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A Comparison of Normalization Techniques for MicroRNA Microarray Data

Author

Listed:
  • Rao Youlan

    (The Ohio State University)

  • Lee Yoonkyung

    (The Ohio State University)

  • Jarjoura David

    (The Ohio State University)

  • Ruppert Amy S

    (The Ohio State University)

  • Liu Chang-gong

    (The Ohio State University)

  • Hsu Jason C

    (The Ohio State University)

  • Hagan John P

    (The Ohio State University)

Abstract

Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature regarding normalization techniques for mRNA microarray data, there are no publications comparing normalization techniques for microRNA (miRNA) microarray data, which are subject to similar sources of measurement error. In this paper, we compare the performance of cyclic loess, quantile normalization, median normalization and no normalization for a single-color microRNA microarray dataset. We show that the quantile normalization method works best in reducing differences in miRNA expression values for replicate tissue samples. By showing that the total mean squared error are lowest across almost all 36 investigated tissue samples, we are assured that the bias correction provided by quantile normalization is not outweighed by additional error variance that can arise from a more complex normalization method. Furthermore, we show that quantile normalization does not achieve these results by compression of scale.

Suggested Citation

  • Rao Youlan & Lee Yoonkyung & Jarjoura David & Ruppert Amy S & Liu Chang-gong & Hsu Jason C & Hagan John P, 2008. "A Comparison of Normalization Techniques for MicroRNA Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-20, July.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:22
    DOI: 10.2202/1544-6115.1287
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    References listed on IDEAS

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    1. Jun Lu & Gad Getz & Eric A. Miska & Ezequiel Alvarez-Saavedra & Justin Lamb & David Peck & Alejandro Sweet-Cordero & Benjamin L. Ebert & Raymond H. Mak & Adolfo A. Ferrando & James R. Downing & Tyler , 2005. "MicroRNA expression profiles classify human cancers," Nature, Nature, vol. 435(7043), pages 834-838, June.
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    Cited by:

    1. Swanhild U Meyer & Sebastian Kaiser & Carola Wagner & Christian Thirion & Michael W Pfaffl, 2012. "Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-13, June.
    2. Bin Wang & Paul Howel & Skjalg Bruheim & Jingfang Ju & Laurie B Owen & Oystein Fodstad & Yaguang Xi, 2011. "Systematic Evaluation of Three microRNA Profiling Platforms: Microarray, Beads Array, and Quantitative Real-Time PCR Array," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
    3. Bin Wang & Shu-Guang Zhang & Xiao-Feng Wang & Ming Tan & Yaguang Xi, 2012. "Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-12, May.

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