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 popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

journal_name

Biometrics

journal_title

Biometrics

authors

Hu J,Zhang L,Wang HJ

doi

10.1111/biom.12478

subject

Has Abstract

pub_date

2016-09-01 00:00:00

pages

815-26

issue

3

eissn

0006-341X

issn

1541-0420

journal_volume

72

pub_type

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