Segmentation and estimation for SNP microarrays: a Bayesian multiple change-point approach.

ABSTRACT: High-density single-nucleotide polymorphism (SNP) microarrays provide a useful tool for the detection of copy number variants (CNVs). The analysis of such large amounts of data is complicated, especially with regard to determining where copy numbers change and their corresponding values. In this article, we propose a Bayesian multiple change-point model (BMCP) for segmentation and estimation of SNP microarray data. Segmentation concerns separating a chromosome into regions of equal copy number differences between the sample of interest and some reference, and involves the detection of locations of copy number difference changes. Estimation concerns determining true copy number for each segment. Our approach not only gives posterior estimates for the parameters of interest, namely locations

SUBMITTER: Tai YC 

PROVIDER: S-EPMC3766751 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature