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

Authors
Sohn, InsukKim, SujongHwang, ChanghaLee, Jae Won
Issue Date
15-Apr-2008
Publisher
ELSEVIER SCIENCE BV
Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.52, no.8, pp.4104 - 4115
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume
52
Number
8
Start Page
4104
End Page
4115
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/123730
DOI
10.1016/j.csda.2008.02.006
ISSN
0167-9473
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. (c) 2008 Elsevier B.V. All rights reserved.
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