mmcmcBayes:An R Package Implementing a Multistage MCMC Framework for Detecting the Differentially Methylated Regions

TL;DR

The paper introduces mmcmcBayes, an R package that uses a multistage MCMC approach with Bayesian modeling to detect differentially methylated regions at the regional level, improving over CpG-based methods.

Contribution

It presents a novel multistage MCMC framework with Bayesian modeling for regional methylation analysis, implemented in an accessible R package with practical tools.

Findings

Demonstrates effective detection of DMRs in simulated data

Shows improved regional detection in Illumina 450K methylation data

Provides a user-friendly R package with visualization tools

Abstract

Identifying differentially methylated regions is an important task in epigenome-wide association studies, where differential signals often arise across groups of neighboring CpG sites. Many existing methods detect differentially methylated regions by aggregating CpG-level test results, which may limit their ability to capture complex regional methylation patterns. In this paper, we introduce the R package mmcmcBayes, which implements a multistage Markov chain Monte Carlo procedure for region-level detection of differentially methylated regions. The method models sample-wise regional methylation summaries using the alpha-skew generalized normal distribution and evaluates evidence for differential methylation between groups through Bayes factors. We use a multistage region-splitting strategy to refine candidate regions based on statistical evidence. We describe the underlying methodology and software implementation, and illustrate its performance through simulation studies and applications to Illumina 450K methylation data. The mmcmcBayes package provides a practical region-level alternative to existing CpG-based differentially methylated regions detection methods and includes supporting functions for summarizing, comparing, and visualizing detected regions.