A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process

Jie Chen, Ayten Yiğiter, Yu-Ping Wang, Hong-Wen Deng
2010 EURASIP Journal on Bioinformatics and Systems Biology  
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
more » ... 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.
doi:10.1155/2010/268513 pmid:20976296 pmcid:PMC3171362 fatcat:trkquxsfvjdu5auivepwomhami