Sample size calculation for multiple testing in microarray data analysis.

Published

Journal Article

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.

Full Text

Duke Authors

Cited Authors

  • Jung, S-H; Bang, H; Young, S

Published Date

  • January 2005

Published In

Volume / Issue

  • 6 / 1

Start / End Page

  • 157 - 169

PubMed ID

  • 15618534

Pubmed Central ID

  • 15618534

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxh026

Language

  • eng

Conference Location

  • England