A Comparison of Normalization Techniques for MicroRNA Microarray Data

• Published in: Statistical Applications in Genetics and Molecular Biology Vol. 7; no. 1; p. 22
• Main Authors: Rao, Youlan, Lee, Yoonkyung, Jarjoura, David, Ruppert, Amy S, Liu, Chang-gong, Hsu, Jason C, Hagan, John P
• Format: Journal Article
• Language: English
• Published: Germany bepress 01.01.2008; De Gruyter
• Subjects: Algorithms; Animals; Bias; cyclic loess normalization; Humans; mean squared error; median normalization; Mice; microRNA; MicroRNAs - analysis; MicroRNAs - genetics; Models, Statistical; Normal Distribution; Oligonucleotide Array Sequence Analysis - statistics & numerical data; quantile normalization; Reproducibility of Results; Research Design; RNA, Neoplasm - analysis; RNA, Neoplasm - genetics; robust estimates; smoothing spline
• ISSN: 1544-6115; 1544-6115
• DOI: 10.2202/1544-6115.1287

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. Submitted: February 28, 2007 · Accepted: June 11, 2008 · Published: July 21, 2008 Recommended Citation Rao, Youlan; Lee, Yoonkyung; Jarjoura, David; Ruppert, Amy S.; Liu, Chang-gong; Hsu, Jason C.; and Hagan, John P. (2008) "A Comparison of Normalization Techniques for MicroRNA Microarray Data," Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 1, Article 22. DOI: 10.2202/1544-6115.1287 Available at: http://www.bepress.com/sagmb/vol7/iss1/art22