recipe bioconductor-flowgraph

Identifying differential cell populations in flow cytometry data accounting for marker frequency






Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts.

package bioconductor-flowgraph

(downloads) docker_bioconductor-flowgraph



depends r-base:


depends r-data.table:


depends r-effsize:

depends r-furrr:

depends r-future:

depends r-ggiraph:

depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-htmlwidgets:

depends r-igraph:

depends r-matrix:

depends r-matrixstats:

depends r-purrr:

depends r-rdpack:

depends r-stringi:

depends r-stringr:

depends r-visnetwork:



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-flowgraph

and update with::

   mamba update bioconductor-flowgraph

To create a new environment, run:

mamba create --name myenvname bioconductor-flowgraph

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

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