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Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond

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Biological Networks and Pathway Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1613))

Abstract

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.

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Acknowledgments

We would like to thank Bárbara Macías Solís for proof reading of the manuscript. Support has been provided in part by the Arkansas INBRE program, with grants from the National Center for Research Resources (P20RR016460) and the National Institute of General Medical Sciences (P20 GM103429) from the National Institutes of Health. Large-scale computer simulations were implemented using the High Performance Computing (HPC) resources at the UALR Computational Research Center supported by the following grants: National Science Foundation grants CRI CNS-0855248, EPS-0701890, MRI CNS-0619069 and OISE-0729792.

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Authors and Affiliations

  1. Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA

    Galina Glazko & Yasir Rahmatallah

  2. Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA

    Boris Zybailov

  3. Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, 33720, Finland

    Frank Emmert-Streib

Authors
  1. Galina Glazko
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  2. Yasir Rahmatallah
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  3. Boris Zybailov
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  4. Frank Emmert-Streib
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Corresponding author

Correspondence to Galina Glazko .

Editor information

Editors and Affiliations

  1. Keck School of Medicine, University of Southern California, Los Angeles, California, USA

    Tatiana V. Tatarinova

  2. Prosapia Genetics, Solana Beach, California, USA

    Yuri Nikolsky

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Glazko, G., Rahmatallah, Y., Zybailov, B., Emmert-Streib, F. (2017). Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond. In: Tatarinova, T., Nikolsky, Y. (eds) Biological Networks and Pathway Analysis. Methods in Molecular Biology, vol 1613. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7027-8_7

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  • DOI: https://doi.org/10.1007/978-1-4939-7027-8_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7025-4

  • Online ISBN: 978-1-4939-7027-8

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