recipe bioconductor-dcanr

Differential co-expression/association network analysis






This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease).

package bioconductor-dcanr

(downloads) docker_bioconductor-dcanr



depends r-base:


depends r-circlize:

depends r-dorng:

depends r-foreach:

depends r-igraph:

depends r-matrix:

depends r-plyr:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-stringr:



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-dcanr

and update with::

   mamba update bioconductor-dcanr

To create a new environment, run:

mamba create --name myenvname bioconductor-dcanr

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-dcanr/tags`_ for valid values for ``<tag>``)

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