A Bayes regression approach to array-CGH data.
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.
Wen
Chi-Chung
CC
National Health Research Institutes, Taiwan.
Wu
Yuh-Jenn
YJ
Huang
Yung-Hsiang
YH
Chen
Wei-Chen
WC
Liu
Shu-Chen
SC
Jiang
Shih Sheng
SS
Juang
Jyh-Lyh
JL
Lin
Chung-Yen
CY
Fang
Wen-Tsen
WT
Hsiung
Chao Agnes
CA
Chang
I-Shou
IS
D001499:Bayes Theorem; D002869:Chromosome Aberrations; D004247:DNA / Q000032:analysis; D005456:Fluorescent Dyes; D023281:Genomics / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016133:Polymerase Chain Reaction; D012044:Regression Analysis
D005456:Fluorescent Dyes; D004247:DNA