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Integrative Approaches for Microarray Data Analysis

  • Levi Waldron
  • Hilary A. CollerEmail author
  • Curtis Huttenhower
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Microarray Meta-analysis Bioinformatics Coexpression Functional interaction networks Biomolecular networks Bayesian networks Regulatory networks Protein function prediction MEFIT COALESCE 

Notes

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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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Levi Waldron
    • 1
  • Hilary A. Coller
    • 2
    Email author
  • Curtis Huttenhower
    • 1
  1. 1.Department of BiostatisticsHarvard School of Public HealthBostonUSA
  2. 2.Department of Molecular BiologyPrinceton UniversityPrincetonUSA

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