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Biostatistics Advance Access published online on April 7, 2006
Biostatistics, doi:10.1093/biostatistics/kxj035
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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 November 1, 2005
Revised March 27, 2006
Accepted March 29, 2006

Article

Regularized linear discriminant analysis and its application in microarrays

Yaqian Guo 1 *, Trevor Hastie 1, and Robert Tibshirani 2

1 Department of Statistics, Stanford University, Stanford, CA 94305
2 Redwood Bldg, Room T101C, Department of Health Research and Policy, Stanford University, Stanford, CA 94305

* To whom correspondence should be addressed.
Yaqian Guo, E-mail: yaqiang{at}stanford.edu


  Abstract

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani et al., 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the SVM classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.


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