New normalization methods using support vector machine quantile regression approach in microarray analysis

Authors:
Insuk Sohn;Sujong Kim;Changha Hwang;Jae Won Lee
Affiliations:
Department of Statistics, Korea University, Seoul 136-701, Republic of Korea;Department of Biochemistry, College of Medicine, Hanyang University, Seoul 133-791, Republic of Korea;Division of Information and Computer Sciences, Dankook University, Seoul, 140-714, Republic of Korea;Department of Statistics, Korea University, Seoul 136-701, Republic of Korea
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

There are many sources of systematic variations in cDNA microarray experiments which affect the measured gene expression levels. Print-tip lowess normalization is widely used in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. However, print-tip lowess normalization performs poorly in situations where error variability for each gene is heterogeneous over intensity ranges. We first develop support vector machine quantile regression (SVMQR) by extending support vector machine regression (SVMR) for the estimation of linear and nonlinear quantile regressions, and then propose some new print-tip normalization methods based on SVMR and SVMQR. We apply our proposed normalization methods to previous cDNA microarray data of apolipoprotein AI-knockout (apoAI-KO) mice, diet-induced obese mice, and genistein-fed obese mice. From our comparative analyses, we find that our proposed methods perform better than the existing print-tip lowess normalization method.