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Biostatistics Advance Access originally published online on May 11, 2006
Biostatistics 2007 8(2):212-227; doi:10.1093/biostatistics/kxl002
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Averaged gene expressions for regression

Mee Young Park*

Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA meeyoung{at}google.com

Trevor Hastie

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

Robert Tibshirani

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

* To whom correspondence should be addressed.

Although averaging is a simple technique, it plays an important role in reducing variance. We use this essential property of averaging in regression of the DNA microarray data, which poses the challenge of having far more features than samples. In this paper, we introduce a two-step procedure that combines (1) hierarchical clustering and (2) Lasso. By averaging the genes within the clusters obtained from hierarchical clustering, we define supergenes and use them to fit regression models, thereby attaining concise interpretation and accuracy. Our methods are supported with theoretical justifications and demonstrated on simulated and real data sets.

Keywords: Averaging; Hierarchical clustering; Lasso; Variance reduction

Received January 3, 2006; revised April 27, 2006; accepted for publication May 8, 2006.


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