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Open Access Highly Accessed Open Badges Research article

A statistical approach for array CGH data analysis

Franck Picard1*, Stephane Robin1*, Marc Lavielle2, Christian Vaisse3 and Jean-Jacques Daudin1

Author Affiliations

1 Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France

2 Université Paris Sud, Equipe Probabilités, Statistique et Modélisation, Orsay, France

3 University of California San Francisco, Diabetes Center, San Francisco, USA

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BMC Bioinformatics 2005, 6:27  doi:10.1186/1471-2105-6-27

Published: 11 February 2005



Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile.


We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context.


Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.