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Statistical Methods for Integrating Multiple Types of High-Throughput Data

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Statistical Methods in Molecular Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 620))

Abstract

Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice.

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Acknowledgments

The authors thank Drs. Wei Pan, Peng Wei, Feng Tai, and Guanghua Xiao for discussions and suggestions, and thank Dr. Peng Wei for providing WinBUGS programs. This work was partially supported by NIH UL1 RR024982 1R21 DA027592, and SPORE P50 CA70907.

Author information

Authors and Affiliations

  1. Division of Biostatistics, Department of Clinical Sciences, The Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Yang Xie & Chul Ahn

Authors
  1. Yang Xie
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  2. Chul Ahn
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Editor information

Editors and Affiliations

  1. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Heejung Bang

  2. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Xi Kathy Zhou

  3. Journal of Experimental Medicine, Rockefeller University Press, First Ave. 1114, New York, 10021, New York, USA

    Heather L. van Epps

  4. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Madhu Mazumdar

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© 2010 Humana Press, a part of Springer Science+Business Media, LLC

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Xie, Y., Ahn, C. (2010). Statistical Methods for Integrating Multiple Types of High-Throughput Data. In: Bang, H., Zhou, X., van Epps, H., Mazumdar, M. (eds) Statistical Methods in Molecular Biology. Methods in Molecular Biology, vol 620. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-580-4_19

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  • DOI: https://doi.org/10.1007/978-1-60761-580-4_19

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-578-1

  • Online ISBN: 978-1-60761-580-4

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