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Activity Number: 234 - SBSS Student Travel Award Session 2
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328679 Presentation
Title: Bayesian Nonparametric Differential Analysis with Application to Colorectal Cancer DNA Methylation
Author(s): Chiyu Gu* and Subharup Guha and Veera Baladandayuthapani and Jeffrey S Morris
Companies: University of Missouri and University of Florida and UT MD Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center
Keywords: Genomic signature; First order models; Mixture models; Sticky Poisson-Dirichlet process; Multicuisine restaurant franchise

Cancer 'omics datasets involve widely varying sizes and scales, measurement variables, and correlation structures. An overarching scientific goal in cancer research is the invention of general statistical techniques that can cleanly sift the signal from the noise in identifying genomic signatures of the disease 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 the motivating colorectal cancer DNA methylation dataset. In simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing techniques for differential DNA methylation. Returning to the motivating dataset, we detect the genomic signature for four subtypes of colorectal cancer.

Authors who are presenting talks have a * after their name.

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