Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.

ABSTRACT: Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with system

SUBMITTER: Ramirez DA 

PROVIDER: S-EPMC11384016 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature