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

2026-02-04

AI Summary

This study addresses the limitation of existing differentially methylated region (DMR) detection methods, which predominantly rely on aggregation at individual CpG sites and thus struggle to capture complex methylation patterns within genomic regions. To overcome this, the authors propose a region-based Bayesian framework that models regional methylation profiles using the alpha-skew generalized normal distribution. The approach incorporates a multi-stage Markov chain Monte Carlo (MCMC) algorithm for adaptive region segmentation and employs Bayes factors to quantify evidence of differential methylation between groups. Evaluated on both simulated data and real Illumina 450K arrays, the method consistently outperforms conventional CpG-aggregation strategies, substantially improving the accuracy of detecting intricate methylation patterns. An accompanying R package is provided to support end-to-end analysis and visualization.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Kernel Methods
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• differentially methylated regions
• epigenome-wide association studies
• CpG sites
• regional methylation patterns

Innovation

• multistage MCMC
• differentially methylated regions
• Bayes factors
• alpha-skew generalized normal distribution
• region-level modeling