license: other
license_name: cc-by-4.0-and-cc-by-nc-4.0
license_link: LICENSE.md
tags:
- biology
- single-cell
- perturbation
GENESIS
GENESIS (Generative ENErgy-based Single-cell Intervention Simulator) generates the single-cell RNA profiles of a perturbation in a biological context it was never measured in. These are the trained models behind the paper Single-cell perturbation prediction near experimental reproducibility with GENESIS. The code is at github.com/rickconci/GENESIS, and the published scores are at RConci/GENESIS_results.
Start here: two models
| Folder | Data | Predicts |
|---|---|---|
tahoe/ |
Tahoe-100M: 50 cancer cell lines × drugs | a drug's effect in 5 cell lines held out of training |
pbmc/ |
Parse PBMC cytokine atlas: 12 donors × 90 cytokines | a cytokine's effect in 4 donors held out of training |
Downloads are gated: request access on this page, then log in once with hf auth login (a token from
huggingface.co/settings/tokens). Inside a clone of the code
(uv sync --extra model), download one model and generate cells for its test conditions:
uvx --from huggingface_hub hf download RConci/GENESIS --include 'tahoe/*' --exclude '*/split_cache/*' --local-dir models
uv run python scripts/eval/generate.py conditions --run-dir "$PWD/models/tahoe" --out-dir results/generated/tahoe --limit 10
Each condition is written as one .npz file holding predictions (cells x 2,000 genes, log1p), gene_names and the
condition's values. This needs a CUDA GPU and no dataset.
Reproduce the paper's figures: reproduce/
The folders are arranged by the figure that first uses them, as in the code's scripts/figures/figureN/. Download
everything with hf download RConci/GENESIS --local-dir models; each figure's score.sh then finds its models by
these names.
| Folder | Figure | Trained on | Seeds |
|---|---|---|---|
tahoe/, pbmc/ |
2 | all training conditions (5 epochs) | 0 |
reproduce/figure2/tahoe_perturbation_split/ |
2 (ED) | Tahoe: a held-out drug is seen in 1-20 other lines; test = 40 drugs × 30 lines × 3 doses | 0 |
reproduce/figure2/pbmc_perturbation_split/ |
2 (ED) | PBMC: a held-out cytokine is seen in 1-5 other donors; test = 30 cytokines × 7 donors | 0 |
reproduce/figure3/scaling/full_seed{1,2}/ |
3a | as tahoe/, with other model seeds; seed 0 of this point is tahoe/ itself |
1, 2 |
reproduce/figure3/scaling/conditions{25,5}_seed{0,1,2}/ |
3a | 25% or 5% of the training conditions (11 or 25 epochs) | 0, 1, 2 |
reproduce/figure3/scaling/cells{25,5}_seed{0,1,2}/ |
3a | every condition, 25% or 5% of its perturbed cells, every control (11 or 25 epochs) | 0, 1, 2 |
reproduce/figure3/overlap/row{0-4}_seed{0,1,2}/ |
3b | 5 held-out lines, each seeing 0, 15, 30, 80 or 164 drugs in training (one Latin-square row per folder; 15 epochs) | 0, 1, 2 |
reproduce/figure4/progeny_5pct_cells/ |
4 | as cells5, on 3,072 genes: the 2,000 plus the PROGENy pathway genes, padded with 6 placeholders |
0 |
reproduce/figure4/hallmark_15pct_conditions/ |
4 (ED) | 15% of the training conditions, on 5,750 genes: the 2,000 plus the MSigDB Hallmark genes | 0 |
Every Tahoe model above, except the perturbation split, is tested on the same 5 held-out cell lines as tahoe/. In
scaling/, seed n draws the training subset and initialises the model. In overlap/, it draws which drugs each line
sees, and every model starts from the same initialisation.
What a model folder holds
| File | What it is |
|---|---|
checkpoints/last.ckpt |
the PyTorch Lightning checkpoint: weights, optimizer state, the full training config |
config_resolved.yaml |
every training setting, as the run used it |
encoder_idx_to_name.json |
the cell lines, drugs, doses, plates, donors or cytokines the model can condition on |
output_gene_names.txt |
the genes it predicts, in output order |
eval/eval_manifest.json, eval/condition_summary.parquet |
the test split and the cells measured per condition |
split_cache/train_idx.npy |
the dataset rows it trained on (only needed to recount training data) |
run.log |
the training log |
hbss_meta.json, hbss_selected_triplets.parquet |
for subsampled runs: which training conditions were kept |
Licence
Each model carries the licence of the data it was trained on (LICENSE.md):
| Folders | Trained on | Licence |
|---|---|---|
every folder with pbmc in its path (pbmc/, reproduce/figure2/pbmc_perturbation_split/) |
the Parse PBMC cytokine atlas, CC BY-NC 4.0 | CC BY-NC 4.0: non-commercial use, with credit |
| every other folder | Tahoe-100M, CC0 1.0 | CC BY 4.0: any use, with credit |
Credit means citing the GENESIS paper, plus the dataset a model was trained on. The code is MIT-licensed.
Data
- Zhang J. et al. Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling. bioRxiv (2025). doi:10.1101/2025.02.20.639398
- Parse Biosciences. 10 Million Human PBMCs in a Single Experiment. parsebiosciences.com/datasets/10-million-human-pbmcs-in-a-single-experiment