recipe bioconductor-peacoqc

Peak-based selection of high quality cytometry data



GPL (>=3)



This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data.

package bioconductor-peacoqc

(downloads) docker_bioconductor-peacoqc



depends bioconductor-complexheatmap:


depends bioconductor-flowcore:


depends bioconductor-flowworkspace:


depends r-base:


depends r-circlize:

depends r-ggplot2:

depends r-gridextra:

depends r-plyr:



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-peacoqc

and update with::

   mamba update bioconductor-peacoqc

To create a new environment, run:

mamba create --name myenvname bioconductor-peacoqc

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

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