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Selecting genes by test statistics.

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.

SUBMITTER: Chen D

PROVIDER: S-EPMC1184045 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature

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Selecting genes by test statistics.

Chen Dechang D Liu Zhenqiu Z Ma Xiaobin X Hua Dong D

Journal of biomedicine & biotechnology 20050601 2

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 stu ...[more]

PMID: 16046818

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