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Bioinformatics Advance Access published online on September 20, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti681
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 15, 2005
Revised September 15, 2005
Accepted September 17, 2005

Article

Classification of microarrays to nearest centroids

Alan R. Dabney 1*

1 Department of Biostatistics, University of Washington, Seattle 98195, USA

* To whom correspondence should be addressed.
Alan R. Dabney, E-mail: adabney{at}u.washington.edu


  Abstract

Motivation: Classification of biological samples by microarrays is a topic of much interest. A number of methods have been proposed and successfully applied to this problem. It has recently been shown that classification by nearest centroids provides an accurate predictor that may outperform much more complicated methods. The "Prediction Analysis of Microarrays" (PAM) approach is one such example, which the authors strongly motivate by its simplicity and intepretability. In this spirit, I seek to assess the performance of classifiers simpler than even PAM.

Results: I surprisingly show that the modified t-statistics and shrunken centroids employed by PAM tend to increase misclassification error when compared to their simpler counterparts. Based on these observations, I propose a classification method called "Classification to Nearest Centroids" (ClaNC). ClaNC ranks genes by standard t-statistics, does not shrink centroids, and uses a class-specific gene-selection procedure. Because of these modifications, ClaNC is arguably simpler and easier to interpret than PAM, and it can be viewed as a traditional nearest centroid classifier that uses specially selected genes. I demonstrate that ClaNC error rates tend to be significantly less than those for PAM, for a given number of active genes.

Availability: Point-and-click software is freely available at http://students.washington.edu/adabney/clanc.


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