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A Bayes regression approach to array-CGH data.

Chi-Chung Wen·Yuh-Jenn Wu·Yung-Hsiang Huang·Wei-Chen Chen·Shu-Chen Liu·Shih Sheng Jiang·Jyh-Lyh Juang·Chung-Yen Lin·Wen-Tsen Fang·Chao Agnes Hsiung·I-Shou Chang
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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.

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Bayes TheoremChromosome AberrationsDNAFluorescent DyesGenomicsHumansModels, StatisticalOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionRegression AnalysisJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Chi-Chung Wen, Yuh-Jenn Wu, Yung-Hsiang Huang, Wei-Chen Chen, Shu-Chen Liu, Shih Sheng Jiang, Jyh-Lyh Juang, Chung-Yen Lin, Wen-Tsen Fang, Chao Agnes Hsiung, I-Shou Chang. 2006-02-16. A Bayes regression approach to array-CGH data.. https://doi.org/10.2202/1544-6115.1149

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