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 driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.

Keywords: CP: developmental biology; CP: systems biology; carcinogenesis; cell development; cell differentiation; cell transitions; differential equations; dynamic systems; gene expression correlation; gene regulatory network; single-cell RNA sequencing; statistical analysis.