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DGEclust: differential expression analysis of clustered count data

Authors :
Vavoulis, Dimitrios V
Francescatto, Margherita
Heutink, Peter
Gough, Julian
Source :
Genome Biology 2015, 16:39
Publication Year :
2014

Abstract

Most published studies on the statistical analysis of count data generated by next-generation sequencing technologies have paid surprisingly little attention on cluster analysis. We present a statistical methodology (DGEclust) for clustering digital expression data, which (contrary to alternative methods) simultaneously addresses the problem of model selection (i.e. how many clusters are supported by the data) and uncertainty in parameter estimation. We show how this methodology can be utilised in differential expression analysis and we demonstrate its applicability on a more general class of problems and higher accuracy, when compared to popular alternatives. DGEclust is freely available at https://bitbucket.org/DimitrisVavoulis/dgeclust<br />Comment: 26 pages, 7 figures

Details

Database :
arXiv
Journal :
Genome Biology 2015, 16:39
Publication Type :
Report
Accession number :
edsarx.1405.0723
Document Type :
Working Paper
Full Text :
https://doi.org/10.1186/s13059-015-0604-6