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

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  • Sohn, Insuk
  • Kim, Sujong
  • Hwang, Changha
  • Lee, Jae Won

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.

Suggested Citation

  • Sohn, Insuk & Kim, Sujong & Hwang, Changha & Lee, Jae Won, 2008. "New normalization methods using support vector machine quantile regression approach in microarray analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 4104-4115, April.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:8:p:4104-4115
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    Cited by:

    1. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.
    2. Allison, David B. & Visscher, Peter M. & Rosa, Guilherme J.M. & Amos, Christopher I., 2009. "Statistical genetics & statistical genomics: Where biology, epistemology, statistics, and computation collide," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1531-1534, March.
    3. Hu, Jianming & Tang, Jingwei & Lin, Yingying, 2020. "A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization," Renewable Energy, Elsevier, vol. 149(C), pages 141-164.
    4. Songfeng Zheng, 2014. "A generalized Newton algorithm for quantile regression models," Computational Statistics, Springer, vol. 29(6), pages 1403-1426, December.

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