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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309717
Title: Empirical Bayesian Incorporation of Method Selection Into Massive Multiple Testing Analyses
Author(s): Stanley Pounds*+ and Cuilan L. Gao and Shesh Nath Rai and Demba Fofana
Companies: St. Jude Children's Research Hospital and University of Tennessee in Chattanooga and University of Louisville and University of Memphis
Keywords: genomics ; multiple-testing ; genome-wide association studies ; assumption evaluation
Abstract:

Many statistical genomics applications, such as screening the association of each of many genes with a phenotype, involve massive multiple testing. The same hypothesis testing method is typically used to perform each test in these types of applications. For example, the t-test may be used to compare the expression of each gene across two groups. However, every hypothesis testing method is based on certain assumptions. Thus, it is impossible that any one hypothesis testing method could be best (or perhaps even valid) for each of a very large number of tests. Therefore, we propose a general empirical Bayesian technique to evaluate assumptions and select the most appropriate hypothesis testing method for each test. This technique reports a final local false discovery rate estimate that accounts for all layers of multiple testing. The advantages of the new technique are observed in simulation studies and example analyses involving microarray and mRNA-seq gene expression data.


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