Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models

Abstract : In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis.[br/] In this work, we focus on the question of clustering DGE profiles as a means to discover groups of co-expressed genes. We propose a Poisson mixture model using a rigorous framework for parameter estimation as well as the choice of the appropriate number of clusters. We illustrate co-expression analyses using our approach on two real RNA-seq datasets. A set of simulation studies also compares the performance of the proposed model with that of several related approaches developed to cluster RNA-seq or serial analysis of gene expression data.[br/] The proposed method is implemented in the open-source R package HTSCluster, available on CRAN.
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Bioinformatics, Oxford University Press (OUP), 2015, 31 (9), pp.1420-1427. 〈10.1093/bioinformatics/btu845〉
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Soumis le : vendredi 4 septembre 2015 - 21:22:40
Dernière modification le : vendredi 4 janvier 2019 - 17:33:38

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Andrea Rau, Cathy Maugis-Rabusseau, Marie-Laure Magniette, Gilles Celeux. Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, Oxford University Press (OUP), 2015, 31 (9), pp.1420-1427. 〈10.1093/bioinformatics/btu845〉. 〈hal-01108821v2〉

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