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

Andréa Rau | Zendy; Cathy Maugis-Rabusseau | Zendy; Marie-Laure Martin-Magniette | Zendy; Gilles Celeux | Zendy

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

Author(s) -

Andréa Rau,

Cathy Maugis-Rabusseau,

Marie-Laure Martin-Magniette,

Gilles Celeux

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu845

Subject(s) - cluster analysis , computer science , normalization (sociology) , poisson distribution , data mining , context (archaeology) , computational biology , rna seq , data set , set (abstract data type) , transcriptome , gene , gene expression , biology , machine learning , artificial intelligence , statistics , genetics , mathematics , paleontology , sociology , anthropology , programming language

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.

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