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Biostatistics Advance Access published online on May 15, 2006
Biostatistics, doi:10.1093/biostatistics/kxl005
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received March 24, 2006
Accepted May 10, 2006

Article

Outlier sums for differential gene expression analysis

Robert Tibshirani 1 * and Trevor Hastie 2

1 Dept. of Health Research & Policy, and Department of Statistics, Stanford University, Stanford, CA 94305
2 Dept. Statistics, and Dept. of Health Research & Policy Stanford University, Stanford, CA 94305

* To whom correspondence should be addressed.
Robert Tibshirani, E-mail: tibs{at}stanford.edu


  Abstract

We propose a method for detecting genes that, in a disease group, exhibit unusually high or gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In a real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent COPA proposal of Tomlins et al. (2005).


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