recipe bioconductor-coseq

Co-Expression Analysis of Sequencing Data






Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.

package bioconductor-coseq

(downloads) docker_bioconductor-coseq



depends bioconductor-biocparallel:


depends bioconductor-deseq2:


depends bioconductor-edger:


depends bioconductor-htsfilter:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-capushe:

depends r-compositions:

depends r-corrplot:

depends r-e1071:

depends r-ggplot2:

depends r-htscluster:

depends r-mvtnorm:

depends r-rmixmod:

depends r-scales:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-coseq

and update with::

   mamba update bioconductor-coseq

To create a new environment, run:

mamba create --name myenvname bioconductor-coseq

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-coseq/tags`_ for valid values for ``<tag>``)

Download stats