ScGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.

ABSTRACT: Gene regulatory networks (GRNs) involve complex and multi-layer regulatory interactions between regulators and their target genes. Precise knowledge of GRNs is important in understanding cellular processes and molecular functions. Recent breakthroughs in single-cell sequencing technology made it possible to infer GRNs at single-cell level. Existing methods, however, are limited by expensive computations, and sometimes simplistic assumptions. To overcome these obstacles, we propose scGREAT, a framework to infer GRN using gene embeddings and transformer from single-cell transcriptomics. scGREAT starts by constructing gene expression and gene biotext dictionaries from scRNA-seq data and gene text information. The representation of TF gene pairs is learned through optimizing embedding space by

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10951644 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature