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Biostatistics Advance Access originally published online on May 15, 2006
Biostatistics 2007 8(1):2-8; 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.

Outlier sums for differential gene expression analysis

Robert Tibshirani*

Department of Health Research & Policy and Department of Statistics, Stanford University, Stanford, CA 94305, USA tibs{at}stat.stanford.edu

Trevor Hastie

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

* To whom correspondence should be addressed.

We propose a method for detecting genes that, in a disease group, exhibit unusually high 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 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 cancer profile outlier analysis proposal of Tomlins and others (2005).

Keywords: Cancer; COPA; Gene expression analysis; Microarray


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