Learning smoothing models of copy number profiles using breakpoint annotations

Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chose...

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Bibliographic Details
Published in:BMC bioinformatics 2013-05, Vol.14 (1), p.164-164, Article 164
Main Authors: Hocking, Toby Dylan, Schleiermacher, Gudrun, Janoueix-Lerosey, Isabelle, Boeva, Valentina, Cappo, Julie, Delattre, Olivier, Bach, Francis, Vert, Jean-Philippe
Format: Article
Language:English
Subjects:
Algorithms
Biology
Cancer
Chromosome Breakpoints
Chromosome Mapping - methods
Dynamic programming
Gene Dosage - genetics
Gene Expression Profiling - methods
Gene mutations
Genetic algorithms
Genetic variation
Humans
Mathematical models
Models, Genetic
Physiological aspects
Studies
Tumors
Values
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Summary:Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chosen using various heuristics. We present three contributions for copy number profile smoothing model selection. First, we propose to select the model and degree of smoothness that maximizes agreement with visual breakpoint region annotations. Second, we develop cross-validation procedures to estimate the error of the trained models. Third, we apply these methods to compare 17 smoothing models on a new database of 575 annotated neuroblastoma copy number profiles, which we make available as a public benchmark for testing new algorithms. 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 accurate in practice on real data. In the neuroblastoma data, the equivalent pelt.n and cghseg.k methods were the best breakpoint detectors, and exhibited reasonable computation times.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-14-164