Section: New Results

Statistical analysis of genomic data

Participants : Gilles Celeux, Andrea Rau.

Andrea Rau and Gilles Celeux, in collaboration with Marie-Laure Martin-Magniette (URGV and UMR AgroParisTech/INRA MIA 518) and Cathy Maugis-Rabusseau (IMT/INSA Toulouse) have developed a method to cluster digital gene expression observations from high-throughput (HTS) data using Poisson mixture models [44] . The proposed model has the advantage of accounting for the particularities of HTS data and providing straightforward procedures for parameter estimation and model selection. A series of simulation experiments was done to compare the performance of the proposed model to that of previously proposed clustering methods for similar sequence-based data, and the performance of the proposed approach was examined on two real high-throughput sequencing data sets. The R package HTSCluster used to implement the proposed Poisson mixture model has been made freely available on CRAN.