ArticleBioinformatics (Oxford, England)2015
M3D: a kernel-based test for spatially correlated changes in methylation profiles.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 37 citations in OpenAlex.
- Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.Epigenetics · 2026Article
- Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals.Genetic epidemiology · 2025Article
- vmrseq: probabilistic modeling of single-cell methylation heterogeneity.Genome biology · 2024Article
- Shedding light on DNA methylation and its clinical implications: the impact of long-read-based nanopore technology.Epigenetics & chromatin · 2024Review
- Testing differentially methylated regions through functional principal component analysis.Journal of applied statistics · 2022Article
- Variant-set association test for generalized linear mixed model.Genetic epidemiology · 2021Article
- LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation.Bioinformatics (Oxford, England) · 2020Article
- Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing.Biostatistics (Oxford, England) · 2019Article
- Melissa: Bayesian clustering and imputation of single-cell methylomes.Genome biology · 2019Article
- dStruct: identifying differentially reactive regions from RNA structurome profiling data.Genome biology · 2019Article
- DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts.Nucleic acids research · 2018Article
- BPRMeth: a flexible Bioconductor package for modelling methylation profiles.Bioinformatics (Oxford, England) · 2018Article
- Integrating Epigenomics into the Understanding of Biomedical Insight.Bioinformatics and biology insights · 2016Review
- methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data.BMC bioinformatics · 2015Article
- Computational epigenomics: challenges and opportunities.Frontiers in genetics · 2015Article
- Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform.BioData mining · 2015Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
motivationDNA methylation is an intensely studied epigenetic mark implicated in many biological processes of direct clinical relevance. Although sequencing-based technologies are increasingly allowing high-resolution measurements of DNA methylation, statistical modelling of such data is still challenging. In particular, statistical identification of differentially methylated regions across different conditions poses unresolved challenges in accounting for spatial correlations within the statistical testing procedure.
resultsWe propose a non-parametric, kernel-based method, M(3)D, to detect higher order changes in methylation profiles, such as shape, across pre-defined regions. The test statistic explicitly accounts for differences in coverage levels between samples, thus handling in a principled way a major confounder in the analysis of methylation data. Empirical tests on real and simulated datasets show an increased power compared to established methods, as well as considerable robustness with respect to coverage and replication levels.
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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.