Dissecting and improving gene regulatory network inference using single-cell transcriptome data.

ABSTRACT: Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature

SUBMITTER: Xue L 

PROVIDER: S-EPMC10620053 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature