Size, power and false discovery rates
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Open Access
- 1 August 2007
- journal article
- Published by Institute of Mathematical Statistics in The Annals of Statistics
- Vol. 35 (4)
- https://doi.org/10.1214/009053606000001460
Abstract
Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spectroscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr's, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of ``significant'' discoveries.Comment: Published in at http://dx.doi.org/10.1214/009053606000001460 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.orgKeywords
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This publication has 31 references indexed in Scilit:
- A mixture model for estimating the local false discovery rate in DNA microarray analysisBioinformatics, 2004
- Large-Scale Simultaneous Hypothesis TestingJournal of the American Statistical Association, 2004
- Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error RatesStatistical Applications in Genetics and Molecular Biology, 2004
- Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-valuesBioinformatics, 2003
- A mixture model approach to detecting differentially expressed genes with microarray dataFunctional & Integrative Genomics, 2003
- Multiple Hypothesis Testing in Microarray ExperimentsStatistical Science, 2003
- A Direct Approach to False Discovery RatesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Empirical bayes methods and false discovery rates for microarraysGenetic Epidemiology, 2002
- Analysis of Variance for Gene Expression Microarray DataJournal of Computational Biology, 2000
- Using specially designed exponential families for density estimationThe Annals of Statistics, 1996