Open Access
February 2003 Multiple Hypothesis Testing in Microarray Experiments
Sandrine Dudoit, Juliet Popper Shaffer, Jennifer C. Boldrick
Statist. Sci. 18(1): 71-103 (February 2003). DOI: 10.1214/ss/1056397487


DNA microarrays are part of a new and promising class of biotechnologies that allow the monitoring of expression levels in cells for thousands of genes simultaneously. An important and common question in DNA microarray experiments is the identification of differentially expressed genes, that is, genes whose expression levels are associated with a response or covariate of interest. The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates. As a typical microarray experiment measures expression levels for thousands of genes simultaneously, large multiplicity problems are generated. This article discusses different approaches to multiple hypothesis testing in the context of DNA microarray experiments and compares the procedures on microarray and simulated data sets.


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Sandrine Dudoit. Juliet Popper Shaffer. Jennifer C. Boldrick. "Multiple Hypothesis Testing in Microarray Experiments." Statist. Sci. 18 (1) 71 - 103, February 2003.


Published: February 2003
First available in Project Euclid: 23 June 2003

zbMATH: 1048.62099
MathSciNet: MR1997066
Digital Object Identifier: 10.1214/ss/1056397487

Keywords: adjusted p-value , DNA microarray. , False discovery rate , family-wise Type I error rate , multiple hypothesis testing , permutation

Rights: Copyright © 2003 Institute of Mathematical Statistics

Vol.18 • No. 1 • February 2003
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