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Activity Number: 468
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #306836
Title: Comparison of False Discovery Rates of Several Statistical Methods in the Context of Gene Expression Data
Author(s): Nusrat Jahan*+ and Pradeep Singh
Companies: James Madison University and Southeast Missouri State University
Address: Dept. of Mathematics and Statistics, Harrisonburg, VA, 22807, United States
Keywords: Genomics ; False discovery rate ; q value ; Jackknife estimator ; Baumgartner test

Multiple testing is a major problem for the differential analysis of gene expression data. High dimensionality of the data produces numerous false positives. False discovery rate, which is the expected proportion of false positives from all rejected null hypotheses, is the most widely used criterion to control this problem. In this study, Monte Carlo method is used to compare false discovery rate under several differential testing procedures: traditional equal variance t test, Satterthawite Welch t test, median based t test, modified t-test based on jackknife estimator of population variance, and Baumgartner nonparametric test.

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