A bayes regression approach to array-CGH data

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

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations


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.

Original languageEnglish
Pages (from-to)i-20
JournalStatistical Applications in Genetics and Molecular Biology
Issue number1
StatePublished - 2006


  • Change point problem
  • Comparative genomic hybridization
  • DNA copy number imbalance


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