Abstract
Protein microarrays are a high-throughput technology capable of generating large quantities of proteomics data. They can be used for general research or for clinical diagnostics. Bioinformatics and statistical analysis techniques are required for interpretation and reaching biologically relevant conclusions from raw data. We describe essential algorithms for processing protein microarray data, including spot-finding on slide images, Z score, and significance analysis of microarrays (SAM) calculations, as well as the concentration dependent analysis (CDA). We also describe available tools for protein microarray analysis, and provide a template for a step-by-step approach to performing an analysis centered on the CDA method. We conclude with a discussion of fundamental and practical issues and considerations.
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Acknowledgments
C.J.W. acknowledges support from the Department of Defense (W81XWH-07-1-0080), the Miles and Eleanor Shore Award, NCI (5R21CA115043-2), the Early Career Physician-Scientist Award of the Howard Hughes Medical Institute, and is a Damon-Runyon Clinical Investigator supported (in part) by the Damon-Runyon Cancer Research Foundation (CI-38-07). O.M. acknowledges support from a Medical Student Fellowship of the Howard Hughes Medical Institute.
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DeLuca, D.S., Marina, O., Ray, S., Zhang, G.L., Wu, C.J., Brusic, V. (2011). Data Processing and Analysis for Protein Microarrays. In: Wu, C. (eds) Protein Microarray for Disease Analysis. Methods in Molecular Biology, vol 723. Humana Press. https://doi.org/10.1007/978-1-61779-043-0_21
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DOI: https://doi.org/10.1007/978-1-61779-043-0_21
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