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Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments

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Statistical Methods for Microarray Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 972))

Abstract

Data quality is intrinsically influenced by design, technical, and analytical parameters. Quality parameters have not yet been well defined for gene expression analysis by microarrays, though ad interim, following recommended good experimental practice guidelines should ensure generation of reliable and reproducible data. Here we summarize essential practical recommendations for experimental design, technical considerations, feature annotation issues, and standardization efforts.

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Authors and Affiliations

  1. Genomics, Biomarker Development, Spheromics, Kontiolahti, Joensuu, Finland

    Andreas Scherer

  2. Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, University of Michigan, Ann Arbor, MI, USA

    Manhong Dai

  3. Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, University of Michigan, Ann Arbour, MI, USA

    Fan Meng

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  1. Andreas Scherer
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  2. Manhong Dai
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  3. Fan Meng
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Correspondence to Andreas Scherer .

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Editors and Affiliations

  1. School of Medicine & Dentistry, Dept. Biostatistics & Computational, University of Rochester, Elmwood Ave. 601, Rochester, 14642, New York, USA

    Andrei Y. Yakovlev

  2. , Department of Probability and Statistics, Charles University, Sokolovska 83, Prague, 18675, Czech Republic

    Lev Klebanov

  3. State University of New York at Buffalo, Main St - 706 Kimball Tower 3435, Buffalo, 14214, New York, USA

    Daniel Gaile

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Scherer, A., Dai, M., Meng, F. (2013). Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_10

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  • DOI: https://doi.org/10.1007/978-1-60327-337-4_10

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