| --- |
| license: mit |
| library_name: expert-model |
| pipeline_tag: other |
| tags: |
| - single-cell |
| - perturb-seq |
| - scRNA-seq |
| - perturbation-prediction |
| - biology |
| --- |
| |
| # ExPert v0.1.0 |
|
|
| ExPert reads a transcriptional response and tells you which genetic perturbations it resembles. This is the |
| production model from the paper. It was trained on 22 Perturb-seq datasets and has one prototype per perturbation in |
| a shared latent space: 8,964 learned from the screens, the rest of its 19,895-gene vocabulary predicted from prior |
| knowledge. It scores single cells or pseudobulks from any human or mouse scRNA-seq query against those prototypes. |
|
|
| Code, installation and documentation: <https://github.com/GoyalLab/ExPert> |
|
|
| ## Usage |
|
|
| ```bash |
| pip install git+https://github.com/GoyalLab/ExPert.git huggingface_hub |
| ``` |
|
|
| ```python |
| import scanpy as sc |
| from huggingface_hub import snapshot_download |
| from expert import ExPert |
| |
| model = ExPert.load(snapshot_download("GoyalLab/ExPert", revision="v0.1.0")) |
| |
| query = sc.read_h5ad("query.h5ad") # raw counts, gene symbols; obs['perturbation'] = 'control' for controls |
| out = model.predict(query) |
| out["soft_predictions"] # cells x perturbations |
| ``` |
|
|
| ## Files |
|
|
| | file | contents | |
| |---|---| |
| | `model.pt` | weights, model configuration, gene and perturbation vocabularies, readout calibration | |
| | `gene_embeddings.npz` | text embeddings of the perturbation vocabulary (prototype prior for untrained genes) | |
|
|
| The training data can be rebuilt with [ExPert-data](https://github.com/GoyalLab/ExPert-data). |
|
|
| ## Citation |
|
|
| _Preprint reference to be added._ |
|
|
