A VAE for analyzing pathways, transcription factors, cell types in single-cell RNA-seq data
Introduction
VEGA is a VAE aimed at analyzing a priori specified latent variables such as pathways. VEGA is implemented with pytorch, and using the scanpy and scvi-tools ecosystem.
Getting started
VEGA needs 2 things to analyze your data:
- A single-cell dataset wrapped using the Scanpy package
- A GMT file specifying the gene module variables (GMVs) and gene membership, eg. from MSigDB
Installation
With pip
With pip, you can run
pip install scvega
Usage
In python, use
import vega
Documentation and issues
- A documentation is available with API reference, installation guide and tutorials.
- Please consider submitting an issue on github if you encounter a bug.
Reproducing paper results
VEGA manuscript results can be reproduced using the following code. Check tags for appropriate version of VEGA for reproducing results.
Reference
If VEGA is useful to your research, please consider citing our Nature Communications article.
Seninge, L., Anastopoulos, I., Ding, H. et al. VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nat Commun 12, 5684 (2021). https://doi.org/10.1038/s41467-021-26017-0
