ArticleGenome biology2024
EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- ScSpTITH: a rank-correlation framework for robust quantification of multi-dimensional tumor heterogeneity.Human genetics · 2026Article
- Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis.bioRxiv : the preprint server for biology · 2026Article
- Latent plasticity of the human pancreas across development, health, and disease.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- DNA Methylation, Aging, and Cancer.Epigenomes · 2025Review
- EpiCHAOS: a metric to quantify epigenomic heterogeneity in single-cell data.Genome biology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epigenetic heterogeneity is a fundamental property of biological systems and is recognized as a potential driver of tumor plasticity and therapy resistance. Single-cell epigenomics technologies have been widely employed to study epigenetic variation between-but not within-cellular clusters. We introduce epiCHAOS: a quantitative metric of cell-to-cell heterogeneity, applicable to any single-cell epigenomics data type. After validation in synthetic datasets, we apply epiCHAOS to investigate global and region-specific patterns of epigenetic heterogeneity across diverse biological systems. EpiCHAOS provides an excellent approximation of stemness and plasticity in development and malignancy, making it a valuable addition to single-cell cancer epigenomics analyses.
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