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