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Activity Number: 347
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304544
Title: A Bayesian Model-Averaging Approach for Observational High-Throughput Data
Author(s): Xi Kathy Zhou*+ and Fei Liu and Andrew J Dannenberg
Companies: Weill Cornell Medical College and IBM T. J. Watson Research Center and Weill Cornell Medical College
Address: 110 Bleecker Street, New York, NY, 10012, United States
Keywords: Bayesian model averaging ; observational ; high throughput data ; microarray ; metabolomics

A key objective of many studies involving high throughput data is to detect differentially altered (DA) bio-molecules. When such data are observational, how to properly control the impact of sample heterogeneity becomes important. Most methods for DA bio-molecule detection can be considered as single model approaches as they rely on a ranking statistic derived from a single model with or without covariate adjustment. Such approaches are conceptually flawed because of unavoidable model misspecification. We show that DA bio-molecule detection intrinsically requires a multi-model handling. To properly control for sample heterogeneity and to provide a flexible and coherent framework for identifying simultaneously DA bio-molecules associated with a single or multiple sample characteristics and/or their interactions, we developed a Bayesian model averaging approach with an empirical prior model probability specification. We demonstrate through simulated microarray data that this approach improves the performance of DE gene detection comparing to single model approaches. Flexibility of this approach is illustrated through analysis of gene expression microarray data and metabolomic data.

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