mmcmcBayes：一个实现多阶段MCMC框架的R包，用于检测差异甲基化区域
mmcmcBayes:An R Package Implementing a Multistage MCMC Framework for Detecting the Differentially Methylated Regions
AI总结：
mmcmcBayes通过多阶段MCMC方法实现区域级别的差异甲基化区域检测，提供了一种基于统计证据的替代方案。
摘要：
识别差异甲基化区域是表观组学广泛关联研究中的重要任务，其中差异信号往往出现在邻近CpG位点的群体中。许多现有方法通过汇总CpG水平测试结果来检测差异甲基化区域，这可能限制了它们捕捉复杂区域甲基化模式的能力。在本文中，我们介绍了R包mmcmcBayes，它实现了一个多阶段马尔可夫链蒙特卡罗过程，用于区域级别的差异甲基化区域检测。该方法使用alpha-skew广义正态分布对样本级别的区域甲基化汇总进行建模，并通过贝叶斯因子评估组间差异甲基化证据。我们使用多阶段区域分割策略来根据统计证据细化候选区域。我们描述了底层方法和软件实现，并通过模拟研究和Illumina 450K甲基化数据的应用来说明其性能。mmcmcBayes包提供了一种实用的区域级别替代方案，用于现有的基于CpG的差异甲基化区域检测方法，并包含用于总结、比较和可视化检测区域的支持函数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
