cell-gears 0.0.4

GEARS

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 results in our paper and (2) train GEARS on your perturbation dataset 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(data_name = '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([['CBL', 'CNN1'], ['FEV']])
gears_model.GI_predict(['CBL', 'CNN1'], GI_genes_file=None)

To use your own dataset, create a scanpy adata object 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

Name	Description
Dataset Tutorial	Tutorial on how to use the dataset loader and read customized data
Model Tutorial	Tutorial on how to train GEARS
Plot top 20 DE genes	Tutorial on how to plot the top 20 DE genes
Uncertainty	Tutorial on how to train an uncertainty-aware GEARS model

Colab

Name	Description
Using Trained Model	Use a model trained on Norman et al. 2019 to make predictions (Needs Colab Pro)

Cite Us

@article {Roohani2022.07.12.499735,
	author = {Roohani, Yusuf and Huang, Kexin and Leskovec, Jure},
	title = {GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations},
	year = {2022},
	doi = {10.1101/2022.07.12.499735},
	publisher = {Cold Spring Harbor Laboratory},
	journal = {bioRxiv}
}

Preprint: Link

Code for reproducing figures: Link