Publication Date:
2013-05-23
Description:
Background; Many models have been proposed to detect copy number alterations in chromosomal copynumber profiles, but it is usually not obvious to decide which is most effective for a givendata set. Furthermore, most methods have a smoothing parameter that determines the numberof breakpoints and must be chosen using various heuristics.Results; We present three contributions for copy number profile smoothing model selection. First, wepropose to select the model and degree of smoothness that maximizes agreement with visualbreakpoint region annotations. Second, we develop cross-validation procedures to estimatethe error of the trained models. Third, we apply these methods to compare 17 smoothingmodels on a new database of 575 annotated neuroblastoma copy number profiles, which wemake available as a public benchmark for testing new algorithms.Conclusions; Whereas previous studies have been qualitative or limited to simulated data, our annotation-guided approach is quantitative and suggests which algorithms are fastest and most accuratein practice on real data. In the neuroblastoma data, the equivalent pelt.n and cghseg.k meth-ods were the best breakpoint detectors, and exhibited reasonable computation times.
Electronic ISSN:
1471-2105
Topics:
Biology
,
Computer Science
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