Test

, Volume 12, Issue 1, pp 1–77 | Cite as

Resampling-based multiple testing for microarray data analysis

Article

Abstract

The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. Westfall and Young (1993) propose resampling-basedp-value adjustment procedures which are highly relevant to microarray experiments. This article discusses different criteria for error control in resampling-based multiple testing, including (a) the family wise error rate of West-fall and Young (1993) and (b) the false discovery rate developed by Benjamini and Hochberg (1995), both from a frequentist viewpoint; and (c) the positive false discovery rate of Storey (2002a), which has a Bayesian motivation. We also introduce our recently developed fast algorithm for implementing the minP adjustment to control family-wise error rate. Adjustedp-values for different approaches are applied to gene expression data from two recently published microarray studies. The properties of these procedures for multiple testing are compared.

Key Words

multiple testing family-wise error rate false discovery rate adjustedp-value fast algorithm minP microarray 

AMS subject classification

62J15 62G09 62P10 

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Copyright information

© Sociedad Española de Estadistica e Investigación Operativa 2003

Authors and Affiliations

  • Youngchao Ge
    • 1
  • Sandrine Dudoit
    • 2
  • Terence P. Speed
    • 3
    • 4
  1. 1.Department of StatisticsUniversity of California, BerkeleyBerkeley
  2. 2.Division of BiostatisticsUniversity of CaliforniaBerkeley
  3. 3.Division of Genetics and BioinformaticsThe Walter and Eliza Hall Institute of Medical ResearchAustralia
  4. 4.Department of StatisticsUniversity of CaliforniaBerkeley

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