A statistical approach for systematic identification of transition cells from scRNA-seq data
Year: 2024
Type: article
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.
Source: Cell Reports Methods
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,200
OpenAlex ID: W4405108207