Data Mining in Genomics and ProteomicsView this Special Issue
Research article | Open Access
Dechang Chen, Zhenqiu Liu, Xiaobin Ma, Dong Hua, "Selecting Genes by Test Statistics", BioMed Research International, vol. 2005, Article ID 589157, 7 pages, 2005. https://doi.org/10.1155/JBB.2005.132
Selecting Genes by Test Statistics
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.
Copyright © 2005 Hindawi Publishing Corporation. 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.