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The 'miss rate' for the analysis of gene expression data
Jonathan Taylor1, Robert Tibshirani, Bradley Efron
1Department of Statistics, Stanford University, Stanford, CA 94305, USA. jonathan.taylor@stanford.edu
Biostatistics (Oxford, England)
|December 25, 2004
Abstract:
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.