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Association Analysis for Large-Scale Gene Set Data

  • Protocol
Gene Function Analysis

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 408))

Abstract

High-throughput experiments in biology often produce sets of genes of potential interests. Some of those gene sets might be of considerable size. Therefore, computer-assisted analysis is necessary for the biological interpretation of the gene sets, and for creating working hypotheses, which can be tested experimentally. One obvious way to analyze gene set data is to associate the genes with a particular biological feature, for example, a given pathway. Statistical analysis could be used to evaluate if a gene set is truly associated with a feature. Over the past few years many tools that perform such analysis have been created. In this chapter, using WebGestalt as an example, it will be explained in detail how to associate gene sets with functional annotations, pathways, publication records, and protein domains.

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Author information

Authors and Affiliations

  1. Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, TN

    Stefan A. Kirov

  2. Vanderbilt University, Nashville, TN

    Bing Zhang & Jay R. Snoddy

Authors
  1. Stefan A. Kirov
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  2. Bing Zhang
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  3. Jay R. Snoddy
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Editor information

Editors and Affiliations

  1. The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD

    Michael F. Ochs

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© 2007 Humana Press Inc.

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Kirov, S.A., Zhang, B., Snoddy, J.R. (2007). Association Analysis for Large-Scale Gene Set Data. In: Ochs, M.F. (eds) Gene Function Analysis. Methods in Molecular Biology™, vol 408. Humana Press. https://doi.org/10.1007/978-1-59745-547-3_2

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  • DOI: https://doi.org/10.1007/978-1-59745-547-3_2

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-734-1

  • Online ISBN: 978-1-59745-547-3

  • eBook Packages: Springer Protocols

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