title: Joint segmentation, calling, and normalization of multiple CGH profiles.
abstract: The statistical analysis of array comparative genomic hybridization (CGH) data has now shifted to the joint assessment of copy number variations at the cohort level. Considering multiple profiles gives the opportunity to correct for systematic biases observed on single profiles, such as probe GC content or the so-called "wave effect." In this article, we extend the segmentation model developed in the univariate case to the joint analysis of multiple CGH profiles. Our contribution is multiple: we propose an integrated model to perform joint segmentation, normalization, and calling for multiple array CGH profiles. This model shows great flexibility, especially in the modeling of the wave effect that gives a likelihood framework to approaches proposed by others. We propose a new dynamic programming algorithm for break point positioning, as well as a model selection criterion based on a modified bayesian information criterion proposed in the univariate case. The performance of our method is assessed using simulated and real data sets. Our method is implemented in the R package cghseg.
authors.affiliation: Laboratoire de Biometrie et Biologie Evolutive, UMR CNRS 5558 - Univ. Lyon 1, F-69622, Villeurbanne, France. franck.picard@univ-lyon1.fr
authors.forename: Franck
authors.initials: F
authors.lastname: Picard
authors.forename: Emilie
authors.initials: E
authors.lastname: Lebarbier
authors.forename: Mark
authors.initials: M
authors.lastname: Hoebeke
authors.forename: Guillem
authors.initials: G
authors.lastname: Rigaill
authors.forename: Baba
authors.initials: B
authors.lastname: Thiam
authors.forename: Stéphane
authors.initials: S
authors.lastname: Robin
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D055028:Comparative Genomic Hybridization / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical