| --- |
| license: mit |
| library_name: expert-model |
| pipeline_tag: other |
| tags: |
| - single-cell |
| - perturb-seq |
| - scRNA-seq |
| - perturbation-prediction |
| - biology |
| --- |
| |
| # ExPert paper checkpoints |
|
|
| The additional checkpoints behind the ExPert paper's benchmark and ablation figures. The production model that the |
| paper's application figures use, and that you should use for your own data, is |
| [GoyalLab/ExPert](https://huggingface.co/GoyalLab/ExPert). |
|
|
| Code and the scripts that use these checkpoints: <https://github.com/GoyalLab/ExPert> (`paper/`) |
|
|
| | folder | what | used in | |
| |---|---|---| |
| | `fold0` … `fold11` | the 12 cross-validation models: each holds out one context (cell line / donor / condition) from training | benchmark and out-of-distribution figures | |
| | `EFFGATE3` | the ablation arm with the gated prior pull | prior-pull ablation figures | |
| | `banks/v0.1.0_fullbank_oldfill.npz` | v0.1.0 prototypes with the earlier fill for genes never perturbed in training (trained rows identical to the model's) | ALD and PMD readouts | |
|
|
| Each checkpoint folder holds `model.pt` and `gene_embeddings.npz` and loads like the production model: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from expert import ExPert |
| |
| path = snapshot_download("GoyalLab/ExPert-paper-models", revision="v0.1.0", allow_patterns="fold4/*") |
| model = ExPert.load(f"{path}/fold4") |
| ``` |
|
|
| The bank is a NumPy archive: `P` (perturbations × latent dims), `labels`, `trained`. |
|
|
| ## Citation |
|
|
| _Preprint reference to be added._ |
|
|
