Bioinformatics Advance Access originally published online on September 16, 2004
Bioinformatics 2005 21(4):529-536; doi:10.1093/bioinformatics/bti032
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Bioinformatics vol. 21 issue 4 © Oxford University Press 2005; all rights reserved.
A semiparametric approach for marker gene selection based on gene expression data
1 Department of Mathematical Sciences, Indiana University South Bend South Bend, IN 46634, USA
2 Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA
*To whom correspondence should be addressed.
Motivation: Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification.
Results: A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates.
Contact: hongyu.zhao{at}yale.edu
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