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A Bayes Regression Approach to Array-CGH Data

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 and Chang I-Shou
Additional contact information
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

Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-22

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.

Date: 2006
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DOI: 10.2202/1544-6115.1149

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