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Functional-Network-Based Gene Set Analysis Using Gene-Ontology

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  • Billy Chang
  • Rafal Kustra
  • Weidong Tian

Abstract

To account for the functional non-equivalence among a set of genes within a biological pathway when performing gene set analysis, we introduce GOGANPA, a network-based gene set analysis method, which up-weights genes with functions relevant to the gene set of interest. The genes are weighted according to its degree within a genome-scale functional network constructed using the functional annotations available from the gene ontology database. By benchmarking GOGANPA using a well-studied P53 data set and three breast cancer data sets, we will demonstrate the power and reproducibility of our proposed method over traditional unweighted approaches and a competing network-based approach that involves a complex integrated network. GOGANPA’s sole reliance on gene ontology further allows GOGANPA to be widely applicable to the analysis of any gene-ontology-annotated genome.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0055635
    DOI: 10.1371/journal.pone.0055635
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    References listed on IDEAS

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    1. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
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    Cited by:

    1. Charles Bettembourg & Christian Diot & Olivier Dameron, 2014. "Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    2. 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.

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