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http://dx.doi.org/10.7465/jkdi.2014.25.5.971

Comparison and analysis of multiple testing methods for microarray gene expression data  

Seo, Sumin (Department of Information and Statistics, Duksung Women's University)
Kim, Tae Houn (Department of PrePharmMed, Duksung Women's University)
Kim, Jaehee (Department of Information and Statistics, Duksung Women's University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.5, 2014 , pp. 971-986 More about this Journal
Abstract
When thousands of hypotheses are tested simultaneously, the probability of rejecting any true hypotheses increases, and large multiplicity problems are generated. To solve these problems, researchers have proposed different approaches to multiple testing methods, considering family-wise error rate (FWER), false discovery rate (FDR) or false nondiscovery rate (FNR) as a type I error and some test statistics. In this article, we discuss Bonferroni (1960), Holm (1979), Benjamini and Hochberg (1995) and Benjamini and Yekutieli (2001) procedures based on T statistics, modified T statistics or local-pooled-error (LPE) statistics. We also consider Sun and Cai (2007) procedure based on Z statistics. These procedures are compared in the simulation and applied to Arabidopsis microarray gene expression data to identify differentially expressed genes.
Keywords
Arabidopsis; false discovery rate; false nondiscovery rate; family-wise error rate; gene expression data; multiple testing;
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