多组DNA甲基化数据的非参数贝叶斯差异分析
Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data
AI总结：
针对多组DNA甲基化数据的差异分析需求，提出基于粘性皮特曼-约过程的非参数贝叶斯方法BayesDiff，可自适应处理序列依赖并同时推断差异特征，经模拟和胃肠道癌症数据集验证有效。
摘要：
癌症研究中的DNA甲基化数据集由大量被称为胞嘧啶-磷酸-鸟嘌呤（CpG）位点的基因组位置的测量值构成，这些位点具有复杂的相关结构。这类研究的一个基本目标是开发统计技术，以识别由不同实验或生物学条件定义的多个患者组的疾病基因组特征。我们提出了BayesDiff，这是一种用于差异分析的非参数贝叶斯方法，它依赖于一类被称为粘性皮特曼-约过程（Sticky Pitman-Yor process）或双餐厅双菜系特许经营（2R2CF）的新型一阶混合模型。BayesDiff方法灵活利用所有CpG位点或探针的信息，自适应地适应由探针间距离差异很大导致的任何序列依赖性，并同时推断患者组的差异基因组特征。通过模拟研究，我们证明了BayesDiff方法相对于现有的DNA甲基化差异分析统计技术的有效性。该方法被应用于分析一个同时表现出序列相关性和相互作用模式的胃肠道（GI）癌症数据集。研究结果支持并补充了上胃肠道癌症中DNA甲基化与基因关联的已知方面。
英文摘要：
DNA methylation datasets in cancer studies are comprised of measurements on a large number of genomic locations called cytosine-phosphate-guanine (CpG) sites with complex correlation structures. A fundamental goal of these studies is the development of statistical techniques that can identify disease genomic signatures across multiple patient groups defined by different experimental or biological conditions. We propose BayesDiff, a nonparametric Bayesian approach for differential analysis relying on a novel class of first order mixture models called the Sticky Pitman-Yor process or two-restaurant two-cuisine franchise (2R2CF). The BayesDiff methodology flexibly utilizes information from all CpG sites or probes, adaptively accommodates any serial dependence due to the widely varying inter-probe distances and performs simultaneous inferences about the differential genomic signature of the patient groups. Using simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing statistical techniques for differential DNA methylation. The methodology is applied to analyze a gastrointestinal (GI) cancer dataset that displays both serial correlations and interaction patterns. The results support and complement known aspects of DNA methylation and gene association in upper GI cancers.
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