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An Introduction to High-Throughput Bioinformatics Data

Keith A. Baggerly
Kevin R. Coombes
Jeffrey S. Morris, The University of Texas M.D. Anderson Cancer Center

Abstract

High throughput biological assays supply thousands of measurements per sample, and the sheer amount of related data increases the need for better models to enhance inference. Such models, however, are more effective if they take into account the idiosyncracies associated with the specific methods of measurement: where the numbers come from. We illustrate this point by describing three different measurement platforms: microarrays, serial analysis of gene expression (SAGE), and proteomic mass spectrometry.

Suggested Citation

Keith A. Baggerly, Kevin R. Coombes, and Jeffrey S. Morris. "An Introduction to High-Throughput Bioinformatics Data" Bayesian Inference in Gene Expression and Proteomics. Ed. KA Do, P Mueller, M Vannucci. New York: Cambridge University Press, 2006. 1-33.
Available at: http://works.bepress.com/jeffrey_s_morris/15

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