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Sequential model selection‐based segmentation to detect DNA copy number variation
Author(s) -
Hu Jianhua,
Zhang Liwen,
Wang Huixia Judy
Publication year - 2016
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12478
Subject(s) - copy number variation , segmentation , selection (genetic algorithm) , variation (astronomy) , model selection , computer science , computational biology , biology , artificial intelligence , evolutionary biology , statistics , pattern recognition (psychology) , mathematics , genetics , genome , gene , physics , astrophysics
Summary 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.