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Clustering

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

Clustering techniques are used to arrange genes in some natural way, that is, to organize genes into groups or clusters with similar behavior across relevant tissue samples (or cell lines). These techniques can also be applied to tissues rather than genes. Methods such as hierarchical agglomerative clustering, k-means clustering, the self-organizing map, and model-based methods have been used. Here we focus on mixtures of normals to provide a model-based clustering of tissue samples (gene signatures) and of gene profiles, including time-course gene expression data.

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Author information

Authors and Affiliations

  1. Department of Mathematics, The University of Queensland, Brisbane, QLD, Australia

    G. J. McLachlan

  2. Department of Health, The University of Queensland, Brisbane, QLD, Australia

    R. W. Bean

  3. School of Medicine, Griffith Health Institute, Griffith University, Brisbane, QLD, Australia

    S. K. Ng

Authors
  1. G. J. McLachlan
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  2. R. W. Bean
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  3. S. K. Ng
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Corresponding author

Correspondence to G. J. McLachlan .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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© 2017 Springer Science+Business Media New York

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McLachlan, G.J., Bean, R.W., Ng, S.K. (2017). Clustering. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_19

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_19

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

  • eBook Packages: Springer Protocols

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