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June 2012 A Bayesian model averaging approach for observational gene expression studies
Xi Kathy Zhou, Fei Liu, Andrew J. Dannenberg
Ann. Appl. Stat. 6(2): 497-520 (June 2012). DOI: 10.1214/11-AOAS526

Abstract

Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a preselected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error due to bias for some genes and reduced efficiency for the others. We evaluated the impact of model misspecification from such approaches on detecting DE genes and identified parameters that affect the magnitude of impact. To properly control for sample heterogeneity and to provide a flexible and coherent framework for identifying simultaneously DE genes associated with a single or multiple sample characteristics and/or their interactions, we proposed a Bayesian model averaging approach which corrects the model misspecification by averaging over model space formed by all relevant covariates. An empirical approach is suggested for specifying prior model probabilities. We demonstrated through simulated microarray data that this approach resulted in improved performance in DE gene identification compared to the single model approaches. The flexibility of this approach is demonstrated through our analysis of data from two observational microarray studies.

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Xi Kathy Zhou. Fei Liu. Andrew J. Dannenberg. "A Bayesian model averaging approach for observational gene expression studies." Ann. Appl. Stat. 6 (2) 497 - 520, June 2012. https://doi.org/10.1214/11-AOAS526

Information

Published: June 2012
First available in Project Euclid: 11 June 2012

zbMATH: 1243.62139
MathSciNet: MR2976480
Digital Object Identifier: 10.1214/11-AOAS526

Keywords: Bayesian model averaging , differential gene expression , microarray , observational study

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 2 • June 2012
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