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The Analysis of Gene Expression Data pp 388–408Cite as

Bayesian Decomposition

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

Abstract

Gene chips and gene expression microarrays offer the opportunity to study biological systems on a genome-wide basis, exploring the full transcriptional response in an experiment or therapy. Because of the complexity of living organisms, these transcriptional responses are complex, with multiple, overlapping groups of genes being expressed in response to continuing internal and external stimuli. In order to use expression measurements to identify upstream modifications in signaling pathways, it is necessary to disentangle these overlapping responses. Bayesian Decomposition provides a method of identifying such overlap and correctly assigning genes to multiple groups, allowing easier identification of pathway modifications. Here the results of the application of Bayesian Decomposition to cell cycle data are shown.

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Authors
  1. Michael F. Ochs
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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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Ochs, M.F. (2003). Bayesian Decomposition. 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_17

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  • DOI: https://doi.org/10.1007/0-387-21679-0_17

  • 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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