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The Analysis of Gene Expression Data pp 409–427Cite as

Bayesian Clustering of Gene Expression Dynamics

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Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

This chapter presents a Bayesian method for model-based clustering of gene expression dynamics and a program implementing it. The method represents gene expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters, given the available data. The main contributions of this approach are the ability to take into account the dynamic nature of gene expression time series during clustering and an automated, principled way to decide when two series are different enough to belong to different clusters. The reliance of this method on an explicit statistical representation of gene expression dynamics makes it possible to use standard statistical techniques to assess the goodness of fit of the resulting model and validate the underlying assumptions. A set of gene expression time series, collected to study the response of human fibroblasts to serum, is used to illustrate the properties of the method and the functionality of the program.

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Authors
  1. Paola Sebastiani
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  2. Marco Ramoni
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  3. Isaac S. Kohane
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Editor information

Editors and Affiliations

  1. Departments of Oncology, Biostatistics,and Pathology, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Giovanni Parmigiani

  2. Departments of Oncology and Biostatistics, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Elizabeth S. Garrett

  3. Departments of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Rafael A. Irizarry

  4. Departments of Biostatistics and Epidemiology, Johns Hopkins University, Baltimore, MD, 21205-2013, USA

    Scott L. Zeger

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© 2003 Springer-Verlag New York, Inc.

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Sebastiani, P., Ramoni, M., Kohane, I.S. (2003). Bayesian Clustering of Gene Expression Dynamics. In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_18

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  • DOI: https://doi.org/10.1007/0-387-21679-0_18

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95577-3

  • Online ISBN: 978-0-387-21679-9

  • eBook Packages: Springer Book Archive

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