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DNA methylation datasets in cancer studies are comprised of measurements on alarge number of genomic locations called cytosine-phosphate-guanine (CpG) siteswith complex correlation structures. A fundamental goal of these studies is thedevelopment of statistical techniques that can identify disease genomicsignatures across multiple patient groups defined by different experimental orbiological conditions. We propose BayesDiff, a nonparametric Bayesian approachfor differential analysis relying on a novel class of first order mixturemodels called the Sticky Pitman-Yor process or two-restaurant two-cuisinefranchise (2R2CF). The BayesDiff methodology flexibly utilizes information fromall CpG sites or probes, adaptively accommodates any serial dependence due tothe widely varying inter-probe distances and performs simultaneous inferencesabout the differential genomic signature of the patient groups. Usingsimulation studies, we demonstrate the effectiveness of the BayesDiff procedurerelative to existing statistical techniques for differential DNA methylation.The methodology is applied to analyze a gastrointestinal (GI) cancer datasetthat displays both serial correlations and interaction patterns. The resultssupport and complement known aspects of DNA methylation and gene association inupper GI cancers.

Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data