ExPert v0.1.0

README.md

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+---
+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._

gene_embeddings.npz

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model.pt

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