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A Bayes Regression Approach to Array-CGH Data

Author

Listed:
  • Wen Chi-Chung

    (National Health Research Institutes, Taiwan)

  • Wu Yuh-Jenn

    (National Health Research Institutes, Taiwan)

  • Huang Yung-Hsiang

    (National Health Research Institutes, Taiwan)

  • Chen Wei-Chen

    (National Health Research Institutes, Taiwan)

  • Liu Shu-Chen

    (National Health Research Institutes, Taiwan)

  • Jiang Shih Sheng

    (National Health Research Institutes, Taiwan)

  • Juang Jyh-Lyh

    (National Health Research Institutes, Taiwan)

  • Lin Chung-Yen

    (National Health Research Institutes, Taiwan)

  • Fang Wen-Tsen

    (National Health Research Institutes, Taiwan)

  • Hsiung Chao Agnes

    (National Health Reserach Institutes, Taiwan)

  • Chang I-Shou

    (National Health Research Institutes, Taiwan)

Abstract

This paper develops a Bayes regression model having change points for the analysis of array-CGH data by utilizing not only the underlying spatial structure of the genomic alterations but also the observation that the noise associated with the ratio of the fluorescence intensities is bigger when the intensities get smaller. We show that this Bayes regression approach is particularly suitable for the analysis of cDNA microarray-CGH data, which are generally noisier than those using genomic clones. A simulation study and a real data analysis are included to illustrate this approach.

Suggested Citation

  • Wen Chi-Chung & Wu Yuh-Jenn & Huang Yung-Hsiang & Chen Wei-Chen & Liu Shu-Chen & Jiang Shih Sheng & Juang Jyh-Lyh & Lin Chung-Yen & Fang Wen-Tsen & Hsiung Chao Agnes & Chang I-Shou, 2006. "A Bayes Regression Approach to Array-CGH Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-22, February.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:3
    DOI: 10.2202/1544-6115.1149
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