vmrseq: probabilistic modeling of single-cell methylation heterogeneity

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 .

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2024-12-30
DOI
https://doi.org/10.1186/s13059-024-03457-7
Citations
2
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
0.47

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

vmrseq: probabilistic modeling of single-cell methylation heterogeneity

Keegan Korthauer, Ning Shen
2 citations
Genome biology
Epigenetics and DNA Methylation
0.47
article

vmrseq: probabilistic modeling of single-cell methylation heterogeneity

Keegan Korthauer, Ning Shen
article en
2 citations

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 .

Genome biologyVol. 25(1)
University of British Columbia (CA), BC Children's Hospital (CA)
BC Children’s Hospital Foundation, BC Children's Hospital, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 33%
Epigenetics and DNA Methylation
0.47
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

vmrseq: probabilistic modeling of single-cell methylation heterogeneity — Keegan Korthauer, Ning Shen · Genome biology (2024) | TGRS Research Map | TGRS