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An Efficient Semiparametric Approach for Marker Gene Selection and Patient Classification

Author

Listed:
  • Jingjing Wu

    (Department of Mathematics and Statistics, University of Calgary, Canada)

  • Guoqiang Chen

    (Enbridge, Canada)

  • Zeny Feng

    (Department of Mathematics and Statistics, University of Guelph, Canada)

Abstract

The advancement of microarray technology has greatly facilitated the research in gene expression based classification of patient samples. For example, in cancer research, microarray gene expression data has been used for cancer or tumor classification. When the study is only focusing on two classes, for example two different cancer types, we propose a two-sample semiparametric model to model the distributions of gene expression level for different classes. To estimate the parameters, we consider both maximum semiparametric likelihood estimate (MLE) and minimum Hellinger distance estimate (MHDE).

Suggested Citation

  • Jingjing Wu & Guoqiang Chen & Zeny Feng, 2017. "An Efficient Semiparametric Approach for Marker Gene Selection and Patient Classification," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(2), pages 40-49, April.
    • Handle: RePEc:adp:jbboaj:v:1:y:2017:i:2:p:40-49
    DOI: 10.19080/BBOAJ.2017.01.555558
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    References listed on IDEAS

    as
    1. Marine Jeanmougin & Aurelien de Reynies & Laetitia Marisa & Caroline Paccard & Gregory Nuel & Mickael Guedj, 2010. "Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
    2. Wu, Jingjing & Karunamuni, Rohana & Zhang, Biao, 2010. "Minimum Hellinger distance estimation in a two-sample semiparametric model," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1102-1122, May.
    3. Karunamuni, Rohana J. & Wu, Jingjing, 2011. "One-step minimum Hellinger distance estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3148-3164, December.
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