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Microarray Data Normalization: The Art and Science of Overcoming Technical Variance to Maximize the Detection of Biologic Variance

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A Beginner’s Guide to Microarrays

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Abstract

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A central goal in the analysis of microarray data is to identify and characterize genes and gene groups that exhibit differential and coordinate expression patterns as a function of biological state differences. This would be an easy task indeed if all gene expression measurements obtained from microarrays were perfectly accurate and consistent. Unfortunately, the reality of the situation is quite different. Microarray-based gene expression measurements are affected by a host of unwanted technical errors, linear and non-linear systematic biases, as well as additional or hidden biologic variances that pertain to individual or state variations that are not necessarily those that are to be evaluated in an experiment. Taking these effects all together, observed differences in gene expression levels between two samples for a given gene can be represented as the sum of two components: technical variance and biological variance. The goals of microarray data normalization are to identify and strip out as much technical variation as possible such that the most accurate expression levels associated with different biological states can be determined. Ideally, one would hope to understand the expression of each gene in relation to all other genes on a microarray, across a series of microarrays, as well as to those measured by other technologies, in other experiments, and in relation to the activity of the entire genome. In other words, exact absolute gene expression levels for all genes under any circumstance. Short of this, optimal normalization techniques seek to maximize the usability of all measurements per array and per array series. Correcting for any technical variation thus allows for the highest resolution view of biologically based variations in gene expression. Following optimal normalization procedures, the multiple origins of biological variation can be dissected in detail.

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Author information

Authors and Affiliations

  1. Center for Environmental Genetics, Department of Environmental Health, College of Medicine, University of Cincinnati, USA

    Maureen A. Sartor M.S. & Mario Medvedovic Ph.D.

  2. Developmental Biology and Pediatric Informatics, Children’ s Hospital Medical Center, University of Cincinnati, USA

    Bruce J. Aronow PhD

Authors
  1. Maureen A. Sartor M.S.
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  2. Mario Medvedovic Ph.D.
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  3. Bruce J. Aronow PhD
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Editor information

Editors and Affiliations

  1. University of Kentucky Medical Center, USA

    Eric M. Blalock

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Sartor, M.A., Medvedovic, M., Aronow, B.J. (2003). Microarray Data Normalization: The Art and Science of Overcoming Technical Variance to Maximize the Detection of Biologic Variance. In: Blalock, E.M. (eds) A Beginner’s Guide to Microarrays. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8760-0_5

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  • DOI: https://doi.org/10.1007/978-1-4419-8760-0_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4684-5

  • Online ISBN: 978-1-4419-8760-0

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