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.

翻译：识别差异甲基化区域是表观基因组关联研究中的一项重要任务，其中差异信号通常出现在相邻CpG位点组之间。许多现有方法通过聚合CpG水平的检验结果来检测差异甲基化区域，这可能限制其捕捉复杂区域甲基化模式的能力。本文介绍了R软件包mmcmcBayes，它实现了一种多阶段马尔可夫链蒙特卡洛程序，用于区域水平的差异甲基化区域检测。该方法使用α-偏斜广义正态分布对样本水平的区域甲基化摘要进行建模，并通过贝叶斯因子评估组间差异甲基化的证据。我们采用多阶段区域分割策略，基于统计证据对候选区域进行细化。我们描述了其底层方法论与软件实现，并通过模拟研究及在Illumina 450K甲基化数据上的应用展示了其性能。mmcmcBayes软件包为现有基于CpG的差异甲基化区域检测方法提供了一个实用的区域水平替代方案，并包含了用于汇总、比较和可视化检测区域的辅助函数。