recipe r-wgcna

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.



GPL3 / GPL (>= 2)




biotools: wgcna, doi: 10.1186/1471-2105-9-559

package r-wgcna

(downloads) docker_r-wgcna



depends bioconductor-annotationdbi:

depends bioconductor-go.db:

depends bioconductor-impute:

depends bioconductor-preprocesscore:

depends libgcc-ng:


depends libstdcxx-ng:


depends r-base:


depends r-doparallel:

depends r-dynamictreecut:


depends r-fastcluster:

depends r-foreach:

depends r-hmisc:

depends r-matrixstats:


depends r-rcpp:


depends r-robust:

depends r-survival:



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 r-wgcna

and update with::

   mamba update r-wgcna

To create a new environment, run:

mamba create --name myenvname r-wgcna

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

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