Journal Article

A personalized microRNA microarray normalization method using a logistic regression model

Bin Wang, Xiao-Feng Wang, Paul Howell, Xuemin Qian, Kun Huang, Adam I. Riker, Jingfang Ju and Yaguang Xi

in Bioinformatics

Volume 26, issue 2, pages 228-234
Published in print January 2010 | ISSN: 1367-4803
Published online November 2009 | e-ISSN: 1460-2059 | DOI:
A personalized microRNA microarray normalization method using a logistic regression model

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Motivation: MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. Its significant effects have contributed to a number of critical biological events including cell proliferation, apoptosis development, as well as tumorigenesis. High-dimensional genomic discovery platforms (e.g. microarray) have been employed to evaluate the important roles of miRNAs by analyzing their expression profiling. However, because of the small total number of miRNAs and the absence of well-known endogenous controls, the traditional normalization methods for messenger RNA (mRNA) profiling analysis could not offer a suitable solution for miRNA analysis. The need for the establishment of new adaptive methods has come to the forefront.

Results: Locked nucleic acid (LNA)-based miRNA array was employed to profile miRNAs using colorectal cancer cell lines under different treatments. The expression pattern of overall miRNA profiling was pre-evaluated by a panel of miRNAs using Taqman-based quantitative real-time polymerase chain reaction (qRT-PCR) miRNA assays. A logistic regression model was built based on qRT-PCR results and then applied to the normalization of miRNA array data. The expression levels of 20 additional miRNAs selected from the normalized list were post-validated. Compared with other popularly used normalization methods, the logistic regression model efficiently calibrates the variance across arrays and improves miRNA microarray discovery accuracy.

Availability: Datasets and R package are available at


Journal Article.  5559 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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