Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics.

ABSTRACT: The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible

SUBMITTER: Morin A 

PROVIDER: S-EPMC10979919 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature