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Activity Number: 571
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305539
Title: Clustering High-Throughput Sequencing Data with Poisson Mixture Models
Author(s): Andrea Rau*+ and Gilles Celeux and Marie-Laure Martin-Magniette and Cathy Maugis-Rabusseau
Companies: INRA GABI and Inria Saclay and INRA URGV/AgroParisTech and IMT/INSA
Address: , Jouy en Josas, International, , France
Keywords: Mixture models ; Clustering ; Co-expression ; RNA-seq ; HTSCluster (R package)

In recent years gene expression studies have increasingly made use of next generation sequencing technology, and in turn, research concerning the appropriate statistical methods for the analysis of digital gene expression has flourished. In this work, we focus on the question of clustering digital gene expression profiles as a means to discover groups of co-expressed genes. As in previous methods defined for the clustering of serial analysis of gene expression (SAGE) data, we use Poisson loglinear models to cluster count-based measures of gene expression; however, rather than using such a model to define a distance metric to be used in a K-means or hierarchical clustering algorithm, we make use of finite mixtures of Poisson loglinear models. This framework has the advantage of providing straightforward procedures for parameter estimation and model selection, as well as an a posteriori probability for each gene of belonging to each cluster. A set of simulation studies compares the performance of the proposed model with that of two previously proposed approaches for SAGE data. We also study the performance of the proposed Poisson mixture model on real high-throughput sequencing data.

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