STGRNS

Introduction

STGRNS, a Transformer-based model, provides a fast and accurate tool to infer gene regulatory networks from a single-cell RNA-seq profile. By leveraging the newly designed neural network structure, STGRNS especially obtains an outperformance on GRN inference.

Instructions and examples are provided in the following tutorials.

Requirement

• scikit-learn (Compatible with all versions)
• Pytorch (With Cudatoolkit is recommanded)
• Numpy > 1.20
• Scanpy > 1.9.1

Data Availability

For the GRN reconstruction task, the processed experimental single-cell gene expression datasets are available on Zenodo at https://doi.org/10.5281/zenodo.3378975.
The gene expression and ChIP-Seq data of bone marrow-derived macrophages, dendritic cells, and mESC(1) are available at https://github.com/xiaoyeye/CNNC.

Tutorial

• Inferring gene regulatory networks

Paper Link

https://academic.oup.com/bioinformatics/article/39/4/btad165/7099621