GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations

This repository hosts the official implementation of GEARS, a method that can predict transcriptional response to both single and multi-gene perturbations using single-cell RNA-sequencing data from perturbational screens.

Installation

Install PyG, and then do pip install cell-gears.

Core API Interface

Using the API, you can (1) reproduce the result in our paper and (2) train your own GEARS model on your perturbation screen using a few lines of code.

from gears import PertData, GEARS

# get data
pert_data = PertData('./data')
# load dataset in paper: norman, adamson, dixit.
pert_data.load(dataset = 'norman')
# specify data split
pert_data.prepare_split(split = 'simulation', seed = 1)
# get dataloader with batch size
pert_data.get_dataloader(batch_size = 32, test_batch_size = 128)

# set up and train a model
gears_model = GEARS(pert_data, device = 'cuda:8')
gears_model.model_initialize(hidden_size = 64)
gears_model.train(epochs = 20)

# save/load model
gears_model.save_model('gears')
gears_model.load_pretrained('gears')

# predict
gears_model.predict([['FOX1A', 'AHR'], ['FEV']])
gears_model.GI_predict([['FOX1A', 'AHR'], ['FEV', 'AHR']])

To use your own dataset, create a scanpy adata variable with a gene_name column in adata.var, and two columns condition, cell_type in adata.obs. Then run:

pert_data.new_data_process(dataset_name = 'XXX', adata = adata)
# to load the processed data
pert_data.load(data_path = './data/XXX')

Demos

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