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A GS-CORE algorithm for performing a reduction test on multiple gene sets and their core genes

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  • Tae Yang

Abstract

Gene-set analysis seeks to identify enriched gene sets that are strongly associated with the phenotype. In many applications, only a small subset of core genes in each enriched gene set is likely associated with the phenotype. The reduction of enriched gene sets to the corresponding leading-edge subsets of core genes is a useful way for biologists to understand the biological processes underlying the association of a gene set with the phenotype of interest. Therefore, we propose a new gene-set analysis that tests the significance of enrichment on multiple gene sets, while simultaneously determining the corresponding leading-edge subsets of core genes. In the proposed analysis, we assigned a newly defined enrichment score to each gene set, and then corrected the statistical significance of the score for multiple testing of many gene sets by controlling the false-discovery rate. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Tae Yang, 2015. "A GS-CORE algorithm for performing a reduction test on multiple gene sets and their core genes," Computational Statistics, Springer, vol. 30(1), pages 29-41, March.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:1:p:29-41
    DOI: 10.1007/s00180-014-0519-9
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    References listed on IDEAS

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    1. Marina Evangelou & Augusto Rendon & Willem H Ouwehand & Lorenz Wernisch & Frank Dudbridge, 2012. "Comparison of Methods for Competitive Tests of Pathway Analysis," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    2. Billy Chang & Rafal Kustra & Weidong Tian, 2013. "Functional-Network-Based Gene Set Analysis Using Gene-Ontology," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-13, February.
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