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Biostatistics 2005 6(1):111-117; doi:10.1093/biostatistics/kxh021
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Biostatistics Vol. 6 No. 1 © Oxford University Press 2005; all rights reserved.

The ‘miss rate’ for the analysis of gene expression data

Jonathan Taylor*

Department of Statistics, Stanford University, Stanford, CA 94305, USA jonathan.taylor{at}stanford.edu

Robert Tibshirani

Department of Health Research & Policy and Department of Statistics, Stanford University, Stanford, CA 94305, USA

Bradley Efron

Departments of Statistics, and Health Research & Policy, Stanford University, Stanford, CA 94305, USA

* To whom correspondence should be addressed.

Multiple testing issues are important in gene expression studies, where typically thousands of genes are compared over two or more experimental conditions. The false discovery rate has become a popular measure in this setting. Here we discuss a complementary measure, the ‘miss rate’, and show how to estimate it in practice.


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