STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.

ABSTRACT:

Motivation

Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.

Results

To address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for

SUBMITTER: Xu J 

PROVIDER: S-EPMC10085635 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature