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Detecting Stable Clusters Using Principal Component Analysis

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Book cover Functional Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 224))

Abstractt

Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well.

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Author information

Authors and Affiliations

  1. Department of Biochemistry, Stanford University School of Medicine, Palo Alto, CA

    Asa Ben-Hur

  2. Clopinet, Berkeley, CA

    Isabelle Guyon

Authors
  1. Asa Ben-Hur
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  2. Isabelle Guyon
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Editor information

Editors and Affiliations

  1. Laboratory of Genetics, NIMH/NHGRI, National Institutes of Health, Rockville, MD

    Michael J. Brownstein

  2. Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, St. Paul, MN

    Arkady B. Khodursky

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© 2003 Humana Press Inc.

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Ben-Hur, A., Guyon, I. (2003). Detecting Stable Clusters Using Principal Component Analysis. In: Brownstein, M.J., Khodursky, A.B. (eds) Functional Genomics. Methods in Molecular Biology, vol 224. Humana Press. https://doi.org/10.1385/1-59259-364-X:159

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  • DOI: https://doi.org/10.1385/1-59259-364-X:159

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-291-9

  • Online ISBN: 978-1-59259-364-4

  • eBook Packages: Springer Protocols

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