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Statistical Contributions to Proteomic Research

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The Urinary Proteome

Part of the book series: Methods in Molecular Biology ((MIMB,volume 641))

Abstract

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the key statistical principles that should guide the experimental design and analysis of such studies.

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Acknowledgements

JSM’s effort was partially supported by a grant from the NCI, R01 CA107304-01. HBG’s effort was supported by NIDA grants DA18310 (cell–cell signaling neuroproteomics center). HBG and JSM are supported by AA13888.

Author information

Authors and Affiliations

  1. Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA

    Jeffrey S. Morris

  2. Department of Bioinformatics and Computational Biology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA

    Keith A. Baggerly & Kevin R. Coombes

  3. Department of Anesthesiology and Perioperative Medicine, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA

    Howard B. Gutstein

Authors
  1. Jeffrey S. Morris
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  2. Keith A. Baggerly
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  3. Howard B. Gutstein
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  4. Kevin R. Coombes
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Editor information

Editors and Affiliations

  1. Medical Center, Dept. Pathology & Cell Biology, Columbia University, West 168th Street 622, New York, 10032-3720, USA

    Alex J. Rai

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Morris, J.S., Baggerly, K.A., Gutstein, H.B., Coombes, K.R. (2010). Statistical Contributions to Proteomic Research. In: Rai, A. (eds) The Urinary Proteome. Methods in Molecular Biology, vol 641. Humana Press. https://doi.org/10.1007/978-1-60761-711-2_9

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  • DOI: https://doi.org/10.1007/978-1-60761-711-2_9

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60761-710-5

  • Online ISBN: 978-1-60761-711-2

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