ArticleGenome biology2024
vmrseq: probabilistic modeling of single-cell methylation heterogeneity.
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 4 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.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework.Nature communications · 2026Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- vmrseq: probabilistic modeling of single-cell methylation heterogeneity.Genome biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies. vmrseq also highlights context-dependent correlations between methylation and gene expression, supporting previous findings and facilitating novel hypotheses on epigenetic regulation. vmrseq is available at https://github.com/nshen7/vmrseq .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.