": [ - "MAP3K1", - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "CUL1", - "AGER", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "IRAK4", - "PPP2R5D", - "DUSP7", - "NFKBIB", - "NFKBIA", - "ECSIT", - "TNIP2", - "TLR9", - "TLR8", - "IRF7", - "TLR7", - "TLR4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "RIPK2", - "RPS27A", - "MEF2A", - "MEF2C", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7" - ], - "module2_reactome_2022_filtered__Toll Like Receptor 9 (TLR9) Cascade R-HSA-168138": [ - "MAP3K1", - "FBXW11", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "TP53", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "TIFA", - "NOD1", - "NOD2", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "TAB3", - "TAB2", - "TAB1", - "CUL1", - "AGER", - "UBC", - "UBB", - "MAP2K6", - "SKP1", - "MAP2K7", - "DUSP6", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "IRAK4", - "PPP2R5D", - "RBSN", - "DUSP7", - "NFKBIB", - "NFKBIA", - "ECSIT", - "TNIP2", - "TLR9", - "IRF7", - "PIK3C3", - "TLR7", - "TLR4", - "PIK3R4", - "HMGB1", - "LY96", - "NLRC5", - "HOXC13", - "CASP8", - "IRAK2", - "NKIRAS1", - "NKIRAS2", - "IRAK1", - "RIPK2", - "RPS27A", - "MEF2A", - "MEF2C", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "FOS", - "NDUFA2", - "S100B", - "PPP2CA", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "UBA52", - "USP14", - "LRRC14", - "PELI3", - "USP18", - "EEA1", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7" - ], - "module2_reactome_2022_filtered__Toll-like Receptor Cascades R-HSA-168898": [ - "MAP3K1", - "FBXW11", - "S100A1", - "PPP2R1A", - "VRK3", - "TICAM1", - "MAPK14", - "MAPK11", - "MAPK10", - "MAPKAPK2", - "MAPKAPK3", - "ALPK1", - "S100A9", - "TP53", - "PTPN4", - "S100A8", - "UBE2N", - "APP", - "RELA", - "NLRX1", - "UBE2D3", - "TIFA", - "UBE2D1", - "NOD1", - "UBE2D2", - "NOD2", - "HSP90B1", - "TANK", - "MAP3K7", - "BTRC", - "MAP3K8", - "S100A12", - "N4BP1", - "MAP2K4", - "MAP2K3", - "MAP2K1", - "LY86", - "TIRAP", - "TRAF3", - "NFKB1", - "NFKB2", - "MYD88", - "CREB1", - "GDNF", - "TRAF6", - "LGMN", - "BTK", - "TAB3", - "TAB2", - "TLR10", - "TAB1", - "BIRC2", - "BIRC3", - "CUL1", - "AGER", - "TBK1", - "UBC", - "UBB", - "PLCG2", - "MAP2K6", - "SKP1", - "APOB", - "MAP2K7", - "CTSB", - "GSDME", - "DUSP6", - "GSDMD", - "IKBIP", - "DUSP3", - "DUSP4", - "JUN", - "RIPK3", - "PTPN11", - "IRAK4", - "PPP2R5D", - "RBSN", - "DNM1", - "DNM2", - "DUSP7", - "DNM3", - "NFKBIB", - "TLR2", - "NFKBIA", - "TLR1", - "ECSIT", - "TNIP2", - "IRF3", - "TLR9", - "TLR8", - "IRF7", - "PIK3C3", - "TLR7", - "TLR6", - "TLR5", - "TLR4", - "TLR3", - "PIK3R4", - "HMGB1", - "LY96", - "NLRC5", - "CTSV", - "HOXC13", - "CTSS", - "CASP8", - "SOCS1", - "IRAK2", - "NKIRAS1", - "IRAK3", - "NKIRAS2", - "CTSL", - "CTSK", - "IRAK1", - "LBP", - "FADD", - "RIPK2", - "RIPK1", - "CD36", - "RPS27A", - "FGA", - "FGB", - "MEF2A", - "MEF2C", - "FGG", - "TMED7-TICAM2", - "CHUK", - "KCNH8", - "CD180", - "FOS", - "NDUFA2", - "CNPY3", - "S100B", - "PPP2CA", - "SARM1", - "PELI2", - "SAA2", - "PELI1", - "UBE2V1", - "BPI", - "UBA52", - "SIGIRR", - "ITGB2", - "USP14", - "LRRC14", - "UNC93B1", - "PELI3", - "ITGAM", - "USP18", - "EEA1", - "PPP2CB", - "RPS6KA5", - "IKBKB", - "MAPK9", - "MAPK8", - "RPS6KA1", - "PPP2R1B", - "RPS6KA3", - "RPS6KA2", - "MAPK3", - "MAPK1", - "IKBKG", - "MAPK7", - "IKBKE" - ], - "module2_reactome_2022_filtered__Transcription Of HIV Genome R-HSA-167172": [ - "SUPT16H", - "NCBP2", - "NCBP1", - "GTF2F1", - "GTF2F2", - "CDK9", - "CDK7", - "GTF2A1", - "GTF2A2", - "CCNK", - "CCNT2", - "RNMT", - "CCNT1", - "ELL", - "CCNH", - "GTF2B", - "SUPT4H1", - "GTF2E1", - "GTF2E2", - "TAF1L", - "NELFCD", - "TAF9B", - "TBP", - "RNGTT", - "TAF15", - "TAF13", - "TAF11", - "TAF12", - "ELOA2", - "GTF2H1", - "TAF10", - "GTF2H2", - "SSRP1", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "SUPT5H", - "TAF1", - "ERCC2", - "ERCC3", - "TAF9", - "TAF7", - "TCEA1", - "TAF6", - "TAF5", - "MNAT1", - "TAF4", - "TAF3", - "TAF2", - "CTDP1", - "NELFA", - "POLR2A", - "TAF7L", - "POLR2B", - "POLR2C", - "POLR2D", - "POLR2E", - "ELOA", - "NELFE", - "POLR2F", - "ELOB", - "POLR2G", - "ELOC", - "POLR2H", - "NELFB", - "POLR2I", - "POLR2J", - "POLR2K", - "POLR2L" - ], - "module2_reactome_2022_filtered__Transcription-Coupled Nucleotide Excision Repair (TC-NER) R-HSA-6781827": [ - "ISY1-RAB43", - "RFC4", - "RFC5", - "USP7", - "RFC2", - "RFC3", - "RBX1", - "COPS5", - "CUL4B", - "COPS4", - "CUL4A", - "CDK7", - "COPS6", - "PRPF19", - "POLE2", - "COPS3", - "POLE3", - "COPS2", - "POLE4", - "COPS8", - "ZNF830", - "PPIE", - "PCNA", - "ELL", - "CCNH", - "GPS1", - "RPS27A", - "HMGN1", - "AQR", - "XRCC1", - "GTF2H1", - "RPA3", - "GTF2H2", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "XAB2", - "DDB1", - "ERCC1", - "ERCC4", - "POLR1A", - "ERCC5", - "POLR1B", - "ERCC2", - "ERCC3", - "ERCC8", - "ERCC6", - "TCEA1", - "UBA52", - "MNAT1", - "UVSSA", - "XPA", - "COPS7A", - "POLD4", - "POLE", - "POLR2A", - "POLR2B", - "UBC", - "POLD1", - "POLR2C", - "UBB", - "EP300", - "POLD2", - "POLR2D", - "POLD3", - "POLR2E", - "POLK", - "POLR2F", - "LRIG1", - "POLR2G", - "POLR2H", - "LRIG3", - "POLR2I", - "POLR2J", - "POLR2K", - "SLC19A1", - "POLR2L" - ], - "module2_reactome_2022_filtered__Transcriptional Regulation By MECP2 R-HSA-8986944": [ - "PTPN1", - "GAMT", - "RBFOX1", - "OPRM1", - "HIPK2", - "NCOR1", - "SST", - "TBL1XR1", - "NCOR2", - "AGO4", - "AGO2", - "AGO3", - "GPRIN1", - "AGO1", - "CAMK4", - "FKBP5", - "TNRC6A", - "TNRC6B", - "CALM1", - "PTPN4", - "SGK1", - "TNRC6C", - "RNMT", - "GPS2", - "FOXG1", - "MOBP", - "DLL1", - "AURKB", - "MECP2", - "IRAK1", - "DGCR8", - "LBR", - "TBL1X", - "MEF2C", - "GAD2", - "GAD1", - "TRPC3", - "OPRK1", - "PRKCA", - "MOV10", - "CREB1", - "CRH", - "PPARG", - "PVALB", - "GRIA2", - "CAMK2D", - "HDAC3", - "NOTCH1", - "HDAC1", - "HDAC2", - "CAMK2A", - "CAMK2B", - "PTEN", - "SLC2A3", - "SLC6A4", - "GRIN2B", - "GRIN2A", - "SOX2", - "SIN3A", - "CAMK2G" - ], - "module2_reactome_2022_filtered__Transcriptional Regulation By TP53 R-HSA-3700989": [ - "PRELID1", - "CRADD", - "GATAD2A", - "HUS1", - "PPP2R1A", - "PRKAB1", - "PRKAB2", - "GATAD2B", - "MAPK14", - "PML", - "MAPK11", - "RRAGB", - "RRAGA", - "RRAGD", - "SCO2", - "RRAGC", - "SCO1", - "BAX", - "CDK12", - "DNA2", - "CDK13", - "RHNO1", - "TP53", - "GLS2", - "PRKAA2", - "CCNK", - "TP53RK", - "CCNH", - "PRKAA1", - "MTA2", - "CHEK1", - "AKT3", - "CHEK2", - "E2F1", - "AKT1", - "PIP4K2A", - "AKT2", - "PIP4K2B", - "E2F4", - "PIP4K2C", - "E2F7", - "E2F8", - "RFFL", - "TAF9B", - "CNOT6L", - "COX8A", - "CREBBP", - "DAXX", - "SURF1", - "GADD45A", - "PLK3", - "TOP3A", - "PLK2", - "COX7A2L", - "NUAK1", - "MOV10", - "TP73", - "TPX2", - "FASN", - "CSNK2B", - "RAD17", - "MDC1", - "MEAF6", - "BRIP1", - "TP53AIP1", - "PRDX5", - "PCBP4", - "KAT5", - "PRDX2", - "MLST8", - "PRDX1", - "TP53BP2", - "BANP", - "BARD1", - "TP63", - "JUN", - "TIGAR", - "RMI1", - "RMI2", - "UBL4A", - "COX6C", - "LRPPRC", - "RAD51D", - "AGO4", - "ARTN", - "CENPJ", - "AGO2", - "AGO3", - "CCNG1", - "AGO1", - "ATM", - "SGK1", - "COX7C", - "PHF20", - "BTG2", - "COX7B", - "SERPINA2", - "RRAD", - "ELL", - "SUPT4H1", - "BRCA1", - "BBC3", - "CASP10", - "NELFCD", - "SMYD2", - "PRELID3A", - "RPS27A", - "NPM1", - "BRD1", - "MLH1", - "CDKN2A", - "CSNK2A2", - "PRMT5", - "FOS", - "GTF2H1", - "RPA3", - "GTF2H2", - "PRMT1", - "GTF2H3", - "GTF2H4", - "GTF2H5", - "COX6B1", - "PPP2CA", - "DDB2", - "CCNE2", - "CCNE1", - "TTC5", - "LAMTOR1", - "LAMTOR3", - "TCEA1", - "LAMTOR2", - "CDK5R1", - "LAMTOR5", - "LAMTOR4", - "CDKN1B", - "COX5A", - "CTDP1", - "SETD9", - "CDKN1A", - "PRKAG2", - "PRKAG3", - "NOC2L", - "PRKAG1", - "COX5B", - "ING5", - "PIP4P1", - "PPP2CB", - "RPTOR", - "ING3", - "TAF7L", - "WRN", - "AIFM2", - "EP300", - "PLAGL1", - "ELOA", - "ELOB", - "ELOC", - "RICTOR", - "BID", - "BRD7", - "TNKS1BP1", - "SUPT16H", - "USP7", - "BRPF3", - "GSR", - "ARID3A", - "CNOT11", - "TSC2", - "CNOT10", - "GTF2F1", - "MSH2", - "TSC1", - "GTF2F2", - "HIPK2", - "POU4F1", - "HIPK1", - "POU4F2", - "CDC25C", - "CNOT6", - "CNOT7", - "GNPDA1", - "CNOT8", - "CNOT1", - "TP53I3", - "CNOT2", - "MAPKAPK5", - "CNOT3", - "FANCD2", - "CNOT4", - "KAT6A", - "DPEP3", - "CNOT9", - "COX20", - "MRE11", - "STEAP3", - "PCNA", - "RNF34", - "CCNT2", - "CCNT1", - "AURKA", - "PRDM1", - "TAF1L", - "NDRG1", - "AURKB", - "COX6A1", - "BCL2L14", - "MPZ", - "PERP", - "RBBP4", - "KMT5A", - 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"CDC25A", - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "CHEK1", - "PSMD5", - "PSMD6", - "PSMD3", - "CHEK2", - "PSMD4", - "PSMD2", - "RPS27A", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "PSMF1" - ], - "module2_reactome_2022_filtered__Ubiquitin-dependent Degradation Of Cyclin D R-HSA-75815": [ - "PSMB9", - "PSMC6", - "PSMC4", - "PSMC5", - "CDK4", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "GSK3B", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "CCND1", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - 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"PDIA6", - "PDIA5", - "ARFGAP1", - "CTDSP2", - "SRPRB", - "DDIT3", - "HYOU1", - "KDELR3", - "HDGF", - "ATF6", - "ATF4", - "DCP2", - "CEBPB", - "ACADVL", - "CUL7", - "CEBPG", - "FKBP14", - "CREBRF", - "GOSR2", - "SRPRA", - "CXXC1", - "DCTN1", - "HERPUD1", - "MYDGF", - "ADD1", - "SERP1", - "CCL2", - "NFYA", - "SEC31A" - ], - "module2_reactome_2022_filtered__VEGFA-VEGFR2 Pathway R-HSA-4420097": [ - "RHOA", - "HSP90AA1", - "JUP", - "CAV1", - "SPHK1", - "BAIAP2", - "VAV1", - "MAPK13", - "VAV2", - "VEGFA", - "MAPK14", - "VAV3", - "MAPK11", - "MAPK12", - "AXL", - "PIK3CB", - "PXDN", - "PIK3CA", - "ABI1", - "MAPKAPK2", - "ABI2", - "ELMO1", - "MAPKAPK3", - "ELMO2", - "NCF1", - "DOCK1", - "ARHGEF7", - "BCAR1", - "CALM1", - "AHCYL1", - "CYFIP2", - "CYFIP1", - "ROCK1", - "ROCK2", - "SH2D2A", - "SHC2", - "MAPKAP1", - "ITPR2", - "ITPR3", - "BRK1", - "PIK3R2", - "HOXC13", - "SHB", - "HSPB1", - "ITPR1", - "NRAS", - "CDH5", - "AKT3", - "AKT1", - "AKT2", - "KDR", - "WASF1", - "WASF2", - "WASF3", - "NANOS3", - "PRKCD", - "NCKAP1L", - "PDPK1", - "CYBB", - "PRKCB", - "CYBA", - "PRKCA", - "MTOR", - "RNASE1", - "CDC42", - "PTK2", - "PRKCZ", - "PIK3R1", - "PKN1", - "CRK", - "PKN2", - "TRIB3", - "PRR5", - "SRC", - "NCF2", - "CTNNB1", - "NCF4", - "NCKAP1", - "ITGB3", - "PRKACG", - "SYNGAP1", - "THEM4", - "MLST8", - "PTK2B", - "CTNNA1", - "HRAS", - "PLCG1", - "FYN", - "ITGAV", - "RICTOR", - "PRKACB", - "NCK1", - "NCK2" - ], - "module2_reactome_2022_filtered__Vif-mediated Degradation Of APOBEC3G R-HSA-180585": [ - "PSMB9", - "RBX1", - "PSMC6", - "PSMC4", - "PSMC5", - "PSMC2", - "PSMC3", - "PSMC1", - "PSMD11", - "PSMD10", - "PSMD13", - "PSMD12", - "PSMD14", - "PSMB11", - "PSMB10", - "PSMD9", - "PSMD7", - "PSMD8", - "PSMD5", - "PSMD6", - "PSMD3", - "PSMD4", - "PSMD2", - "RPS27A", - "APOBEC3G", - "PSMA6", - "PSMA7", - "PSMA4", - "PSMA5", - "SEM1", - "PSMA2", - "PSMA3", - "PSME4", - "PSMA1", - "PSME2", - "PSME3", - "PSME1", - "UBA52", - "CUL5", - "PSMA8", - "PSMB7", - "PSMB8", - "PSMB5", - "PSMB6", - "PSMB3", - "UBC", - "PSMB4", - "UBB", - "PSMB1", - "PSMB2", - "ELOB", - "ELOC", - "PSMF1" - ], - "module2_reactome_2022_filtered__Viral mRNA Translation R-HSA-192823": [ - "RPL22", - "RPS8", - "RPL21", - "RPS9", - "RPS6", - "RPL23", - "RPS7", - "RPL13A", - "RPS3A", - "GNL3", - "RPSA", - "DNAJC3", - "XK", - "RPL37A", - "FAU", - "RPL26", - "RPL28", - "RPL22L1", - "RPL27", - "GRSF1", - "RPL29", - "RPL26L1", - "RPL11", - "RPL12", - "RPS27L", - "RPS4Y2", - "RPS4Y1", - "RPL36AL", - "RPL36A", - "RPL15", - "RPL14", - "RPS5", - "RPS2", - "RPS3", - "RPL19", - "RPS15A", - "RPL18", - "RPS27A", - "RPL41", - "PBRM1", - "RPL35A", - "RPL23A", - "RPL39L", - "RPL3L", - "RPS27", - "RPS29", - "RPS28", - "RPL27A", - "RPS21", - "RPS20", - "RPS23", - "UBA52", - "RPS25", - "RSL24D1", - "RPS24", - "RPL5", - "RPL6", - "RPL3", - "RPL31", - "RPL30", - "RPL4", - "RPLP0", - "RPL32", - "RPL10L", - "RPL35", - "RPLP2", - "RPL34", - "RPLP1", - "RPL10A", - "MRPL13", - "RPL9", - "RPL7", - "RPL8", - "RPS16", - "BLOC1S6", - "RPS15", - 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"ZCCHC7", - "AUH", - "SLC71A2", - "AOPEP", - "SNX30", - "MRRF", - "NR6A1", - "LRSAM1", - "GPR107", - "NOTCH1", - "USP6NL", - "MINDY3", - "RSU1", - "PARD3", - "ZEB1", - "HERC4", - "LRMDA", - "CAMK2G", - "TCF7L2", - "CNNM2", - "BTBD10", - "IMMP1L", - "HSD17B12", - "CSTPP1", - "CELF1", - "INTS4", - "ATM", - "SLX4IP", - "CPSF7", - "MTCL2", - "SLC9D1", - "LATS2", - "ADGRL3", - "KIAA1328", - "MIA2", - "DLG2", - "MTMR12", - "DOCK1", - "PIP4K2A", - "FOXO1", - "ITPR1", - "NGLY1", - "UEVLD", - "UBE3B", - "IPMK", - "DLG5", - "SLC2A13", - "GXYLT1", - "DIP2C", - "MAGI1", - "CSNK1G3", - "SRFBP1", - "MIPOL1", - "EXT2", - "NUBPL", - "NEK7", - "FER", - "UPF2", - "SCLT1", - "FRMD4A", - "VTI1A", - "TEX9", - "ZNF827", - "NR3C2", - "ANKAR", - "RNF144A", - "ASAP2", - "ADAM17", - "FLI1", - "WWC2", - "BICD1", - "NBAS", - "CACUL1", - "RABGAP1L", - "FAM168B", - "PTPN14", - "MGAT5", - "SETBP1", - "EPG5", - "ARK2N", - "ATG10", - "XRCC4", - "DCLRE1C", - "USP12", - "PAN3", - "MBNL1", - "SLC30A6", - "FARP1", - "UTRN", - 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"OR7A11P", - "WWP2", - "UVRAG", - "ZNF26", - "NTRK1", - "ZNF658B", - "MAFK", - "NUDT16", - "PPIAP22", - "KLHL9", - "TAFA2", - "LINC01565", - "EIF1AY", - "OR10R3P", - "MT-CO2", - "ANKRD13B", - "SMIM11P1", - "LRRTM3", - "MTCO3P12", - "CNOT7", - "BRINP2", - "LRRC10", - "GRM3", - "UBE2J1", - "R3HDM4", - "MTND4LP30", - "STKLD1", - "TYW1", - "ITPRIPL1", - "PRC1", - "MAP3K3", - "PJA2", - "OR2L2", - "CHIAP2", - "TCEAL6", - "NEU4", - "LOC100287792", - "ECEL1P1", - "C2orf72", - "NHSL2", - "SYT15", - "RPL12P16", - "GPANK1", - "PRAMEF7", - "RACK1", - "AKT1S1", - "LINC01951", - "FGF7P3", - "PCDHA1", - "KRT17P5", - "LINC01460", - "HTN3", - "CFAP144P2", - "ANKRD20A9P", - "COL6A6", - "ANKRD28", - "ZNF580", - "RPL23AP80", - "RPL29P7", - "CHP1P2", - "SCYL2P1", - "GAPDHP36", - "RPL13P", - "PCNAP3", - "RPSAP53", - "NOTO", - "LOC102724637", - "MIR17HG", - "GPR89P", - "RPL22P14", - "FAM200A", - "TTC4P1", - "SFTPA3P", - "AGBL4-AS1", - "CATIP-AS2", - "RCC2P5", - "RN7SL188P", - "RN7SL807P", - "RPL35AP10", - "RPSAP5", - "NT5C3AP2", - "PGBD4P4", - "IL20RB-AS1", - "CRYZP2", - "LINC02282", - "EIF4EBP1P1", - "CTNS-AS1", - "MIR4705", - "NARF-AS2", - "MIR4735", - "APOOP1", - "SNORD114-21", - "hsa-mir-1253", - "FADS2B", - "MIR6735", - "RN7SL166P", - "MIR6511A1", - "MIR6793", - "MIR1264" - ], - "module3_processed__SM_39": [ - "KRIT1", - "CDC14B", - "SGK3", - "FOXP1", - "HDAC8", - "SPICE1", - "ANAPC10", - "MIR4435-2HG", - "GUSBP15", - "ATP6AP1-DT", - "LINC00474", - "DEFT1P", - "ZDHHC12-DT", - "LOC102723305", - "RPS6P14", - "LOC124907913", - "MTCYBP32", - "HMGN2P48", - "RPL19P5", - "SHQ1P1", - "RN7SL214P", - "LINC02202", - "FABP6-AS1", - "LINC02021", - "ST13P3", - "CTAGE6" - ], - "module3_processed__SM_40": [ - "ARHGAP44", - "DAPK2", - "PLEKHA5", - "KIF16B", - "PTPRS", - "LIFR", - "MOB3B", - "RUNX2", - "SPECC1", - "SLC27A1", - "PDZD2", - "FHOD3", - "LRRC1", - "B4GALNT3", - "GABPB2", - "RHBDL2", - "LRP5", - "ZNF704", - "PPFIBP2", - "MTMR10", - "PRR23B", - "SLC25A51P4", - "ZNF766" - ], - "module3_processed__SM_41": [ - "NFIX", - "PEX3", - "TRIT1", - "NEO1", - "IGF2BP2", - "TUBE1", - "MAPKAPK5", - "FMR1", - "PPP1R13L", - "ITGB8", - "ERLIN1", - "BMPR1A", - "FAM184A", - "HBS1L", - "PEX7", - "HECA", - "SCAP", - "UTP20", - "SIRT5", - "QRSL1", - "PLXNA3", - "LANCL2", - "MORC4", - "PTGFRN", - "ATAD1", - "TAF4B", - "CTDSPL", - "EGFR", - "ZNF462", - "ANK3", - "TMEM267", - "ADGRA3", - "ZUP1", - "NUS1", - "APOOL", - "SLC25A28", - "SLC37A3", - "PPP1R9A", - "LSS", - "DDR2", - "DNAAF5", - "FZD6", - "FBXO22", - "CMTM8", - "GJC3", - "WDR73", - "CASC2", - "SERHL2", - "LHFPL6", - "SLIT3", - "ZBTB7C", - "SLC2A3P2", - "CIB1", - "AKAP14", - "CYP4F3", - "OR13C8", - "SCGB2B2", - "OR10C1", - "CTBP2P4", - "LYRM4-AS1" - ], - "module3_processed__SM_42": [ - "OSBPL5", - "LTBP1", - "NCKAP1", - "TRAM2", - "PLPP1", - "MAST4", - "RBMS2", - "SYNJ2", - "TNS1", - "ITGB5", - "FAT1", - "SORBS1", - "SEC23A", - "PPFIBP1", - "ATN1", - "PTK7", - "IFT43", - "PDZRN3", - "ACVR2A", - "FOXP2", - "PALLD", - "HEATR5A", - "MICAL2", - "LAMC1", - "ARMC9", - "DYNC2LI1", - "SEPTIN11", - "LRIG3", - "GULP1", - "PHLDB2", - "PDGFC", - "OSMR", - "TNKS1BP1", - "CRIM1", - "SEC24D", - "EPS8", - "DDAH1", - "ENAH", - "GRAMD2B", - "RBPMS", - "KIF26B", - "PRKCI", - "GALNT10", - "REEP3", - "SPSB1", - "HORMAD2", - "GALNT9", - "PDE6G", - "CA5BP1", - "KLHL14", - "DNM3", - "DIO1" - ], - "module3_processed__SM_43": [ - "SLK", - "MAPK6", - "ACTN1", - "STK24", - "CD276", - "CORO1C", - "SMAD5", - "AGAP3", - "PHC2", - "NIBAN2", - "RDX", - "SH3GL1", - "CTNNB1", - "CLIC4", - "ARHGAP30" - ], - "module3_processed__SM_44": [ - "PTBP1", - "NUB1", - "CLK1", - "OFD1", - "TPR", - "NOP58", - "GDI2", - "TAF11", - "THUMPD1", - "XRCC5", - "RSBN1", - "FUS", - "PAPOLA", - "HNRNPC", - "SUPT16H", - "CDC5L", - "HNRNPH3", - "HNRNPM", - "RBM23", - "YY1", - "PNN", - "PRPF6", - "DNAJC2", - "DNAJB6", - "TAX1BP1", - "EIF3A", - "SMC3", - "KPNB1", - "CHORDC1", - "RSRC2", - "KRR1", - "PDCD10", - "COMMD2", - "ACTR3", - "SF3B1", - "CEBPZ", - "CAPZA1", - "SFPQ", - "SRSF11", - "PRRC2C", - "UBE2B", - "RBM25", - "SEPTIN7", - "SMU1", - "AMD1", - "MPHOSPH10", - "LRRFIP1", - "HNRNPR", - "KTN1", - "ORMDL1", - "ILF3", - "SNX6", - "SAFB2", - "RBM39", - "UBE2D2", - "SNRPA1", - "PNISR", - "PRPF38B", - "RBM17", - "SYNCRIP", - "MED4", - "TRA2B", - "ACTR2", - "PPIG", - "CIRSR", - "G3BP2", - "TDG", - "TCF25", - "TIPRL", - "SNRNP200", - "UBXN4", - "ATG12", - "DDX46", - "LUC7L2", - "RBMX", - "MTDH", - "SMARCA5", - "HNRNPU", - "USP16", - "SON", - "SAP30BP", - "RBBP4", - "CCNL1", - "RAD21", - "PSIP1", - "SUGT1", - "FAM204A", - "TAF1D", - "SF1", - "MAPK1IP1L", - "DNAJC7", - "KIF5B", - "ZNF24", - "AKIRIN1", - "DHX36", - "ZNF746", - "OTOP3", - "NPIPA1", - "SEMA4A", - "SULT1A1", - "B3GNT6", - "CPSF1P1", - "SRSF6P1", - "NDUFB1P2" - ], - "module3_processed__SM_45": [ - "REX1BD", - "RPS20", - "TMSB10", - "RPL26L1", - "MRPS10", - "MRPL43", - "MRPS35", - "RPL18", - "RPL31", - "ACADVL", - "MRPS34", - "MRPL22", - "RPS5", - "MRPL28", - "RTRAF", - "RPL6", - "RPLP0", - "MRPS33", - "MRPS18A", - "RPL3", - "RAB5IF", - "C19orf53", - "RPS16", - "MRPL4", - "RPS19", - "RPL18A", - "ETHE1", - "MRPL32", - "RPL28", - "RPL19", - "MRPL27", - "RPL34", - "C11orf58", - "RPS13", - "MRPL51", - "C12orf57", - "MRPL18", - "RPS12", - "MRPL2", - "MRPS30", - "RPL24", - "MRPL3", - "RPS15", - "MRPL37", - "RPL22", - "MRPS15", - "RPS25", - "MRPS14", - "RPL21", - "MRPS2", - "RPL5", - "TRIR", - "RPS10", - "MRPS7", - "RPL23", - "MRPS26", - "MRPS12", - "RPL36", - "MRPL34", - "METTL26", - "SHFL", - "MRPS25", - "RPL27", - "MRPL35", - "ERG28", - "KGD4", - "RPS15A", - "MRPL44", - "MRPL47", - "C9orf78", - "MRPL50", - "RPL35", - "RPS6", - "MRPL15", - "CFAP68", - "SQOR", - "RPLP1", - "RPS24", - "MYG1", - "RPS2", - "RMP24", - "RPS11", - "RPL13A", - "RPL11", - "RPS8", - "MRPL24", - "MRPL9", - "LTAP1", - "C1orf35", - "RPS27A", - "MRPS5", - "RPL32", - "RPS3A", - "RPL37", - "RPL7L1", - "CHLSN", - "RPL10", - "MRPS28", - "RPL7", - "RPL7A", - "RPS3", - "MRPL49", - "FAU", - "MRPL39", - "ATP5MJ", - "RPL30", - "MRPL17", - "CFAP298", - "MRPL10", - "RPL8", - "RPL26", - "RPL29", - "CZIB", - "MRPL55", - "MRPS18C", - "KRTCAP2", - "RPL9", - "RPN1", - "RPS14", - "FMC1", - "RPL36AL", - "RPL27A", - "MRPL16", - "RPL13", - "C19orf48P", - "NUDT8", - "MRPL58", - "RPSA", - "C15orf40", - "GON7", - "RPS9", - "C11orf24", - "BBLN", - "MRPL36", - "RPS21", - "RPS7", - "MRPL13", - "MRPL52", - "RPL38", - "MRPL57", - "MICOS10", - "UQCC4", - "RPL4", - "MRPL11", - "RPL15", - "MICOS13", - "MRPS22", - "CLTB", - "RPS6KB2", - "TTTY14", - "CD163", - "ZNF491", - "BET1L", - "ZNF648", - "OR52N2", - "OR52D1", - "OR4C6", - "GOLGA8CP", - "ERCC6L2", - "UBA7", - "AP1S2", - "MAFA", - "GRID1", - "TCHHL1", - "TCEAL7", - "FAM167B", - "INTS5", - "ACTBP7", - "ATP4B", - "AQP7P1", - "NF2", - "PRR5", - "TMEM216", - "DPPA3P2", - "ZNF833P", - "CR1L", - "CFAP43", - "AADACL2", - "GOLGA6L9", - "ODAD3", - "ZNF560", - "DDX42", - "CEP290", - "UNC13B", - "PLXNB3", - "EPS8L3", - "ZNF830", - "MAGEB3", - "RYR3", - "PNMA5", - "MT-ND4", - "CIPC", - "L1CAM", - "RASGEF1A", - "MAGEE1", - "SLC44A4", - "DHX16", - "SMG1P2", - "GPX3", - "HMGN2P32", - "PPIAP68", - "ZNF726", - "RPL23AP29", - "OR4E2", - "TBX18-AS1", - "ASTN2-AS1", - "NPM1P9", - "LOC124902609", - "ZDHHC20P1", - "SUCLG2-DT", - "RN7SL594P", - "LINC02046", - "RN7SL452P", - "VSIG8", - "ELFN2-1", - "SUMO2P20", - "LINC01475", - "LINC01169", - "MIR3929", - "TBC1D3P5", - "HUPCOS" - ], - "module3_processed__SM_46": [ - "IL32", - "TRAF3IP3", - "IDS", - "CD6", - "WAS", - "DEF6", - "BTN3A1", - "SH2D2A", - "CASP8", - "CNN2", - "CLEC2D", - "ACAP1", - "ARHGEF1", - "PPP2R5C", - "PTPRC", - "STK17B", - "FYB1", - "FXYD5", - "DDX24", - "RGS1", - "FLT3LG", - "CREM", - "MFNG", - "PIK3IP1", - "SUN2", - "GRAP2", - "STK4", - "RNF125", - "CORO1A", - "LEPROTL1", - "CD37", - "IL27RA", - "RASAL3", - "GIMAP2", - "AKNA", - "SPOCK2", - "IL10RA", - "CD5", - "SELPLG", - "BIN2", - "CDKN1B", - "ARHGDIB", - "CCND3", - "ITK", - "ZAP70", - "CYTIP", - "SRSF7", - "CD2", - "CD48", - "YPEL5", - "SRGN", - "ADGRE5", - "ARHGAP9", - "S1PR4", - "HCST", - "EVI2A", - "BCL11B", - "RAC2", - "LSP1", - "GMFG", - "GIMAP4", - "BTG1", - "SARAF", - "PTPN22", - "LCP1", - "MYO1G", - "LIMD2", - "STAT4", - "GLIPR1", - "GPR65", - "PSTPIP1", - "SKAP1", - "CD53", - "PTPN7", - "ARL6IP5", - "PPP1R18", - "MSN", - "IL2RG", - "DOK2", - "ZFP36L2", - "NR4A2", - "CD96", - "SAMSN1", - "SLA", - "TSC22D3", - "RNF166", - "ITGB2", - "CD3G", - "IKZF3", - "LAPTM5", - "DENND2D", - "FCMR", - "PBXIP1", - "PYHIN1", - "SAMD3", - "SYTL3", - "SLFN5", - "CD3D", - "TMC8", - "RHOH", - "CD52", - "EMB", - "PTGER4", - "RASGRP1", - "THEMIS", - "CD7", - "RESF1", - "TBC1D10C", - "CYP11B2", - "OR3A1", - "ITPRID1", - "GAS1", - "ALG12", - "DNAH2", - "GPR173", - "JAG2", - "CCDC190", - "RPS23", - "GNG2", - "OR9Q2", - "PTPN1", - "POTEI", - "SLC39A10", - "ZSCAN25", - "ZNF358", - "TOX", - "BRD2", - "LINC01123", - "ARPP19P2", - "ASIC3", - "RPSAP54", - "HMGB3P30", - "GPSM3", - "IGHJ4", - "TPTE2P3", - "LINC02453" - ] -} diff --git a/model_dependencies/performer_pytorch_cont/__init__.py b/model_dependencies/performer_pytorch_cont/__init__.py deleted file mode 100644 index 97b4fa8dfe0614300aeb3b8ccc79a705a0a9e4e1..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Export the bundled Performer attention implementation. -from performer_pytorch_cont.performer_pytorch import PerformerLM, Performer, FastAttention, SelfAttention \ No newline at end of file diff --git a/model_dependencies/performer_pytorch_cont/ding_models.py b/model_dependencies/performer_pytorch_cont/ding_models.py deleted file mode 100644 index 131015306fc61734b3fd994b0c6067f37fd25fe3..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/ding_models.py +++ /dev/null @@ -1,431 +0,0 @@ -from performer_pytorch_cont.performer_pytorch import * -import torch.nn as nn -from torch_geometric.nn import SGConv -import pandas as pd -import os -from tqdm import tqdm -import networkx as nx -import pickle -import sys -class Gene2VecPositionalEmbedding_NoFreeze(nn.Module): - def __init__(self, dim, max_seq_len, gene2vec_file = None): - super().__init__() - gene2vec_weight = np.load(resolve_gene2vec_path(gene2vec_file)) - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze = False) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -class TwoLayerMLPEmb(nn.Module): - def __init__(self, base_dim): - super(TwoLayerMLPEmb, self).__init__() - self.fc1 = nn.Linear(1, 50) # Input size: 1; output size: 50. - self.fc2 = nn.Linear(50, base_dim) # Input size: 50; output size: base_dim. - - def forward(self, x): - x = torch.relu(self.fc1(x)) - x = self.fc2(x) - return x - - - -class Gene_Ontology_GNN(nn.Module): - def __init__(self, dim, max_seq_len, G_go, G_go_weight, - num_layers = 1, - device = 'cuda', - use_gene2vec = True, - gene2vec_file = None): - super().__init__() - if use_gene2vec: - gene2vec_weight = np.load(resolve_gene2vec_path(gene2vec_file)) - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight).float() - self.emb = nn.Embedding.from_pretrained(gene2vec_weight, freeze = False) - else: - self.emb = nn.Embedding(max_seq_len, dim, max_norm=True) - self.G_go = G_go.to(device) - self.G_go_weight = G_go_weight.to(device) - self.num_layers = num_layers - self.sim_layers = torch.nn.ModuleList() - for i in range(1, self.num_layers + 1): - self.sim_layers.append(SGConv(dim, dim, 1, add_self_loops = False)) #The graph must include self loops itself. - - def forward(self, x): #x: B, N or B, N, D - t = torch.arange(x.shape[1], device=x.device) #N, [0, 1, ..., N-1] - t_emb = self.emb(t) #N, D - #print("t_emb.dtype: ", t_emb.dtype) - #print("self.G_go.dtype: ", self.G_go.dtype) - #print("self.G_go_weight.dtype: ", self.G_go_weight.dtype) - for idx, layer in enumerate(self.sim_layers): - t_emb = layer(t_emb, self.G_go, self.G_go_weight) - if idx < self.num_layers - 1: - t_emb = t_emb.relu() - return t_emb - -def get_go_auto(gene_list, data_path): - go_path = data_path - if go_path[-10:] != "_graph.csv": - go_path = data_path + "_graph.csv" - - if os.path.exists(go_path): - return pd.read_csv(go_path) - else: - ## download gene2go.pkl - with open(data_path + '.pkl', 'rb') as f: - gene2go = pickle.load(f) - - gene2go = {i: list(gene2go[i]) for i in gene_list if i in gene2go} - edge_list = [] - for g1 in tqdm(gene2go.keys()): - if len(gene2go[g1]) == 0: - continue - for g2 in gene2go.keys(): - if len(gene2go[g2]) == 0: - continue - edge_list.append((g1, g2, len(np.intersect1d(gene2go[g1], gene2go[g2]))/len(np.union1d(gene2go[g1], gene2go[g2])))) - - edge_list_filter = [i for i in edge_list if i[2] > 0] - further_filter = [i for i in edge_list if i[2] > 0.1] - df_edge_list = pd.DataFrame(further_filter).rename(columns = {0: 'gene1', 1: 'gene2', 2: 'score'}) - - df_edge_list = df_edge_list.rename(columns = {'gene1': 'source', 'gene2': 'target', 'score': 'importance'}) - df_edge_list.to_csv(go_path, index = False) - return df_edge_list - -class GeneSimNetwork(): - def __init__(self, edge_list, gene_list): - self.edge_list = edge_list - self.G = nx.from_pandas_edgelist(self.edge_list, source='source', - target='target', edge_attr=['importance'], - create_using=nx.DiGraph()) - self.gene_list = gene_list - for n in self.gene_list: - if n not in self.G.nodes(): - self.G.add_node(n) - node_map = {} - for i, n in enumerate(self.gene_list): - node_map[n] = i - - edge_index_ = [(node_map[e[0]], node_map[e[1]]) for e in - self.G.edges] - self.edge_index = torch.tensor(edge_index_, dtype=torch.long).T - #self.edge_weight = torch.Tensor(self.edge_list['importance'].values) - - edge_attr = nx.get_edge_attributes(self.G, 'importance') - importance = np.array([edge_attr[e] for e in self.G.edges]) - self.edge_weight = torch.Tensor(importance) - -def get_similarity_network(gene_list, data_path, num_similar_genes_go_graph): - df_jaccard = get_go_auto(gene_list, data_path) - df_out = df_jaccard.groupby('target').apply(lambda x: x.nlargest(num_similar_genes_go_graph + 1,['importance'])).reset_index(drop = True) - return df_out #edge_list - - - -class PerformerLM_GO(nn.Module): - def __init__( - self, - *, - # num_tokens, # num of tokens - max_seq_len, # max length of sequence - dim, # dim of tokens - depth, # layers - heads, # num of heads - dim_head = 64, # dim of heads - local_attn_heads = 0, - local_window_size = 256, - causal = False, - ff_mult = 4, - nb_features = None, - feature_redraw_interval = 1000, - reversible = False, - ff_chunks = 1, - ff_glu = False, - emb_dropout = 0., - ff_dropout = 0., - attn_dropout = 0., - generalized_attention = False, - kernel_fn = nn.ReLU(), - use_scalenorm = False, - use_rezero = False, - cross_attend = False, - no_projection = False, - tie_embed = False, # False: output is num of tokens, True: output is dim of tokens //multiply final embeddings with token weights for logits, like gpt decoder// - g2v_position_emb = True, # priority: gene2vec, no embedding - auto_check_redraw = True, - qkv_bias = False, - G_go = None, - G_go_weight = None, - go_num_layers = 1, - device = 'cuda', - go_use_gene2vec = True, - gene2vec_file = None - ): - - super().__init__() - print('cast_tuple') - local_attn_heads = cast_tuple(local_attn_heads) - print('finish') - self.max_seq_len = max_seq_len - # self.token_emb = nn.Embedding(num_tokens, dim) - self.token_emb = TwoLayerMLP() - print('g2v') - if g2v_position_emb: - self.pos_emb = Gene2VecPositionalEmbedding(dim, max_seq_len, gene2vec_file=gene2vec_file) - self.layer_pos_emb = Always(None) - else: - self.pos_emb = torch.zeros_like - self.layer_pos_emb = Always(None) - self.go_conv = Gene_Ontology_GNN(dim = dim, - max_seq_len = max_seq_len, - G_go = G_go, - G_go_weight = G_go_weight, - num_layers = go_num_layers, - device = device, - use_gene2vec = go_use_gene2vec, - gene2vec_file = gene2vec_file) - print('dropout') - self.dropout = nn.Dropout(emb_dropout) - print('performer') - self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - self.norm = nn.LayerNorm(dim) - # self.to_out = nn.Linear(dim, num_tokens) if not tie_embed else None - self.to_out = nn.Linear(dim, 1) if not tie_embed else None - - def check_redraw_projections(self): - self.performer.check_redraw_projections() - - def fix_projection_matrices_(self): - self.performer.fix_projection_matrices_() - - def forward(self, x, return_encodings = False, output_attentions = False, **kwargs): - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # token and positional embedding - x = self.token_emb(x.unsqueeze(dim=-1)) - if output_attentions: - x.requires_grad_() # used for attn_map output - x += self.pos_emb(x) - x += self.go_conv(x) - x = self.dropout(x) - - # performer layers - layer_pos_emb = self.layer_pos_emb(x) - - if output_attentions: - x, attn_weights = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - # norm and to logits - x = self.norm(x) - if return_encodings: - return x, attn_weights - - if exists(self.to_out): - return self.to_out(x), attn_weights - - return (x @ self.token_emb.weight.t()), attn_weights - else: - x = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - - # norm and to logits - x = self.norm(x) - if return_encodings: - return x - - if exists(self.to_out): - x = self.to_out(x) - return x - - return x @ self.token_emb.weight.t() - - -class DualEncoderSCFM(nn.Module): - def __init__( - self, - max_seq_len, # max length of sequence - top_seq_len = 2048, - base_dim = 200, # dim of tokens - mini_enc_depth = 1, # layers in mini encoder - mini_enc_heads = 8, # num of heads in mini encoder - mini_enc_dim_head = 64, # dim of heads in mini encoder - large_dim = 1280, # dim of tokens in large encoder - large_enc_depth = 50, # layers in large encoder - large_enc_heads = 10, # num of heads in large encoder - large_enc_dim_head = 128, # dim of heads in large encoder - dec_depth = 1, # layers in decoder - dec_heads = 8, # num of heads decoder - dec_dim_head = 64, # dim of heads decoder - mask_token_thres = -1, #values smaller than -1 is a mask - ############ All default below ########### - local_attn_heads = 0, - local_window_size = 256, - causal = False, - ff_mult = 4, - nb_features = None, - feature_redraw_interval = 1000, - reversible = False, - ff_chunks = 1, - ff_glu = False, - emb_dropout = 0., - ff_dropout = 0., - attn_dropout = 0., - generalized_attention = False, - kernel_fn = nn.ReLU(), - use_scalenorm = False, - use_rezero = False, - cross_attend = False, - no_projection = False, - g2v_position_emb = True, # priority: gene2vec, no embedding - auto_check_redraw = True, - qkv_bias = False, - ################### For gnn-go encoder ########## - G_go = None, - G_go_weight = None, - go_num_layers = 1, - device = 'cuda', - go_use_gene2vec = True, - gene2vec_file = None - ): - - super().__init__() - local_attn_heads = cast_tuple(local_attn_heads) - self.max_seq_len = max_seq_len - self.mask_token_thres = mask_token_thres - self.top_seq_len = top_seq_len - self.base_dim = base_dim - self.large_dim = large_dim - - self.token_emb = TwoLayerMLPEmb(base_dim) - self.token_norm = nn.LayerNorm(base_dim) - self.mask_emb = nn.Embedding(1, base_dim, max_norm=True, dtype = torch.float32) - - if g2v_position_emb: - self.pos_emb = Gene2VecPositionalEmbedding_NoFreeze(base_dim, max_seq_len, gene2vec_file=gene2vec_file) - self.layer_pos_emb = Always(None) - else: - self.pos_emb = torch.zeros_like - self.layer_pos_emb = Always(None) - self.go_conv = Gene_Ontology_GNN(dim = base_dim, - max_seq_len = max_seq_len, - G_go = G_go, - G_go_weight = G_go_weight, - num_layers = go_num_layers, - device = device, - use_gene2vec = go_use_gene2vec, - gene2vec_file = gene2vec_file) - self.dropout = nn.Dropout(emb_dropout) - - self.mini_encoder = Performer(base_dim, mini_enc_depth, mini_enc_heads, mini_enc_dim_head, - local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - - self.base_to_large = nn.Linear(base_dim, large_dim) - self.large_in_norm = nn.LayerNorm(large_dim) - self.large_encoder = Performer(large_dim, large_enc_depth, large_enc_heads, large_enc_dim_head, - local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - self.large_to_base = nn.Linear(large_dim, base_dim) - self.large_to_base_out_norm = nn.LayerNorm(base_dim) - - self.decoder = Performer(base_dim, dec_depth, dec_heads, dec_dim_head, - local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - self.decode_norm = nn.LayerNorm(base_dim) - - self.exp_to_out = nn.Linear(base_dim, 1) - - - def check_redraw_projections(self): - self.mini_encoder.check_redraw_projections() - self.large_encoder.check_redraw_projections() - self.decoder.check_redraw_projections() - - def fix_projection_matrices_(self): - self.mini_encoder.fix_projection_matrices_() - self.large_encoder.fix_projection_matrices_() - self.decoder.fix_projection_matrices_() - - - def forward(self, x, - return_encodings = False, - output_attentions = False, - **kwargs): - if (return_encodings or output_attentions): - output = {"top_encodings": None, - "left_encodings": None, - "merged_encodings": None, - "merged_decodings": None, - "large_enc_attentions": None, - "mini_enc_attentions": None, - "dec_attentions": None} - else: - output = None - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - d, L = self.base_dim, self.top_seq_len - _, top_indices = torch.topk(x, L, dim=1) #(B, L, ), - - x_mask = (x <= self.mask_token_thres).to(torch.float32).unsqueeze(dim=-1) #b, n, 1 - - ### token and positional embedding - x_emb = self.token_emb(x.unsqueeze(dim=-1)) #b, n, base_dim - mask_vecs = self.mask_emb(torch.zeros((b, 1), dtype= torch.long, device = device)) #b, 1, base_dim - x_emb = (1 - x_mask) * x_emb + x_mask * mask_vecs - - x_emb = self.token_norm(x_emb) #b, n, base_dim - - x_emb += self.pos_emb(x_emb) - x_emb += self.go_conv(x_emb) # caused CUDA error: device-side assert triggered - - x_emb = self.dropout(x_emb) - layer_pos_emb = self.layer_pos_emb(x_emb) - - ### Get top-L and left n- L embeddings for each row. - x_emb_top = torch.gather(x_emb, 1, top_indices.unsqueeze(-1).expand(-1, -1, d)) #b, L, d - top_mask = torch.zeros(b, n, dtype=torch.bool, device=x_emb_top.device) - top_mask.scatter_(1, top_indices, True) - x_emb_left = x_emb.masked_select(~top_mask.unsqueeze(-1)).view(b, n - L, d) #b, n - L, d - - ### Use Dual Encoder to encode them - - ### Large Encoder - x_emb_top = self.base_to_large(x_emb_top) #b, n, D - x_emb_top = self.large_in_norm(x_emb_top) - #x_enc_top = self.large_encoder(x_emb_top, pos_emb = layer_pos_emb, output_attentions = False, **kwargs) - if output_attentions: - x_enc_top, output["large_enc_attentions"] = self.large_encoder(x_emb_top, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - else: - x_enc_top = self.large_encoder(x_emb_top, pos_emb = layer_pos_emb, output_attentions = False, **kwargs) - x_enc_top = self.large_to_base(x_enc_top) #b, n, d - x_enc_top = self.large_to_base_out_norm(x_enc_top) - - ### Mini Encoder - x_enc_left = self.mini_encoder(x_emb_left, pos_emb = layer_pos_emb, output_attentions = False, **kwargs) - #if output_attentions: - # x_enc_left, output["mini_enc_attentions"] = self.mini_encoder(x_emb_left, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - - ### Merge top and left encodings - x_enc_merge = torch.empty_like(x_emb) - x_enc_merge.scatter_(1, top_indices.unsqueeze(-1).expand(-1, -1, d), x_enc_top) - x_enc_merge.masked_scatter_(~top_mask.unsqueeze(-1), x_enc_left) #b, n, d - - ### Decode the merged encodings: - x_enc_merge += self.pos_emb(x_enc_merge) - x_enc_merge += self.go_conv(x_enc_merge) - #if output_attentions: - # x_dec_merge, output["dec_attentions"] = self.decoder(x_enc_merge, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - x_dec_merge = self.decoder(x_enc_merge, pos_emb = layer_pos_emb, output_attentions = False, **kwargs) - x_dec_merge = self.decode_norm(x_dec_merge) - exp_out = self.exp_to_out(x_dec_merge) #Pretrain - - if not return_encodings: - if output_attentions: - return exp_out, output - else: - return exp_out - else: - output["top_encodings"] = x_enc_top - output["left_encodings"] = x_enc_left - output["merged_encodings"] = x_enc_merge - output["merged_decodings"] = x_dec_merge - return output diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch.py b/model_dependencies/performer_pytorch_cont/performer_pytorch.py deleted file mode 100644 index 6e71bf30fc28f0ed522fe1e0e5f9658e2ec74d07..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch.py +++ /dev/null @@ -1,678 +0,0 @@ -import math -from pathlib import Path -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from torch.cuda.amp import autocast -from einops import rearrange, repeat - -from functools import partial -from contextlib import contextmanager - -from local_attention import LocalAttention -from performer_pytorch_cont.reversible import ReversibleSequence, SequentialSequence - -try: - from apex import amp - APEX_AVAILABLE = True -except: - APEX_AVAILABLE = False - -# helpers - -def resolve_gene2vec_path(gene2vec_file): - """Validate an explicit embedding file for a legacy gene vocabulary.""" - if gene2vec_file is None: - raise ValueError( - 'Supply gene2vec_file with embeddings matched to this legacy model gene order. ' - 'The original gene2vec_16906.npy file is not bundled.' - ) - path = Path(gene2vec_file).expanduser().resolve() - if not path.is_file(): - raise FileNotFoundError(f'Gene2vec embeddings not found: {path}') - return str(path) - - -def exists(val): - return val is not None - -def empty(tensor): - return tensor.numel() == 0 - -def default(val, d): - return val if exists(val) else d - -@contextmanager -def null_context(): - yield - -def cast_tuple(val): - return (val,) if not isinstance(val, tuple) else val - -# def get_module_device(module): -# return next(module.parameters).device - -def get_module_device(module): - try: - return next(module.parameters()).device - except StopIteration: - # For nn.DataParallel compatibility in PyTorch 1.5 - def find_tensor_attributes(module): - tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] - return tuples - gen = module._named_members(get_members_fn=find_tensor_attributes) - first_tuple = next(gen) - return first_tuple[1].device - -def find_modules(nn_module, type): - return [module for module in nn_module.modules() if isinstance(module, type)] - -class Always(nn.Module): - def __init__(self, val): - super().__init__() - self.val = val - - def forward(self, *args, **kwargs): - return self.val - -# kernel functions - -# transcribed from jax to pytorch from -# https://github.com/google-research/google-research/blob/master/performer/fast_attention/jax/fast_attention.py - -def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - ratio = (projection_matrix.shape[0] ** -0.5) - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - diag_data = data ** 2 - diag_data = torch.sum(diag_data, dim=-1) - diag_data = (diag_data / 2.0) * (data_normalizer ** 2) - diag_data = diag_data.unsqueeze(dim=-1) - - if is_query: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - - torch.max(data_dash, dim=-1, keepdim=True).values) + eps) - else: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - torch.max(data_dash)) + eps) - - return data_dash.type_as(data) - -def generalized_kernel(data, *, projection_matrix, kernel_fn = nn.ReLU(), kernel_epsilon = 0.001, normalize_data = True, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - if projection_matrix is None: - return kernel_fn(data_normalizer * data) + kernel_epsilon - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - data_prime = kernel_fn(data_dash) + kernel_epsilon - return data_prime.type_as(data) - -def orthogonal_matrix_chunk(cols, device = None): - unstructured_block = torch.randn((cols, cols), device = device) - q, r = torch.qr(unstructured_block.cpu(), some = True) - q, r = map(lambda t: t.to(device), (q, r)) - return q.t() - -def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, device = None): - nb_full_blocks = int(nb_rows / nb_columns) - - block_list = [] - - for _ in range(nb_full_blocks): - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q) - - remaining_rows = nb_rows - nb_full_blocks * nb_columns - if remaining_rows > 0: - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q[:remaining_rows]) - - final_matrix = torch.cat(block_list) - - if scaling == 0: - multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1) - elif scaling == 1: - multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device) - else: - raise ValueError(f'Invalid scaling {scaling}') - - return torch.diag(multiplier) @ final_matrix - -# linear attention classes with softmax kernel - -# non-causal linear attention -def linear_attention(q, k, v): - k_cumsum = k.sum(dim = -2) - D_inv = 1. / torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...de', k, v) - out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv) - return out - -# efficient causal linear attention, created by EPFL -# TODO: rewrite EPFL's CUDA kernel to do mixed precision and remove half to float conversion and back -def causal_linear_attention(q, k, v, eps = 1e-6): - from fast_transformers.causal_product import CausalDotProduct - autocast_enabled = torch.is_autocast_enabled() - is_half = isinstance(q, torch.cuda.HalfTensor) - assert not is_half or APEX_AVAILABLE, 'half tensors can only be used if nvidia apex is available' - cuda_context = null_context if not autocast_enabled else partial(autocast, enabled = False) - - causal_dot_product_fn = amp.float_function(CausalDotProduct.apply) if is_half else CausalDotProduct.apply - - k_cumsum = k.cumsum(dim=-2) + eps - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - - with cuda_context(): - if autocast_enabled: - q, k, v = map(lambda t: t.float(), (q, k, v)) - - out = causal_dot_product_fn(q, k, v) - - out = torch.einsum('...nd,...n->...nd', out, D_inv) - return out - -# inefficient causal linear attention, without cuda code, for reader's reference -# not being used -def causal_linear_attention_noncuda(q, k, v, chunk_size = 128): - last_k_cumsum = 0 - last_context_cumsum = 0 - outs = [] - - for q, k, v in zip(*map(lambda t: t.chunk(chunk_size, dim = -2), (q, k, v))): - k_cumsum = last_k_cumsum + k.cumsum(dim=-2) - - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...nde', k, v) - context_cumsum = last_context_cumsum + context.cumsum(dim=-3) - out = torch.einsum('...nde,...nd,...n->...ne', context_cumsum, q, D_inv) - - last_k_cumsum = k_cumsum[:, :, -1:] - last_context_cumsum = context_cumsum[:, :, -1:] - outs.append(out) - - return torch.cat(outs, dim = -2) - -def norm_tensor(tensor, dim=-1): - return tensor / tensor.sum(dim=dim).unsqueeze(dim) - -class FastAttention(nn.Module): - def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), no_projection = False): - super().__init__() - nb_features = default(nb_features, int(dim_heads * math.log(dim_heads))) - - self.dim_heads = dim_heads - self.nb_features = nb_features - self.ortho_scaling = ortho_scaling - - self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling) - projection_matrix = self.create_projection() - self.register_buffer('projection_matrix', projection_matrix) - - self.generalized_attention = generalized_attention - self.kernel_fn = kernel_fn - - # if this is turned on, no projection will be used - # queries and keys will be softmax-ed as in the original efficient attention paper - self.no_projection = no_projection - - self.causal = causal - if causal: - try: - import fast_transformers.causal_product.causal_product_cuda - self.causal_linear_fn = partial(causal_linear_attention) - except ImportError: - print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version') - self.causal_linear_fn = causal_linear_attention_noncuda - - @torch.no_grad() - def redraw_projection_matrix(self, device): - projections = self.create_projection(device = device) - self.projection_matrix.copy_(projections) - del projections - - def forward(self, q, k, v, output_attentions = False): - device = q.device - # inds = [8060, 8064, 6243, 8575, 10342, 10913, 9366, 993, 7796, 5210, 5212, 5504, 6851, 6559, 5508, 13107, 13820] - if self.no_projection: - q = q.softmax(dim = -1) - k = torch.exp(k) if self.causal else k.softmax(dim = -2) - - elif self.generalized_attention: - create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device) - q, k = map(create_kernel, (q, k)) - - else: - create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device) - q = create_kernel(q, is_query = True) - k = create_kernel(k, is_query = False) - - attn_fn = linear_attention if not self.causal else self.causal_linear_fn - out = attn_fn(q, k, v) - if output_attentions: - v_diag = torch.eye(v.shape[-2]).to(device) - v_diag = v_diag.unsqueeze(0).unsqueeze(0).repeat(v.shape[0],v.shape[1],1,1) - # attn_weights = torch.zeros(1, 1, len(inds), len(inds)).to(device).to(torch.float16) - # attn_weights = torch.zeros(1, q.shape[1], len(inds), len(inds)).to(device).to(torch.float16) - attn_weights = torch.zeros(1, 1, q.shape[2], q.shape[2]).to(device).to(torch.float16) - for head_dim in range(q.shape[1]): - # attn_weights[0, head_dim] = torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16)))[0, inds][:, inds] - attn_weights += torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16))) - # attn_weights += norm_tensor(torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16))), dim=-1) - attn_weights /= q.shape[1] - return out, attn_weights - else: - return out - -# classes - -class ReZero(nn.Module): - def __init__(self, fn): - super().__init__() - self.g = nn.Parameter(torch.tensor(1e-3)) - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(x, **kwargs) * self.g - -class PreScaleNorm(nn.Module): - def __init__(self, dim, fn, eps=1e-5): - super().__init__() - self.fn = fn - self.g = nn.Parameter(torch.ones(1)) - self.eps = eps - - def forward(self, x, **kwargs): - n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps) - x = x / n * self.g - return self.fn(x, **kwargs) - -class PreLayerNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - def forward(self, x, **kwargs): - return self.fn(self.norm(x), **kwargs) - -class Chunk(nn.Module): - def __init__(self, chunks, fn, along_dim = -1): - super().__init__() - self.dim = along_dim - self.chunks = chunks - self.fn = fn - - def forward(self, x, **kwargs): - if self.chunks == 1: - return self.fn(x, **kwargs) - chunks = x.chunk(self.chunks, dim = self.dim) - return torch.cat([self.fn(c, **kwargs) for c in chunks], dim = self.dim) - -class FeedForward(nn.Module): - def __init__(self, dim, mult = 4, dropout = 0., activation = None, glu = False): - super().__init__() - activation = default(activation, nn.GELU) - - self.glu = glu - self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) - self.act = activation() - self.dropout = nn.Dropout(dropout) - self.w2 = nn.Linear(dim * mult, dim) - - def forward(self, x, **kwargs): - if not self.glu: - x = self.w1(x) - x = self.act(x) - else: - x, v = self.w1(x).chunk(2, dim=-1) - x = self.act(x) * v - - x = self.dropout(x) - x = self.w2(x) - return x - -class CausalSelfAttention_TSSA(nn.Module): - def __init__(self, dim, heads=8, dropout=0., bias=False): - super().__init__() - assert dim % heads == 0, 'dimension must be divisible by number of heads' - dim_head = dim // heads - self.to_qkv = nn.Linear(dim, dim, bias=bias) - self.to_out = nn.Linear(dim, dim) - self.attn_dropout = nn.Dropout(dropout) - self.resid_dropout = nn.Dropout(dropout) - self.n_head = heads - self.n_embd = dim - self.dropout = dropout - self.temp = nn.Parameter(torch.ones(heads, 1)) - # Use a learnable scaling factor rather than a fixed-length matrix. - self.denom_bias = nn.Parameter(torch.zeros(heads, 1, 1)) - - def forward(self, x, pos_emb=None, context=None, mask=None, context_mask=None, output_attentions=False): - B, T, C = x.size() - - # Compute queries, keys, and values. - w = self.to_qkv(x) - w = w.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) - w_sq = w ** 2 - denom = (torch.cumsum(w_sq, dim=-2)).clamp_min(1e-12) - w_normed = (w_sq / denom) + self.denom_bias - tmp = torch.sum(w_normed, dim=-1) * self.temp - - Pi = F.softmax(tmp, dim=1) # B, nh, T - dots = torch.cumsum(w_sq * Pi.unsqueeze(-1), dim=-2) / (Pi.cumsum(dim=-1) + 1e-8).unsqueeze(-1) - attn = 1. / (1 + dots) - attn = self.attn_dropout(attn) - y = - torch.mul(w.mul(Pi.unsqueeze(-1)), attn) - y = y.transpose(1, 2).contiguous().view(B, T, C) # Concatenate the attention-head outputs. - y = self.resid_dropout(self.to_out(y)) - - if output_attentions: - # Return placeholder weights; TSSA does not form a conventional attention matrix. - attn_weights = torch.zeros(B, self.n_head, T, T, device=x.device) - return y, attn_weights - return y - -class SelfAttention(nn.Module): - def __init__( - self, - dim, - causal = False, - heads = 8, - dim_head = 64, - local_heads = 0, - local_window_size = 256, - nb_features = None, - feature_redraw_interval = 1000, - generalized_attention = False, - kernel_fn = nn.ReLU(), - dropout = 0., - no_projection = False, - qkv_bias = False - ): - super().__init__() - # Use TSSA for the attention operation. - self.attn = CausalSelfAttention_TSSA( - dim=dim, - heads=heads, - dropout=dropout, - bias=qkv_bias - ) - # Retain these attributes for compatibility with the existing interface. - self.heads = heads - self.global_heads = heads - local_heads - self.local_attn = None # TSSA does not use local attention. - self.to_out = self.attn.to_out # Reuse the TSSA output projection. - - def forward(self, x, pos_emb = None, context = None, mask = None, context_mask = None, output_attentions = False, **kwargs): - # Run the TSSA forward pass. - # This TSSA implementation supports self-attention only. - if exists(context) and context is not x: - raise NotImplementedError("Cross attention is not implemented in TSSA") - - # Apply TSSA attention. - return self.attn(x, pos_emb, context, mask, context_mask, output_attentions) - if output_attentions: - return self.dropout(out), attn_weights - else: - return self.dropout(out) - -# positional embeddings - -class AbsolutePositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -# rotary positional embedding helpers - -def rotate_every_two(x): - x = rearrange(x, '... (d j) -> ... d j', j = 2) - x1, x2 = x.unbind(dim = -1) - x = torch.stack((-x2, x1), dim = -1) - return rearrange(x, '... d j -> ... (d j)') - -def apply_rotary_pos_emb(q, k, sinu_pos): - sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j = 2) - sin, cos = sinu_pos.unbind(dim = -2) - sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j = 2), (sin, cos)) - q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) - return q, k - -# sinusoidal positional embeddings - -class Gene2VecPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len, gene2vec_file = None): - super().__init__() - gene2vec_weight = np.load(resolve_gene2vec_path(gene2vec_file)) - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -# performer - -class Performer(nn.Module): - def __init__( - self, - dim, # dimension - depth, # layers - heads, # heads - dim_head, # dim of head - local_attn_heads = 0, # num of local attention heads, (heads - local_attn_heads) is num of global performers - local_window_size = 256, # window size of local attention - causal = False, # autoregressive or not - ff_mult = 4, # dim of intermediate features after attention / dim of input features - nb_features = None, # number of random features, if not set, will default to (d * log(d)), where d is the dimension of each head ?? what is random feature ?? - feature_redraw_interval = 1000, # how frequently to redraw the projection matrix, the more frequent, the slower the training - reversible = False, # reversible layers, from Reformer (save memory) - ff_chunks = 1, # chunk feedforward layer, from Reformer - generalized_attention = False, # defaults to softmax approximation, but can be set to True for generalized attention ?? what is generalized attention ?? - kernel_fn = nn.ReLU(), # the kernel function to be used, if generalized attention is turned on, defaults to Relu - use_scalenorm = False, # use scale norm, from 'Transformers without Tears' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - use_rezero = False, # use Rezero or not, from 'Rezero is all you need' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - ff_glu = False, # use GLU (Gated Linear Units) variant for feedforward - ff_dropout = 0., # feedforward dropout - attn_dropout = 0., # post-attention dropout - cross_attend = False, # ?? - no_projection = False, # ?? - auto_check_redraw = True, # ?? - qkv_bias = True, # ?? - ): - super().__init__() - layers = nn.ModuleList([]) - local_attn_heads = cast_tuple(local_attn_heads) - local_attn_heads = local_attn_heads * depth if len(local_attn_heads) == 1 else local_attn_heads - assert len(local_attn_heads) == depth, 'tuple specifying number of local attention heads per depth must be equal to the total depth' - assert all(map(lambda n: n >= 0 and n <= heads, local_attn_heads)), 'local attention head value must be less than the total number of heads' - - if use_scalenorm: - wrapper_fn = partial(PreScaleNorm, dim) - elif use_rezero: - wrapper_fn = ReZero - else: - wrapper_fn = partial(PreLayerNorm, dim) - - for _, local_heads in zip(range(depth), local_attn_heads): - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, causal = causal, heads = heads, dim_head = dim_head, local_heads = local_heads, local_window_size = local_window_size, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - # if no need cross_attend(decoder), begin next cycle - if not cross_attend: - continue - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, heads = heads, dim_head = dim_head, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - - execute_type = ReversibleSequence if reversible else SequentialSequence - - route_attn = ((True, False),) * depth * (2 if cross_attend else 1) # ((True, False), (True, False), (True, False), (True, False), (True, False), (True, False)) - route_context = ((False, False), (True, False)) * depth - attn_route_map = {'mask': route_attn, 'pos_emb': route_attn} - context_route_map = {'context': route_context, 'context_mask': route_context} if cross_attend else {} - self.net = execute_type(layers, args_route = {**attn_route_map, **context_route_map}) - - # keeping track of when to redraw projections for all attention layers - self.auto_check_redraw = auto_check_redraw - self.feature_redraw_interval = feature_redraw_interval - self.register_buffer('calls_since_last_redraw', torch.tensor(0)) - - def fix_projection_matrices_(self): - self.feature_redraw_interval = None - - def check_redraw_projections(self): - if not self.training: - return - - if exists(self.feature_redraw_interval) and self.calls_since_last_redraw >= self.feature_redraw_interval: - device = get_module_device(self) - - fast_attentions = find_modules(self, FastAttention) - for fast_attention in fast_attentions: - fast_attention.redraw_projection_matrix(device) - - self.calls_since_last_redraw.zero_() - return - - self.calls_since_last_redraw += 1 - - def forward(self, x, output_attentions = False, **kwargs): - if self.auto_check_redraw: - self.check_redraw_projections() - return self.net(x, output_attentions = output_attentions, **kwargs) - -class TwoLayerMLP(nn.Module): - def __init__(self): - super(TwoLayerMLP, self).__init__() - self.fc1 = nn.Linear(1, 50) # Input size: 1, Output size: 100 - self.fc2 = nn.Linear(50, 200) # Input size: 100, Output size: 200 - - def forward(self, x): - x = torch.relu(self.fc1(x)) - x = self.fc2(x) - return x - -class PerformerLM(nn.Module): - def __init__( - self, - *, - # num_tokens, # num of tokens - max_seq_len, # max length of sequence - dim, # dim of tokens - depth, # layers - heads, # num of heads - dim_head = 64, # dim of heads - local_attn_heads = 0, - local_window_size = 256, - causal = False, - ff_mult = 4, - nb_features = None, - feature_redraw_interval = 1000, - reversible = False, - ff_chunks = 1, - ff_glu = False, - emb_dropout = 0., - ff_dropout = 0., - attn_dropout = 0., - generalized_attention = False, - kernel_fn = nn.ReLU(), - use_scalenorm = False, - use_rezero = False, - cross_attend = False, - no_projection = False, - tie_embed = False, # False: output is num of tokens, True: output is dim of tokens //multiply final embeddings with token weights for logits, like gpt decoder// - g2v_position_emb = True, # priority: gene2vec, no embedding - auto_check_redraw = True, - qkv_bias = False, - gene2vec_file = None - ): - super().__init__() - print('cast_tuple') - local_attn_heads = cast_tuple(local_attn_heads) - print('finish') - self.max_seq_len = max_seq_len - # self.token_emb = nn.Embedding(num_tokens, dim) - self.token_emb = TwoLayerMLP() - print('g2v') - if g2v_position_emb: - self.pos_emb = Gene2VecPositionalEmbedding(dim, max_seq_len, gene2vec_file=gene2vec_file) - self.layer_pos_emb = Always(None) - else: - self.pos_emb = torch.zeros_like - self.layer_pos_emb = Always(None) - print('dropout') - self.dropout = nn.Dropout(emb_dropout) - print('performer') - self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - self.norm = nn.LayerNorm(dim) - # self.to_out = nn.Linear(dim, num_tokens) if not tie_embed else None - self.to_out = nn.Linear(dim, 1) if not tie_embed else None - - def check_redraw_projections(self): - self.performer.check_redraw_projections() - - def fix_projection_matrices_(self): - self.performer.fix_projection_matrices_() - - def forward(self, x, return_encodings = False, output_attentions = False, **kwargs): - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # token and positional embedding - x = self.token_emb(x.unsqueeze(dim=-1)) - if output_attentions: - x.requires_grad_() # used for attn_map output - x += self.pos_emb(x) - x = self.dropout(x) - - # performer layers - layer_pos_emb = self.layer_pos_emb(x) - - if output_attentions: - x, attn_weights = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - # norm and to logits - x = self.norm(x) - if return_encodings: - return x, attn_weights - - if exists(self.to_out): - return self.to_out(x), attn_weights - - return (x @ self.token_emb.weight.t()), attn_weights - else: - x = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - - # norm and to logits - x = self.norm(x) - if return_encodings: - return x - - if exists(self.to_out): - x = self.to_out(x) - return x - - return x @ self.token_emb.weight.t() \ No newline at end of file diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/__init__.py b/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/__init__.py deleted file mode 100644 index 97b4fa8dfe0614300aeb3b8ccc79a705a0a9e4e1..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Export the bundled Performer attention implementation. -from performer_pytorch_cont.performer_pytorch import PerformerLM, Performer, FastAttention, SelfAttention \ No newline at end of file diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_old.py b/model_dependencies/performer_pytorch_cont/performer_pytorch_old.py deleted file mode 100644 index 6b0ed3a9171aaff2a94b7a2331560461f043c613..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch_old.py +++ /dev/null @@ -1,668 +0,0 @@ -import math -from pathlib import Path -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from torch.cuda.amp import autocast -from einops import rearrange, repeat - -from functools import partial -from contextlib import contextmanager - -from local_attention import LocalAttention -from performer_pytorch_cont.reversible import ReversibleSequence, SequentialSequence - -try: - from apex import amp - APEX_AVAILABLE = True -except: - APEX_AVAILABLE = False - -# helpers - -def resolve_gene2vec_path(gene2vec_file): - """Validate an explicit embedding file for a legacy gene vocabulary.""" - if gene2vec_file is None: - raise ValueError( - 'Supply gene2vec_file with embeddings matched to this legacy model gene order. ' - 'The original gene2vec_16906.npy file is not bundled.' - ) - path = Path(gene2vec_file).expanduser().resolve() - if not path.is_file(): - raise FileNotFoundError(f'Gene2vec embeddings not found: {path}') - return str(path) - - -def exists(val): - return val is not None - -def empty(tensor): - return tensor.numel() == 0 - -def default(val, d): - return val if exists(val) else d - -@contextmanager -def null_context(): - yield - -def cast_tuple(val): - return (val,) if not isinstance(val, tuple) else val - -# def get_module_device(module): -# return next(module.parameters).device - -def get_module_device(module): - try: - return next(module.parameters()).device - except StopIteration: - # For nn.DataParallel compatibility in PyTorch 1.5 - def find_tensor_attributes(module): - tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] - return tuples - gen = module._named_members(get_members_fn=find_tensor_attributes) - first_tuple = next(gen) - return first_tuple[1].device - -def find_modules(nn_module, type): - return [module for module in nn_module.modules() if isinstance(module, type)] - -class Always(nn.Module): - def __init__(self, val): - super().__init__() - self.val = val - - def forward(self, *args, **kwargs): - return self.val - -# kernel functions - -# transcribed from jax to pytorch from -# https://github.com/google-research/google-research/blob/master/performer/fast_attention/jax/fast_attention.py - -def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - ratio = (projection_matrix.shape[0] ** -0.5) - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - diag_data = data ** 2 - diag_data = torch.sum(diag_data, dim=-1) - diag_data = (diag_data / 2.0) * (data_normalizer ** 2) - diag_data = diag_data.unsqueeze(dim=-1) - - if is_query: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - - torch.max(data_dash, dim=-1, keepdim=True).values) + eps) - else: - data_dash = ratio * ( - torch.exp(data_dash - diag_data - torch.max(data_dash)) + eps) - - return data_dash.type_as(data) - -def generalized_kernel(data, *, projection_matrix, kernel_fn = nn.ReLU(), kernel_epsilon = 0.001, normalize_data = True, device = None): - b, h, *_ = data.shape - - data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1. - - if projection_matrix is None: - return kernel_fn(data_normalizer * data) + kernel_epsilon - - projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h) - projection = projection.type_as(data) - - data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection) - - data_prime = kernel_fn(data_dash) + kernel_epsilon - return data_prime.type_as(data) - -def orthogonal_matrix_chunk(cols, device = None): - unstructured_block = torch.randn((cols, cols), device = device) - q, r = torch.qr(unstructured_block.cpu(), some = True) - q, r = map(lambda t: t.to(device), (q, r)) - return q.t() - -def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, device = None): - nb_full_blocks = int(nb_rows / nb_columns) - - block_list = [] - - for _ in range(nb_full_blocks): - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q) - - remaining_rows = nb_rows - nb_full_blocks * nb_columns - if remaining_rows > 0: - q = orthogonal_matrix_chunk(nb_columns, device = device) - block_list.append(q[:remaining_rows]) - - final_matrix = torch.cat(block_list) - - if scaling == 0: - multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1) - elif scaling == 1: - multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device) - else: - raise ValueError(f'Invalid scaling {scaling}') - - return torch.diag(multiplier) @ final_matrix - -# linear attention classes with softmax kernel - -# non-causal linear attention -def linear_attention(q, k, v): - k_cumsum = k.sum(dim = -2) - D_inv = 1. / torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...de', k, v) - out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv) - return out - -# efficient causal linear attention, created by EPFL -# TODO: rewrite EPFL's CUDA kernel to do mixed precision and remove half to float conversion and back -def causal_linear_attention(q, k, v, eps = 1e-6): - from fast_transformers.causal_product import CausalDotProduct - autocast_enabled = torch.is_autocast_enabled() - is_half = isinstance(q, torch.cuda.HalfTensor) - assert not is_half or APEX_AVAILABLE, 'half tensors can only be used if nvidia apex is available' - cuda_context = null_context if not autocast_enabled else partial(autocast, enabled = False) - - causal_dot_product_fn = amp.float_function(CausalDotProduct.apply) if is_half else CausalDotProduct.apply - - k_cumsum = k.cumsum(dim=-2) + eps - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - - with cuda_context(): - if autocast_enabled: - q, k, v = map(lambda t: t.float(), (q, k, v)) - - out = causal_dot_product_fn(q, k, v) - - out = torch.einsum('...nd,...n->...nd', out, D_inv) - return out - -# inefficient causal linear attention, without cuda code, for reader's reference -# not being used -def causal_linear_attention_noncuda(q, k, v, chunk_size = 128): - last_k_cumsum = 0 - last_context_cumsum = 0 - outs = [] - - for q, k, v in zip(*map(lambda t: t.chunk(chunk_size, dim = -2), (q, k, v))): - k_cumsum = last_k_cumsum + k.cumsum(dim=-2) - - D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q)) - context = torch.einsum('...nd,...ne->...nde', k, v) - context_cumsum = last_context_cumsum + context.cumsum(dim=-3) - out = torch.einsum('...nde,...nd,...n->...ne', context_cumsum, q, D_inv) - - last_k_cumsum = k_cumsum[:, :, -1:] - last_context_cumsum = context_cumsum[:, :, -1:] - outs.append(out) - - return torch.cat(outs, dim = -2) - -def norm_tensor(tensor, dim=-1): - return tensor / tensor.sum(dim=dim).unsqueeze(dim) - -class FastAttention(nn.Module): - def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), no_projection = False): - super().__init__() - nb_features = default(nb_features, int(dim_heads * math.log(dim_heads))) - - self.dim_heads = dim_heads - self.nb_features = nb_features - self.ortho_scaling = ortho_scaling - - self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling) - projection_matrix = self.create_projection() - self.register_buffer('projection_matrix', projection_matrix) - - self.generalized_attention = generalized_attention - self.kernel_fn = kernel_fn - - # if this is turned on, no projection will be used - # queries and keys will be softmax-ed as in the original efficient attention paper - self.no_projection = no_projection - - self.causal = causal - if causal: - try: - import fast_transformers.causal_product.causal_product_cuda - self.causal_linear_fn = partial(causal_linear_attention) - except ImportError: - print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version') - self.causal_linear_fn = causal_linear_attention_noncuda - - @torch.no_grad() - def redraw_projection_matrix(self, device): - projections = self.create_projection(device = device) - self.projection_matrix.copy_(projections) - del projections - - def forward(self, q, k, v, output_attentions = False): - device = q.device - # inds = [8060, 8064, 6243, 8575, 10342, 10913, 9366, 993, 7796, 5210, 5212, 5504, 6851, 6559, 5508, 13107, 13820] - if self.no_projection: - q = q.softmax(dim = -1) - k = torch.exp(k) if self.causal else k.softmax(dim = -2) - - elif self.generalized_attention: - create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device) - q, k = map(create_kernel, (q, k)) - - else: - create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device) - q = create_kernel(q, is_query = True) - k = create_kernel(k, is_query = False) - - attn_fn = linear_attention if not self.causal else self.causal_linear_fn - out = attn_fn(q, k, v) - if output_attentions: - v_diag = torch.eye(v.shape[-2]).to(device) - v_diag = v_diag.unsqueeze(0).unsqueeze(0).repeat(v.shape[0],v.shape[1],1,1) - # attn_weights = torch.zeros(1, 1, len(inds), len(inds)).to(device).to(torch.float16) - # attn_weights = torch.zeros(1, q.shape[1], len(inds), len(inds)).to(device).to(torch.float16) - attn_weights = torch.zeros(1, 1, q.shape[2], q.shape[2]).to(device).to(torch.float16) - for head_dim in range(q.shape[1]): - # attn_weights[0, head_dim] = torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16)))[0, inds][:, inds] - attn_weights += torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16))) - # attn_weights += norm_tensor(torch.abs(attn_fn(q[:,head_dim].to(torch.float16), k[:,head_dim].to(torch.float16), v_diag[:,head_dim].to(torch.float16))), dim=-1) - attn_weights /= q.shape[1] - return out, attn_weights - else: - return out - -# classes - -class ReZero(nn.Module): - def __init__(self, fn): - super().__init__() - self.g = nn.Parameter(torch.tensor(1e-3)) - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(x, **kwargs) * self.g - -class PreScaleNorm(nn.Module): - def __init__(self, dim, fn, eps=1e-5): - super().__init__() - self.fn = fn - self.g = nn.Parameter(torch.ones(1)) - self.eps = eps - - def forward(self, x, **kwargs): - n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps) - x = x / n * self.g - return self.fn(x, **kwargs) - -class PreLayerNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.norm = nn.LayerNorm(dim) - self.fn = fn - def forward(self, x, **kwargs): - return self.fn(self.norm(x), **kwargs) - -class Chunk(nn.Module): - def __init__(self, chunks, fn, along_dim = -1): - super().__init__() - self.dim = along_dim - self.chunks = chunks - self.fn = fn - - def forward(self, x, **kwargs): - if self.chunks == 1: - return self.fn(x, **kwargs) - chunks = x.chunk(self.chunks, dim = self.dim) - return torch.cat([self.fn(c, **kwargs) for c in chunks], dim = self.dim) - -class FeedForward(nn.Module): - def __init__(self, dim, mult = 4, dropout = 0., activation = None, glu = False): - super().__init__() - activation = default(activation, nn.GELU) - - self.glu = glu - self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1)) - self.act = activation() - self.dropout = nn.Dropout(dropout) - self.w2 = nn.Linear(dim * mult, dim) - - def forward(self, x, **kwargs): - if not self.glu: - x = self.w1(x) - x = self.act(x) - else: - x, v = self.w1(x).chunk(2, dim=-1) - x = self.act(x) * v - - x = self.dropout(x) - x = self.w2(x) - return x - -class SelfAttention(nn.Module): - def __init__( - self, - dim, - causal = False, - heads = 8, - dim_head = 64, - local_heads = 0, - local_window_size = 256, - nb_features = None, - feature_redraw_interval = 1000, - generalized_attention = False, - kernel_fn = nn.ReLU(), - dropout = 0., - no_projection = False, - qkv_bias = False - ): - super().__init__() - assert dim % heads == 0, 'dimension must be divisible by number of heads' - dim_head = default(dim_head, dim // heads) - inner_dim = dim_head * heads - self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, no_projection = no_projection) - - self.heads = heads - self.global_heads = heads - local_heads - self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None - - self.to_q = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_k = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_v = nn.Linear(dim, inner_dim, bias = qkv_bias) - self.to_out = nn.Linear(inner_dim, dim) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, pos_emb = None, context = None, mask = None, context_mask = None, output_attentions = False, **kwargs): - b, n, _, h, gh = *x.shape, self.heads, self.global_heads - - cross_attend = exists(context) - - context = default(context, x) - context_mask = default(context_mask, mask) if not cross_attend else context_mask - - q, k, v = self.to_q(x), self.to_k(context), self.to_v(context) - - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) - (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v)) - - attn_outs = [] - - if not empty(q): - if exists(context_mask): - global_mask = context_mask[:, None, :, None] - v.masked_fill_(~global_mask, 0.) - - if exists(pos_emb) and not cross_attend: - q, k, = apply_rotary_pos_emb(q, k, pos_emb) - - if output_attentions: - out, attn_weights = self.fast_attention(q, k, v, output_attentions) - else: - out = self.fast_attention(q, k, v) - attn_outs.append(out) - - if not empty(lq): - assert not cross_attend, 'local attention is not compatible with cross attention' - out = self.local_attn(lq, lk, lv, input_mask = mask) - attn_outs.append(out) - - out = torch.cat(attn_outs, dim = 1) # combine attn_out and cross_attn_out, here we have only attn_out, that means this line does nothing - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - if output_attentions: - return self.dropout(out), attn_weights - else: - return self.dropout(out) - -# positional embeddings - -class AbsolutePositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -# rotary positional embedding helpers - -def rotate_every_two(x): - x = rearrange(x, '... (d j) -> ... d j', j = 2) - x1, x2 = x.unbind(dim = -1) - x = torch.stack((-x2, x1), dim = -1) - return rearrange(x, '... d j -> ... (d j)') - -def apply_rotary_pos_emb(q, k, sinu_pos): - sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j = 2) - sin, cos = sinu_pos.unbind(dim = -2) - sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j = 2), (sin, cos)) - q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k)) - return q, k - -# sinusoidal positional embeddings - -class Gene2VecPositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len, gene2vec_file = None): - super().__init__() - gene2vec_weight = np.load(resolve_gene2vec_path(gene2vec_file)) - gene2vec_weight = np.concatenate((gene2vec_weight, np.zeros((1, gene2vec_weight.shape[1]))), axis=0) - gene2vec_weight = torch.from_numpy(gene2vec_weight) - self.emb = nn.Embedding.from_pretrained(gene2vec_weight) - - def forward(self, x): - t = torch.arange(x.shape[1], device=x.device) - return self.emb(t) - -# performer - -class Performer(nn.Module): - def __init__( - self, - dim, # dimension - depth, # layers - heads, # heads - dim_head, # dim of head - local_attn_heads = 0, # num of local attention heads, (heads - local_attn_heads) is num of global performers - local_window_size = 256, # window size of local attention - causal = False, # autoregressive or not - ff_mult = 4, # dim of intermediate features after attention / dim of input features - nb_features = None, # number of random features, if not set, will default to (d * log(d)), where d is the dimension of each head ?? what is random feature ?? - feature_redraw_interval = 1000, # how frequently to redraw the projection matrix, the more frequent, the slower the training - reversible = False, # reversible layers, from Reformer (save memory) - ff_chunks = 1, # chunk feedforward layer, from Reformer - generalized_attention = False, # defaults to softmax approximation, but can be set to True for generalized attention ?? what is generalized attention ?? - kernel_fn = nn.ReLU(), # the kernel function to be used, if generalized attention is turned on, defaults to Relu - use_scalenorm = False, # use scale norm, from 'Transformers without Tears' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - use_rezero = False, # use Rezero or not, from 'Rezero is all you need' paper, a substitute for LayerNorm, priority: scalenorm.rezero.layernorm - ff_glu = False, # use GLU (Gated Linear Units) variant for feedforward - ff_dropout = 0., # feedforward dropout - attn_dropout = 0., # post-attention dropout - cross_attend = False, # ?? - no_projection = False, # ?? - auto_check_redraw = True, # ?? - qkv_bias = True, # ?? - ): - super().__init__() - layers = nn.ModuleList([]) - local_attn_heads = cast_tuple(local_attn_heads) - local_attn_heads = local_attn_heads * depth if len(local_attn_heads) == 1 else local_attn_heads - assert len(local_attn_heads) == depth, 'tuple specifying number of local attention heads per depth must be equal to the total depth' - assert all(map(lambda n: n >= 0 and n <= heads, local_attn_heads)), 'local attention head value must be less than the total number of heads' - - if use_scalenorm: - wrapper_fn = partial(PreScaleNorm, dim) - elif use_rezero: - wrapper_fn = ReZero - else: - wrapper_fn = partial(PreLayerNorm, dim) - - for _, local_heads in zip(range(depth), local_attn_heads): - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, causal = causal, heads = heads, dim_head = dim_head, local_heads = local_heads, local_window_size = local_window_size, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - # if no need cross_attend(decoder), begin next cycle - if not cross_attend: - continue - layers.append(nn.ModuleList([ - wrapper_fn(SelfAttention(dim, heads = heads, dim_head = dim_head, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection)), - wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)) - ])) - - execute_type = ReversibleSequence if reversible else SequentialSequence - - route_attn = ((True, False),) * depth * (2 if cross_attend else 1) # ((True, False), (True, False), (True, False), (True, False), (True, False), (True, False)) - route_context = ((False, False), (True, False)) * depth - attn_route_map = {'mask': route_attn, 'pos_emb': route_attn} - context_route_map = {'context': route_context, 'context_mask': route_context} if cross_attend else {} - self.net = execute_type(layers, args_route = {**attn_route_map, **context_route_map}) - - # keeping track of when to redraw projections for all attention layers - self.auto_check_redraw = auto_check_redraw - self.feature_redraw_interval = feature_redraw_interval - self.register_buffer('calls_since_last_redraw', torch.tensor(0)) - - def fix_projection_matrices_(self): - self.feature_redraw_interval = None - - def check_redraw_projections(self): - if not self.training: - return - - if exists(self.feature_redraw_interval) and self.calls_since_last_redraw >= self.feature_redraw_interval: - device = get_module_device(self) - - fast_attentions = find_modules(self, FastAttention) - for fast_attention in fast_attentions: - fast_attention.redraw_projection_matrix(device) - - self.calls_since_last_redraw.zero_() - return - - self.calls_since_last_redraw += 1 - - def forward(self, x, output_attentions = False, **kwargs): - if self.auto_check_redraw: - self.check_redraw_projections() - return self.net(x, output_attentions = output_attentions, **kwargs) - -class TwoLayerMLP(nn.Module): - def __init__(self): - super(TwoLayerMLP, self).__init__() - self.fc1 = nn.Linear(1, 50) # Input size: 1, Output size: 100 - self.fc2 = nn.Linear(50, 200) # Input size: 100, Output size: 200 - - def forward(self, x): - x = torch.relu(self.fc1(x)) - x = self.fc2(x) - return x - -class PerformerLM(nn.Module): - def __init__( - self, - *, - # num_tokens, # num of tokens - max_seq_len, # max length of sequence - dim, # dim of tokens - depth, # layers - heads, # num of heads - dim_head = 64, # dim of heads - local_attn_heads = 0, - local_window_size = 256, - causal = False, - ff_mult = 4, - nb_features = None, - feature_redraw_interval = 1000, - reversible = False, - ff_chunks = 1, - ff_glu = False, - emb_dropout = 0., - ff_dropout = 0., - attn_dropout = 0., - generalized_attention = False, - kernel_fn = nn.ReLU(), - use_scalenorm = False, - use_rezero = False, - cross_attend = False, - no_projection = False, - tie_embed = False, # False: output is num of tokens, True: output is dim of tokens //multiply final embeddings with token weights for logits, like gpt decoder// - g2v_position_emb = True, # priority: gene2vec, no embedding - auto_check_redraw = True, - qkv_bias = False, - gene2vec_file = None - ): - super().__init__() - print('cast_tuple') - local_attn_heads = cast_tuple(local_attn_heads) - print('finish') - self.max_seq_len = max_seq_len - # self.token_emb = nn.Embedding(num_tokens, dim) - self.token_emb = TwoLayerMLP() - print('g2v') - if g2v_position_emb: - self.pos_emb = Gene2VecPositionalEmbedding(dim, max_seq_len, gene2vec_file=gene2vec_file) - self.layer_pos_emb = Always(None) - else: - self.pos_emb = torch.zeros_like - self.layer_pos_emb = Always(None) - print('dropout') - self.dropout = nn.Dropout(emb_dropout) - print('performer') - self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias) - self.norm = nn.LayerNorm(dim) - # self.to_out = nn.Linear(dim, num_tokens) if not tie_embed else None - self.to_out = nn.Linear(dim, 1) if not tie_embed else None - - def check_redraw_projections(self): - self.performer.check_redraw_projections() - - def fix_projection_matrices_(self): - self.performer.fix_projection_matrices_() - - def forward(self, x, return_encodings = False, output_attentions = False, **kwargs): - b, n, device = *x.shape, x.device - assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}' - - # token and positional embedding - x = self.token_emb(x.unsqueeze(dim=-1)) - if output_attentions: - x.requires_grad_() # used for attn_map output - x += self.pos_emb(x) - x = self.dropout(x) - - # performer layers - layer_pos_emb = self.layer_pos_emb(x) - - if output_attentions: - x, attn_weights = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - # norm and to logits - x = self.norm(x) - if return_encodings: - return x, attn_weights - - if exists(self.to_out): - return self.to_out(x), attn_weights - - return (x @ self.token_emb.weight.t()), attn_weights - else: - x = self.performer(x, pos_emb = layer_pos_emb, output_attentions = output_attentions, **kwargs) - - # norm and to logits - x = self.norm(x) - if return_encodings: - return x - - if exists(self.to_out): - x = self.to_out(x) - return x - - return x @ self.token_emb.weight.t() \ No newline at end of file diff --git a/model_dependencies/performer_pytorch_cont/reversible.py b/model_dependencies/performer_pytorch_cont/reversible.py deleted file mode 100644 index 891b300366e151f65a66c3f278958c19825db8b1..0000000000000000000000000000000000000000 --- a/model_dependencies/performer_pytorch_cont/reversible.py +++ /dev/null @@ -1,167 +0,0 @@ -import torch -import torch.nn as nn -from operator import itemgetter -from torch.autograd.function import Function -from torch.utils.checkpoint import get_device_states, set_device_states - -# for routing arguments into the functions of the reversible layer -def route_args(router, args, depth): - routed_args = [(dict(), dict()) for _ in range(depth)] - matched_keys = [key for key in args.keys() if key in router] - - for key in matched_keys: - val = args[key] - for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])): - new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes) - routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args}) - return routed_args - -# following example for saving and setting rng here https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html -class Deterministic(nn.Module): - def __init__(self, net): - super().__init__() - self.net = net - self.cpu_state = None - self.cuda_in_fwd = None - self.gpu_devices = None - self.gpu_states = None - - def record_rng(self, *args): - self.cpu_state = torch.get_rng_state() - if torch.cuda._initialized: - self.cuda_in_fwd = True - self.gpu_devices, self.gpu_states = get_device_states(*args) - - def forward(self, *args, record_rng = False, set_rng = False, **kwargs): - if record_rng: - self.record_rng(*args) - - if not set_rng: - return self.net(*args, **kwargs) - - rng_devices = [] - if self.cuda_in_fwd: - rng_devices = self.gpu_devices - - with torch.random.fork_rng(devices=rng_devices, enabled=True): - torch.set_rng_state(self.cpu_state) - if self.cuda_in_fwd: - set_device_states(self.gpu_devices, self.gpu_states) - return self.net(*args, **kwargs) - -# heavily inspired by https://github.com/RobinBruegger/RevTorch/blob/master/revtorch/revtorch.py -# once multi-GPU is confirmed working, refactor and send PR back to source -class ReversibleBlock(nn.Module): - def __init__(self, f, g): - super().__init__() - self.f = Deterministic(f) - self.g = Deterministic(g) - - def forward(self, x, f_args = {}, g_args = {}): - x1, x2 = torch.chunk(x, 2, dim=2) - y1, y2 = None, None - - with torch.no_grad(): - y1 = x1 + self.f(x2, record_rng=self.training, **f_args) - y2 = x2 + self.g(y1, record_rng=self.training, **g_args) - - return torch.cat([y1, y2], dim=2) - - def backward_pass(self, y, dy, f_args = {}, g_args = {}): - y1, y2 = torch.chunk(y, 2, dim=2) - del y - - dy1, dy2 = torch.chunk(dy, 2, dim=2) - del dy - - with torch.enable_grad(): - y1.requires_grad = True - gy1 = self.g(y1, set_rng=True, **g_args) - torch.autograd.backward(gy1, dy2) - - with torch.no_grad(): - x2 = y2 - gy1 - del y2, gy1 - - dx1 = dy1 + y1.grad - del dy1 - y1.grad = None - - with torch.enable_grad(): - x2.requires_grad = True - fx2 = self.f(x2, set_rng=True, **f_args) - torch.autograd.backward(fx2, dx1, retain_graph=True) - - with torch.no_grad(): - x1 = y1 - fx2 - del y1, fx2 - - dx2 = dy2 + x2.grad - del dy2 - x2.grad = None - - x = torch.cat([x1, x2.detach()], dim=2) - dx = torch.cat([dx1, dx2], dim=2) - - return x, dx - -class _ReversibleFunction(Function): - @staticmethod - def forward(ctx, x, blocks, args): - ctx.args = args - for block, kwarg in zip(blocks, args): - x = block(x, **kwarg) - ctx.y = x.detach() - ctx.blocks = blocks - return x - - @staticmethod - def backward(ctx, dy): - y = ctx.y - args = ctx.args - for block, kwargs in zip(ctx.blocks[::-1], args[::-1]): - y, dy = block.backward_pass(y, dy, **kwargs) - return dy, None, None - -class SequentialSequence(nn.Module): - def __init__(self, layers, args_route = {}): - super().__init__() - assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' - self.layers = layers - self.args_route = args_route - - def forward(self, x, output_attentions = False, **kwargs): - args = route_args(self.args_route, kwargs, len(self.layers)) - layers_and_args = list(zip(self.layers, args)) - - if output_attentions: - attn_weights = [] - for (f, g), (f_args, g_args) in layers_and_args: - if output_attentions: - x = x + f(x, output_attentions = output_attentions, **f_args)[0] - attn_weights.append(f(x, output_attentions = output_attentions, **f_args)[1].unsqueeze(0)) - else: - x = x + f(x, **f_args) - x = x + g(x, **g_args) - if output_attentions: - attn_weights = torch.transpose(torch.cat(attn_weights, dim=0), 0, 1) # the final dim is (batch, layer, head, len, len) - attn_weights = torch.mean(attn_weights, dim=1) # the dim is (batch, head, len, len) - return x, attn_weights - else: - return x - -class ReversibleSequence(nn.Module): - def __init__(self, blocks, args_route = {}): - super().__init__() - self.args_route = args_route - self.blocks = nn.ModuleList([ReversibleBlock(f=f, g=g) for f, g in blocks]) - - def forward(self, x, **kwargs): - x = torch.cat([x, x], dim=-1) - - blocks = self.blocks - args = route_args(self.args_route, kwargs, len(blocks)) - args = list(map(lambda x: {'f_args': x[0], 'g_args': x[1]}, args)) - - out = _ReversibleFunction.apply(x, blocks, args) - return torch.stack(out.chunk(2, dim=-1)).sum(dim=0) \ No newline at end of file diff --git a/model_dependencies/pytorch_cont/__init__.py b/model_dependencies/pytorch_cont/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..056a45b0004f36f92bcd568b106c4a5cd2cae20a --- /dev/null +++ b/model_dependencies/pytorch_cont/__init__.py @@ -0,0 +1,2 @@ +# Export the bundled Performer attention implementation. +from pytorch_cont.pytorch import PerformerLM, Performer, FastAttention, SelfAttention \ No newline at end of file diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/ding_models.py b/model_dependencies/pytorch_cont/ding_models.py similarity index 97% rename from model_dependencies/performer_pytorch_cont/performer_pytorch_cont/ding_models.py rename to model_dependencies/pytorch_cont/ding_models.py index 131015306fc61734b3fd994b0c6067f37fd25fe3..06bf7bdb09ba18739ef27ed4503515b4bfe086e4 100644 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/ding_models.py +++ b/model_dependencies/pytorch_cont/ding_models.py @@ -1,4 +1,4 @@ -from performer_pytorch_cont.performer_pytorch import * +from pytorch_cont.pytorch import * import torch.nn as nn from torch_geometric.nn import SGConv import pandas as pd diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch.py b/model_dependencies/pytorch_cont/pytorch.py similarity index 97% rename from model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch.py rename to model_dependencies/pytorch_cont/pytorch.py index 6e71bf30fc28f0ed522fe1e0e5f9658e2ec74d07..050f37b49372bbf8f187372bf63af0c141347967 100644 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch.py +++ b/model_dependencies/pytorch_cont/pytorch.py @@ -11,7 +11,7 @@ from functools import partial from contextlib import contextmanager from local_attention import LocalAttention -from performer_pytorch_cont.reversible import ReversibleSequence, SequentialSequence +from pytorch_cont.reversible import ReversibleSequence, SequentialSequence try: from apex import amp diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch_old.py b/model_dependencies/pytorch_cont/pytorch_old.py similarity index 97% rename from model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch_old.py rename to model_dependencies/pytorch_cont/pytorch_old.py index 6b0ed3a9171aaff2a94b7a2331560461f043c613..9673f6378fbb9b6c75f7675f8578de39820aa61d 100644 --- a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/performer_pytorch_old.py +++ b/model_dependencies/pytorch_cont/pytorch_old.py @@ -11,7 +11,7 @@ from functools import partial from contextlib import contextmanager from local_attention import LocalAttention -from performer_pytorch_cont.reversible import ReversibleSequence, SequentialSequence +from pytorch_cont.reversible import ReversibleSequence, SequentialSequence try: from apex import amp diff --git a/model_dependencies/performer_pytorch_cont/performer_pytorch_cont/reversible.py b/model_dependencies/pytorch_cont/reversible.py similarity index 100% rename from model_dependencies/performer_pytorch_cont/performer_pytorch_cont/reversible.py rename to model_dependencies/pytorch_cont/reversible.py diff --git a/weight/0810model/mae_tost_70M_75mask_ec2.pkl b/weight/BioMod.pkl similarity index 100% rename from weight/0810model/mae_tost_70M_75mask_ec2.pkl rename to weight/BioMod.pkl diff --git a/weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth b/weight/BioMod.pth similarity index 100% rename from weight/0810model/mae_tost_70M_75mask_ec2_epoch0.pth rename to weight/BioMod.pth