A semiparametric approach for marker gene selection based on gene expression data

作者：Guan Z; Zhao HY^*

来源：Bioinformatics, 2005, 21(4): 529-536.

DOI：10.1093/bioinformatics/bti032

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.

• 出版日期2005-2-15