Nonparametric Bayes Differential Analysis for Dependent Multigroup Data with Application to DNA Methylation Analyses in Cancer

Abstract

Modern cancer genomics datasets involve widely varying sizes and scales, measurement variables, and correlation structures. A fundamental analytical goal in these high-throughput studies is the development of general statistical techniques that can cleanly sift the signal from noise in identifying disease-specific genomic signatures across a set of experimental or biological conditions. We propose BayesDiff, a nonparametric Bayesian approach based on a novel class of first order mixture models, called the Sticky Poisson-Dirichlet process or multicuisine restaurant franchise. The BayesDiff methodology flexibly utilizes information from all the measurements and adaptively accommodates any serial dependence in the data, accounting for the inter-probe distances, to perform simultaneous inferences on the variables. The technique is applied to analyze a DNA methylation gastrointestinal (GI) cancer dataset, which displays both serial correlations and complex interaction patterns. Our analyses and results both support and complement known aspects of DNA methylation and gene association in upper GI cancers. In simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing techniques for differential DNA methylation.