recipe bioconductor-cytomem

Marker Enrichment Modeling (MEM)






MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD.

package bioconductor-cytomem

(downloads) docker_bioconductor-cytomem



depends bioconductor-flowcore:


depends r-base:


depends r-gplots:

depends r-matrixstats:



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-cytomem

and update with::

   mamba update bioconductor-cytomem

To create a new environment, run:

mamba create --name myenvname bioconductor-cytomem

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

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