Sequential model selection-based segmentation to detect DNA copy number variation.

ABSTRACT: Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing pop

SUBMITTER: Hu J 

PROVIDER: S-EPMC5018994 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature