recipe bioconductor-amountain

Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach



GPL (>= 2)




biotools: amountain, doi: 10.1101/056952

A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach.

package bioconductor-amountain

(downloads) docker_bioconductor-amountain



depends gsl:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:




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-amountain

and update with::

   mamba update bioconductor-amountain

To create a new environment, run:

mamba create --name myenvname bioconductor-amountain

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

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