Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data.

ABSTRACT: Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets,

SUBMITTER: Luo Q 

PROVIDER: S-EPMC9791356 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature