A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process. (17th August 2010)

• Record Type: Journal Article
• Authors: Chen Chen, Jie Jie; Yiğiter Yiğiter, Ayten Ayten; Wang Wang, Yu-Ping Yu-Ping; Deng Deng, Hong-Wen Hong-Wen
• Other Names: Wang Wang Yue Joseph Yue Joseph Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2010(2010)
• Publication Date: 2010-08-17
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2010/268513
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.