recipe bioconductor-cytofqc

Labels normalized cells for CyTOF data and assigns probabilities for each label






cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.

package bioconductor-cytofqc

(downloads) docker_bioconductor-cytofqc



depends bioconductor-catalyst:


depends bioconductor-flowcore:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-e1071:

depends r-eztune:

depends r-gbm:

depends r-ggplot2:

depends r-hrbrthemes:

depends r-matrixstats:

depends r-randomforest:

depends r-rmarkdown:

depends r-ssc:



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-cytofqc

and update with::

   mamba update bioconductor-cytofqc

To create a new environment, run:

mamba create --name myenvname bioconductor-cytofqc

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

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