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

The ‘miss rate’ for the analysis of gene expression data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics Oxford University Press

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

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References (14)

Publisher
Oxford University Press
Copyright
Biostatistics Vol. 6 No. 1 © Oxford University Press 2005; all rights reserved.
ISSN
1465-4644
eISSN
1468-4357
DOI
10.1093/biostatistics/kxh021
pmid
15618531
Publisher site
See Article on Publisher Site

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.

Journal

BiostatisticsOxford University Press

Published: Jan 1, 2005

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