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Journal of Biomedicine and Biotechnology
Volume 2005 (2005), Issue 2, Pages 132-138
http://dx.doi.org/10.1155/JBB.2005.132
Research article

Selecting Genes by Test Statistics

1Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA
2Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA
3Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA
4Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA

Received 28 April 2004; Revised 22 November 2004; Accepted 23 November 2004

Copyright © 2005 This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.