recipe bioconductor-flowsom

Using self-organizing maps for visualization and interpretation of cytometry data



GPL (>= 2)




biotools: flowsom, doi: 10.1002/cyto.a.22625

FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees.

package bioconductor-flowsom

(downloads) docker_bioconductor-flowsom



depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-consensusclusterplus:


depends bioconductor-consensusclusterplus:


depends bioconductor-flowcore:


depends bioconductor-flowcore:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-colorramps:

depends r-dplyr:

depends r-ggforce:

depends r-ggnewscale:

depends r-ggplot2:

depends r-ggpubr:

depends r-igraph:

depends r-magrittr:

depends r-rlang:

depends r-rtsne:

depends r-tidyr:

depends r-xml:



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-flowsom

and update with::

   mamba update bioconductor-flowsom

To create a new environment, run:

mamba create --name myenvname bioconductor-flowsom

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

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