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
Biometricsjournal_title
Biometricsauthors
Hu J,Zhang L,Wang HJdoi
10.1111/biom.12478subject
Has Abstractpub_date
2016-09-01 00:00:00pages
815-26issue
3eissn
0006-341Xissn
1541-0420journal_volume
72pub_type
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