A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities.

ABSTRACT: Advances in single-cell RNA-sequencing techniques reveal the existence of distinct cell subpopulations. Identification of transcription factors (TFs) that define the identity of these subpopulations poses a challenge. Here, we postulate that identity depends on background subpopulations, and is determined by a synergistic core combination of TFs mainly uniquely expressed in each subpopulation, but also TFs more broadly expressed across background subpopulations. Building on this view, we develop a new computational method for determining such synergistic identity cores of subpopulations within a given cell population. Our method utilizes an information-theoretic measure for quantifying transcriptional synergy, and implements a novel algorithm for searching for optimal synergistic cores. It

SUBMITTER: Okawa S 

PROVIDER: S-EPMC6468312 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature