Ensuring the statistical soundness of competitive gene set approaches: Gene filtering and genome-scale coverage are essential

Shailesh Tripathi, Galina V. Glazko, Frank Emmert-Streib

    Research output: Contribution to journalArticleScientificpeer-review

    21 Citations (Scopus)

    Abstract

    In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

    Original languageEnglish
    JournalNucleic Acids Research
    Volume41
    Issue number7
    DOIs
    Publication statusPublished - Apr 2013
    Publication typeA1 Journal article-refereed

    ASJC Scopus subject areas

    • Genetics

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