recipe bioconductor-spicyr

Spatial analysis of in situ cytometry data



GPL (>=2)



spicyR provides a series of functions to aid in the analysis of both immunofluorescence and mass cytometry imaging data as well as other assays that can deeply phenotype individual cells and their spatial location.

package bioconductor-spicyr

(downloads) docker_bioconductor-spicyr



depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-spatialexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-concaveman:

depends r-data.table:

depends r-dplyr:

depends r-ggforce:

depends r-ggplot2:

depends r-lme4:

depends r-lmertest:

depends r-mgcv:

depends r-pheatmap:

depends r-rlang:

depends r-scam:

depends r-spatstat.explore:

depends r-spatstat.geom:

depends r-tidyr:



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-spicyr

and update with::

   mamba update bioconductor-spicyr

To create a new environment, run:

mamba create --name myenvname bioconductor-spicyr

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

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