A Bayesian Analysis of Copy Number Variations in Array Comparative Genomic Hybridization Data

Abstract

Array Comparative Genomic Hybridization (CGH) has been widely used for detecting genomic copy number variations (CNVs). The central goal of array CGH data analysis is to accurately detect homogeneous regions of log intensity ratios which represent relative changes in DNA copy number. Various methods have been proposed in recent years. Most methods, however, do not consider correlations of neighboring probe measurements, and are usually designed for analysis at single sample level rather than detecting common or recurrent CNVs among multiple samples. We propose a Bayesian segment-based approach for efficient analysis of array CGH data. The proposed method is based on simple assumptions but is general enough to accommodate various spatial correlations among probe measurements. It also allows for multiple samples with recurrent CNVs, therefore is able to borrow strength across samples. In contrast to another probe-based approach developed in the same Bayesian framework, the segment-based approach parameterizes the mean log intensity ratios in a more appropriate way, which leads to a posterior sampling scheme based on reversible-jump Markov chain Monte Carlo. We perform a simulation study to compare these two approaches and the commonly-used circular binary segmentation method and Bayesian hidden Markov model method. The segment-based approach achieves better estimation accuracy and higher computational efficiency compared to the probe-based approach, and also provides improved results compared to the other two methods, especially for data with relatively low signal to noise ratio and high correlation. The segment-based approach is further applied to the Corriel cell lines data and Pancreatic Adenocarcinoma data.