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Linear Combination Test for Hierarchical Gene Set Analysis

Author

Listed:
  • Wang Xiaoming

    (University of Alberta and Shanghai University of Finance and Economics)

  • Dinu Irina

    (University of Alberta)

  • Liu Wei

    (University of Alberta)

  • Yasui Yutaka

    (University of Alberta)

Abstract

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements, especially within each gene set, can be highly correlated; and (3) the number of gene sets that can be examined is large and increasing rapidly. These challenges call for gene-set testing procedures that have both efficiency in computation for large GSAs and high power in the presence of the high correlation.

Suggested Citation

  • Wang Xiaoming & Dinu Irina & Liu Wei & Yasui Yutaka, 2011. "Linear Combination Test for Hierarchical Gene Set Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-18, March.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:13
    DOI: 10.2202/1544-6115.1641
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    References listed on IDEAS

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    1. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    2. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
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