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

Andrea Rau1, Cathy Maugis-Rabusseau2, Marie-Laure Martin-Magniette3

  • 1INRA, UMR1313 Génétique animale et biologie intégrative, Jouy-en-Josas, France, AgroParisTech, UMR1313 Génétique animale et biologie intégrative, Paris 05, France, Institut de Mathématiques de Toulouse, INSA de Toulouse, Université de Toulouse, Toulouse, France, UMR AgroParisTech/INRA MIA 518, Paris, France, INRA, UMR 1165 URGV, Saclay Plant Sciences, Evry, France, UEVE, UMR URGV, Saclay Plant Sciences, Evry, France, CNRS, ERL 8196, URGV, Saclay Plant Sciences, Evry, France and Inria Saclay - Île-de-France, Orsay, France INRA, UMR1313 Génétique animale et biologie intégrative, Jouy-en-Josas, France, AgroParisTech, UMR1313 Génétique animale et biologie intégrative, Paris 05, France, Institut de Mathématiques de Toulouse, INSA de Toulouse, Université de Toulouse, Toulouse, France, UMR AgroParisTech/INRA MIA 518, Paris, France, INRA, UMR 1165 URGV, Saclay Plant Sciences, Evry, France, UEVE, UMR URGV, Saclay Plant Sciences, Evry, France, CNRS, ERL 8196, URGV, Saclay Plant Sciences, Evry, France and Inria Saclay - Île-de-France, Orsay, France.

Bioinformatics (Oxford, England)
|January 8, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new Poisson mixture model for clustering digital gene expression (DGE) profiles. The method effectively identifies co-expressed genes in RNA-seq data and is available as an R package.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • High-throughput sequencing technologies are widely used for gene expression studies.
  • Statistical methods for digital gene expression (DGE) analysis, particularly normalization and differential analysis, are areas of active research.

Purpose of the Study:

  • To develop a robust statistical method for clustering DGE profiles to identify co-expressed genes.
  • To provide a framework for parameter estimation and determining the optimal number of clusters.

Main Methods:

  • A Poisson mixture model is proposed for clustering DGE profiles.
  • Parameter estimation and cluster number selection are rigorously addressed.
  • The method is evaluated using real RNA-seq datasets and simulation studies.

Main Results:

  • The proposed Poisson mixture model effectively clusters DGE profiles.
  • Co-expression analyses are demonstrated on two real RNA-seq datasets.
  • Performance comparisons with existing methods show the model's efficacy.

Conclusions:

  • The developed Poisson mixture model offers a reliable approach for co-expressed gene discovery in DGE data.
  • The method is implemented in the open-source R package HTSCluster, facilitating its use in the research community.