ArticleCell reports methods2024
A statistical approach for systematic identification of transition cells from scRNA-seq data.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- scTrimClust: a fast approach to robust scRNA-seq analysis using trimmed cell clusters.Bioinformatics advances · 2026Article
- Understanding the immune microenvironment of ovarian cancer.Frontiers in oncology · 2026Review
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
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Authors and funding
6 authors.
Funding
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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.
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