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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.


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1544-6115
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A Comparison of Normalization Techniques for MicroRNA Microarray Data

Youlan Rao1 / Yoonkyung Lee2 / David Jarjoura3 / Amy S Ruppert4 / Chang-gong Liu5 / Jason C Hsu6 / John P Hagan7

1The Ohio State University

2The Ohio State University

3The Ohio State University

4The Ohio State University

5The Ohio State University

6The Ohio State University

7The Ohio State University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1287, July 2008

Publication History

Published Online:
2008-07-21

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.

Keywords: microRNA; median normalization; cyclic loess normalization; quantile normalization; robust estimates; smoothing spline; mean squared error

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