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:	2.0.11
Depends:	R (≥ 2.10.0)
Imports:	edgeR, plotrix, capushe, grDevices, graphics, stats
Suggests:	HTSFilter, Biobase
Published:	2023-09-05
DOI:	10.32614/CRAN.package.HTSCluster
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:	README, NEWS
In views:	Omics
CRAN checks:	HTSCluster results

Documentation:

Reference manual:	HTSCluster.pdf
Vignettes:	Co-expression analysis of RNA-seq data with the "HTSCluster" package (source, R code)

Downloads:

Package source:	HTSCluster_2.0.11.tar.gz
Windows binaries:	r-devel: HTSCluster_2.0.11.zip, r-release: HTSCluster_2.0.11.zip, r-oldrel: HTSCluster_2.0.11.zip
macOS binaries:	r-release (arm64): HTSCluster_2.0.11.tgz, r-oldrel (arm64): HTSCluster_2.0.11.tgz, r-release (x86_64): HTSCluster_2.0.11.tgz, r-oldrel (x86_64): HTSCluster_2.0.11.tgz
Old sources:	HTSCluster archive

Reverse dependencies:

Reverse imports:

coseq

Linking:

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