recipe bioconductor-regenrich

Gene regulator enrichment analysis



GPL (>= 2)



This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking.

package bioconductor-regenrich

(downloads) docker_bioconductor-regenrich



depends bioconductor-biocparallel:


depends bioconductor-biocset:


depends bioconductor-deseq2:


depends bioconductor-dose:


depends bioconductor-fgsea:


depends bioconductor-limma:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-dplyr:

depends r-ggplot2:


depends r-magrittr:

depends r-randomforest:

depends r-reshape2:

depends r-tibble:

depends r-wgcna:



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-regenrich

and update with::

   mamba update bioconductor-regenrich

To create a new environment, run:

mamba create --name myenvname bioconductor-regenrich

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

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