HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data

A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).

Version: 2.0.8
Depends: R (≥ 2.10.0)
Imports: edgeR, plotrix, capushe, grDevices, graphics, stats
Suggests: HTSFilter, Biobase
Published: 2016-05-26
Author: Andrea Rau, Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis- Rabusseau
Maintainer: Andrea Rau <andrea.rau at jouy.inra.fr>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: HTSCluster citation info
Materials: NEWS
CRAN checks: HTSCluster results

Downloads:

Reference manual: HTSCluster.pdf
Vignettes: Co-expression analysis of RNA-seq data with the "HTSCluster" package
Package source: HTSCluster_2.0.8.tar.gz
Windows binaries: r-devel: HTSCluster_2.0.8.zip, r-release: HTSCluster_2.0.8.zip, r-oldrel: HTSCluster_2.0.8.zip
OS X Mavericks binaries: r-release: HTSCluster_2.0.8.tgz, r-oldrel: HTSCluster_2.0.8.tgz
Old sources: HTSCluster archive

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