IMR Press / FBE / Volume 2 / Issue 1 / DOI: 10.2741/E80

Frontiers in Bioscience-Elite (FBE) is published by IMR Press from Volume 13 Issue 2 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Cross phenotype normalization of microarray data
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1 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
2 Research Center for Genetic Medicine, Children's National Medical Center, Washington, DC 20010, USA
3 Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA

*Author to whom correspondence should be addressed.

Front. Biosci. (Elite Ed) 2010, 2(1), 171–186;
Published: 1 January 2010

Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normalization across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a comparison study of four normalization approaches, namely, linear regression (LR), Loess regression, invariant ranking (IR) and iterative nonlinear regression (INR) method, for gene expression. Among these four methods, LR and Loess regression methods use all available genes to estimate either a linear or nonlinear normalization function; while IR and INR methods feature some iterative processes to identify invariantly expressed genes (IEGs) for nonlinear normalization. We tested these normalization approaches on three real microarray data sets and evaluated their performance in terms of variance reduction and fold-change preservation. By comparison, we found that (1) LR method exhibits the worst performance in both variance reduction and fold-change preservation, and (2) INR method shows an improved performance in achieving low expression variance across replicates and excellent fold-change preservation for differently expressed genes.

nonlinear regression
gene expression profiling
microarray data analysis
computational bioinformatics
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