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

Zhong Guan·Hongyu Zhao
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Abstract

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@yale.edu.

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Biomarkers, TumorData Interpretation, StatisticalDiagnosis, Computer-AssistedGene Expression ProfilingGenetic MarkersGenetic Predisposition to DiseaseGenetic TestingHumansLeukemiaModels, GeneticModels, StatisticalNeoplasm ProteinsReproducibility of ResultsSensitivity and SpecificityComparative StudyEvaluation StudyJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.Validation Study

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Zhong Guan, Hongyu Zhao. 2004-09-16. A semiparametric approach for marker gene selection based on gene expression data.. https://doi.org/10.1093/bioinformatics%2Fbti032

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