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A statistical approach for systematic identification of transition cells from scRNA-seq data
Yuanxin Wang1, Merve Dede1, Vakul Mohanty1
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Cell Reports Methods
|December 7, 2024
Summary
We developed CellTran, a new method using gene expression correlations to find cells transitioning between states. This approach helps understand development and disease by identifying key transition cells and their molecular drivers.
Area of Science:
- Molecular Biology
- Genomics
- Computational Biology
Background:
- Understanding cellular state transitions is key for biology and disease research.
- Single-cell RNA sequencing (scRNA-seq) provides insights, but current tools focus on expression, not regulatory shifts.
Purpose of the Study:
- To present CellTran, a novel statistical method for detecting transition cells from scRNA-seq data.
- To identify cells undergoing state changes without needing to resolve gene regulatory networks.
Main Methods:
- CellTran utilizes paired-gene expression correlations to identify transition cells.
- The method was applied to diverse biological contexts: tissue regeneration, embryonic development, preinvasive lesions, and immune responses.
Main Results:
- CellTran successfully identified transition cells across various biological scenarios.
- Distinct gene expression profiles of these transition cells were revealed.
Conclusions:
- CellTran offers a powerful approach to study cellular state transitions using scRNA-seq.
- The findings enhance understanding of molecular mechanisms driving these transitions and aid in identifying therapeutic targets.
