Multiple Hypothesis Testing in Microarray Experiments

Sandrine Dudoit, Division of Biostatistics, University of California, Berkeley
Juliet Popper Shaffer, Department of Statistics, University of California, Berkeley
Jennifer C. Boldrick, Dept. of Microbiology & Immunology, Stanford University

Abstract

DNA microarrays are a new and promising biotechnology which allows the monitoring of expression levels in cells for thousands of genes simultaneously. An important and common question in microarray experiments is the identification of differentially expressed genes, i.e., 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 microarray experiments and compares the procedures on microarray and simulated datasets.