recipe bioconductor-cytoviewer

An interactive multi-channel image viewer for R






This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads.

package bioconductor-cytoviewer

(downloads) docker_bioconductor-cytoviewer



depends bioconductor-cytomapper:


depends bioconductor-ebimage:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-archive:

depends r-base:


depends r-colourpicker:

depends r-rcolorbrewer:

depends r-shiny:

depends r-shinycssloaders:

depends r-shinydashboard:

depends r-svglite:

depends r-svgpanzoom:

depends r-viridis:



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-cytoviewer

and update with::

   mamba update bioconductor-cytoviewer

To create a new environment, run:

mamba create --name myenvname bioconductor-cytoviewer

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

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