Gene selection for sample classifications in microarray experiments

Chen A.N. Tsai, Chun Houh Chen, Te Chang Lee, I. Ching Ho, Ueng Cheng Yang, James J. Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene set becomes an important prestep in sample classification. In this study, we propose a family-wise error (FWE) rate approach to selection of discriminatory genes for two-sample or multiple-sample classification. The FWE approach controls the probability of the number of one or more false positives at a prespecified level. A public colon cancer data set is used to evaluate the performance of the proposed approach for the two classification methods: k nearest neighbors (k-NN) and support vector machine (SVM). The selected gene sets from the proposed procedure appears to perform better than or comparable to several results reported in the literature using the univariate analysis without performing multivariate search. In addition, we apply the FWE approach to a toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV) for a total of 55 samples for a multisample classification. Two gene sets are considered: the gene set ΩF formed by the ANOVA F-test, and a gene set ΩT formed by the union of one-versus-all t-tests. The predicted accuracies are evaluated using the internal and external crossvalidation. Using the SVM classification, the overall accuracies to predict 55 samples into one of the nine treatments are above 80% for internal crossvalidation. ΩF has slightly higher accuracy rates than ΩT. The overall predicted accuracies are above 70% for the external crossvalidation; the two gene sets ΩT and ΩF performed equally well.

Original languageEnglish
Pages (from-to)607-614
Number of pages8
JournalDNA and Cell Biology
Volume23
Issue number10
DOIs
StatePublished - Oct 2004

Fingerprint

Dive into the research topics of 'Gene selection for sample classifications in microarray experiments'. Together they form a unique fingerprint.

Cite this