A segmentation-clustering problem for the analysis of array CGH data

Abstract. 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. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences (or BACs) share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not assess a biological status to the detected segments. We propose a new model for this segmentation-clustering problem, combining a segmentation model with a mixture model. We present an hybrid algorithm to estimate the parameters of the model by maximum likelihood. This algorithm is based on dynamic programming and on the EM algorithm. We also propose to adaptively estimate the number of segments when the number of clusters is fixed. An example of our procedure is presented, based on publicly available data sets