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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._
|