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

Cancer progression, tumor formations, and many genetic diseases are related to aberrations in some chromosomal regions. Chromosomal aberrations are often reflected in DNA copy number changes, also known as copy number variations (CNVs) [1]. To study such chromosomal aberrations, experiments are often conducted based on tumor samples from a cell-line-using technologies such as aCGH or SNP arrays. For instance, in aCGH experiments, a DNA test sample and a diploid reference sample are first fluorescently labeled by Cy3 and Cy5. Then, the samples are mixed and hybridized to the microarray. Finally, the image intensities from the test and reference samples can be obtained for all DNA probes (bio-markers) along the chromosome [2, 3]. The log-base-2 ratios of the test and reference intensities, usually denoted as , are used to generate an aCGH profile [4]. To reduce noise, the Gaussian-smoothed profile is often used. With an appropriate normalization process, is viewed as a Gaussian distribution of mean 0 and variance [4, 5]. The deviation from mean 0 and variance in data may indicate a copy number change. Therefore, detecting DNA copy number changes becomes the problem of how to identify significant parameter changes occurred in the sequence of observations.There are a number of computational and statistical methods developed for the detection of CNVs based on aCGH data and SNP data. Examples include a finite Gaussian mixture model [6], pair wise t-tests [7], adaptive weights smoothing [8], circular binary segmentation (CBS) [4], hidden Markov modeling (HMM) [9], maximum likelihood estimation [10], and many others. A comparison between several of these methods for the analysis of aCGH data was given by Lai et al. [11]. There are continued efforts on developing methods for accurate detection of CNVs. Nannya et al. [12] developed a robust algorithm for copy number analysis of the human genome using high-density oligonucleotide microarrays. Price et al. [13] adapted the Smit