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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

Online
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1544-6115
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Volume 5, Issue 1

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Volume 1 (2002)

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
Published Online: 2006-02-16 | DOI: https://doi.org/10.2202/1544-6115.1149

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.

Keywords: change point problem; comparative genomic hybridization; DNA copy number imbalance

About the article

Published Online: 2006-02-16


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 5, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1149.

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