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Open Access Highly Accessed Research article

Learning smoothing models of copy number profiles using breakpoint annotations

Toby Dylan Hocking1234*, Gudrun Schleiermacher5, Isabelle Janoueix-Lerosey5, Valentina Boeva4, Julie Cappo5, Olivier Delattre5, Francis Bach1 and Jean-Philippe Vert234

Author Affiliations

1 INRIA Sierra project-team, Paris, F-75013, France

2 Centre for computational biology, Mines ParisTech, Fontainebleau, F-77300, France

3 Institut Curie, Paris, France

4 INSERM U900, Paris, F-75248, France

5 INSERM U830, Paris, F-75248, France

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BMC Bioinformatics 2013, 14:164  doi:10.1186/1471-2105-14-164

Published: 22 May 2013

Abstract

Background

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.

Results

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.

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 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.