recipe bioconductor-fcoex

FCBF-based Co-Expression Networks for Single Cells






The fcoex package implements an easy-to use interface to co-expression analysis based on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is adapted from the CEMiTool package, relying on visualizations and code designed and written by CEMiTool's authors.

package bioconductor-fcoex

(downloads) docker_bioconductor-fcoex



depends bioconductor-clusterprofiler:


depends bioconductor-fcbf:


depends bioconductor-pathwaypca:


depends bioconductor-singlecellexperiment:


depends r-base:


depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-igraph:

depends r-intergraph:

depends r-matrix:

depends r-network:

depends r-progress:

depends r-scales:

depends r-sna:

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-fcoex

and update with::

   mamba update bioconductor-fcoex

To create a new environment, run:

mamba create --name myenvname bioconductor-fcoex

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

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