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.