metadata
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
pip install git+https://github.com/GoyalLab/ExPert.git huggingface_hub
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.
Citation
Preprint reference to be added.