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Integrative Approaches for Microarray Data Analysis

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Next Generation Microarray Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 802))

Abstract

Microarrays were one of the first technologies of the genomic revolution to gain widespread adoption, rapidly expanding from a cottage industry to the source of thousands of experimental results. They were one of the first assays for which data repositories and metadata were standardized and researchers were required by many journals to make published data publicly available. Microarrays provide high-throughput insights into the biological functions of genes and gene products; however, they also present a “curse of dimensionality,” whereby the availability of many gene expression measurements in few samples make it challenging to distinguish noise from true biological signal. All of these factors argue for integrative approaches to microarray data analysis, which combine data from multiple experiments to increase sample size, avoid laboratory-specific bias, and enable new biological insights not possible from a single experiment. Here, we discuss several approaches to integrative microarray analysis for a diverse range of applications, including biomarker discovery, gene function and interaction prediction, and regulatory network inference. We also show how, by integrating large microarray compendia with diverse genomic data types, more nuanced biological hypotheses can be explored computationally. This chapter provides overviews and brief descriptions of each of these approaches to microarray integration.

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Acknowledgments

The authors would like to thank the editors of this title for their gracious support, the laboratories of Olga Troyanskaya and Leonid Kruglyak for their valuable input, and all of the members of the Coller and Huttenhower laboratories. This research was supported by PhRMA Foundation grant 2007RSGl9572, NIH/NIGMS 1R01 GM081686, NSF DBI-1053486, NIH grant T32 HG003284, and NIGMS Center of Excellence grant P50 GM071508. H.A.C. was the Milton E. Cassel scholar of the Rita Allen Foundation.

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Authors and Affiliations

  1. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Levi Waldron & Curtis Huttenhower

  2. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Hilary A. Coller

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  1. Levi Waldron
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  2. Hilary A. Coller
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  3. Curtis Huttenhower
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Correspondence to Hilary A. Coller .

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Editors and Affiliations

  1. Norwegian Radium Hospital, Oslo University Hospital, Montebello, Oslo, 0310, Norway

    Junbai Wang

  2. School of Medicine, University of Colorado Denver, E. 17th Avenue 12801, Aurora, 80010, Colorado, USA

    Aik Choon Tan

  3. School of Mathematics and Statistics, University of Glasgow, Glasgow, 3800, United Kingdom

    Tianhai Tian

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Waldron, L., Coller, H.A., Huttenhower, C. (2012). Integrative Approaches for Microarray Data Analysis. In: Wang, J., Tan, A., Tian, T. (eds) Next Generation Microarray Bioinformatics. Methods in Molecular Biology, vol 802. Humana Press. https://doi.org/10.1007/978-1-61779-400-1_11

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  • DOI: https://doi.org/10.1007/978-1-61779-400-1_11

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