A statistical approach for systematic identification of transition cells from scRNA-seq data.

ABSTRACT: Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms drivi

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11704623 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature