| --- |
| license: cc-by-4.0 |
| language: en |
| tags: [biology, single-cell, transcriptomics, coexpression, baseline] |
| --- |
| |
| # ConvergeCELL Coexpression Baselines — v0.1.0 |
|
|
| Per-tissue gene–gene correlation matrices trained on the |
| [ConvergeCELL Pseudobulk Panel](https://huggingface.co/datasets/nicolas-lynn/vcell-perturbation-panel) |
| (panel size pb20, restricted to 8,000 HVGs). |
|
|
| Each baseline is a fitted :class:`CoexpressionBaselineModel` — predicts gene |
| expression via ``Y = (X − μ)/σ @ C @ σ + μ``. Useful as the **co-expression |
| floor** any foundation model must beat to claim it has learned anything |
| beyond gene–gene correlation structure. |
|
|
| ## Baselines (12) |
|
|
| | Tissue | File | # training samples | Size | |
| |---|---|---:|---:| |
| | `skin` | `coexpr_tissue_skin.npz` | 965 | 209 MB | |
| | `lung` | `coexpr_tissue_lung.npz` | 575 | 130 MB | |
| | `bone_marrow` | `coexpr_tissue_bone_marrow.npz` | 470 | 116 MB | |
| | `kidney` | `coexpr_tissue_kidney.npz` | 440 | 95 MB | |
| | `eye` | `coexpr_tissue_eye.npz` | 400 | 117 MB | |
| | `liver` | `coexpr_tissue_liver.npz` | 385 | 104 MB | |
| | `spleen` | `coexpr_tissue_spleen.npz` | 370 | 117 MB | |
| | `blood` | `coexpr_tissue_blood.npz` | 330 | 72 MB | |
| | `heart` | `coexpr_tissue_heart.npz` | 280 | 93 MB | |
| | `islet` | `coexpr_tissue_islet.npz` | 195 | 92 MB | |
| | `fat` | `coexpr_tissue_fat.npz` | 175 | 131 MB | |
| | `bladder` | `coexpr_tissue_bladder.npz` | 25 | 45 MB | |
|
|
| ## How to load |
|
|
| ```python |
| import virtual_cell.perturbation as isp |
| adata = ... # your input AnnData |
| model = isp.load_model("coexpr-baseline-T_cell", adata=adata) # downloads from HF + wraps |
| preds = model.get_expression_predictions(adata) |
| ``` |
|
|
| Direct download: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import numpy as np |
| |
| path = hf_hub_download(repo_id="nicolas-lynn/vcell-coexpr-baselines", |
| filename="coexpr_tissue_T_cell.npz", |
| repo_type="dataset") |
| art = np.load(path, allow_pickle=True) |
| C, gene_names = art["C"], art["gene_names"].tolist() |
| ``` |
|
|
| ## Provenance |
|
|
| - **Trained on**: HuggingFace dataset `nicolas-lynn/vcell-perturbation-panel` |
| panel size `pb20`, panel version v1.0.0 |
| - **HVG selection**: top 8,000 variable genes via `scanpy.pp.highly_variable_genes` |
| (`flavor='seurat_v3'`) on the full panel before group splitting |
| - **Min samples per group**: 20 |
|
|
| ## License |
|
|
| CC BY 4.0. |
|
|
