Extracting the strongest signals from omics data: Differentially expressed pathways and beyond

Galina Glazko, Yasir Rahmatallah, Boris Zybailov, Frank Emmert-Streib

    Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


    The analysis of gene sets (in a form of functionally related genes or pathways) has become the method of choice for extracting the strongest signals from omics data. The motivation behind using gene sets instead of individual genes is two-fold. First, this approach incorporates pre-existing biological knowledge into the analysis and facilitates the interpretation of experimental results. Second, it employs a statistical hypotheses testing framework. Here, we briefly review main Gene Set Analysis (GSA) approaches for testing differential expression of gene sets and several GSA approaches for testing statistical hypotheses beyond differential expression that allow extracting additional biological information from the data. We distinguish three major types of GSA approaches testing: (1) differential expression (DE), (2) differential variability (DV), and (3) differential co-expression (DC) of gene sets between two phenotypes. We also present comparative power analysis and Type I error rates for different approaches in each major type of GSA on simulated data. Our evaluation presents a concise guideline for selecting GSA approaches best performing under particular experimental settings. The value of the three major types of GSA approaches is illustrated with real data example. While being applied to the same data set, major types of GSA approaches result in complementary biological information.

    Original languageEnglish
    Title of host publicationMethods in Molecular Biology
    Number of pages35
    ISBN (Electronic)978-1-4939-7027-8
    Publication statusPublished - 2017
    Publication typeA3 Book chapter

    Publication series

    NameMethods in Molecular Biology
    ISSN (Print)1064-3745


    • Competitive
    • Differential co-expression
    • Differential expression
    • Differential variability
    • Gene set analysis approaches
    • Hypotheses testing
    • Omics data
    • Self-contained

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Molecular Biology
    • Genetics


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