Introduction

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND AND IMPLEMENTATION: The proposed method is implemented in the open-source R package HTSCluster, available on CRAN. CONTACT: andrea.rau@jouy.inra.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Publications

  1. Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models.
    Cite this
    Rau A, Maugis-Rabusseau C, Martin-Magniette ML, Celeux G, 2015-05-01 - Bioinformatics (Oxford, England)

Credits

  1. Andrea Rau
    Developer

    INRA, UMR1313 Génétique animale et biologie intégrative, France

  2. Cathy Maugis-Rabusseau
    Developer

    INRA, UMR1313 Génétique animale et biologie intégrative, France

  3. Marie-Laure Martin-Magniette
    Developer

    INRA, UMR1313 Génétique animale et biologie intégrative, France

  4. Gilles Celeux
    Investigator

    INRA, UMR1313 Génétique animale et biologie intégrative, France

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Summary
AccessionBT001286
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionFrance
Submitted ByGilles Celeux