recipe bioconductor-spatialheatmap







The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data.

package bioconductor-spatialheatmap

(downloads) docker_bioconductor-spatialheatmap



depends bioconductor-edger:


depends bioconductor-genefilter:


depends bioconductor-s4vectors:


depends bioconductor-scater:


depends bioconductor-scran:


depends bioconductor-scuttle:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-ggplotify:

depends r-gplots:

depends r-gridextra:

depends r-grimport:

depends r-igraph:

depends r-matrix:

depends r-reshape2:

depends r-rsvg:

depends r-shiny:

depends r-shinydashboard:

depends r-spscomps:


depends r-tibble:

depends r-xml2:



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-spatialheatmap

and update with::

   mamba update bioconductor-spatialheatmap

To create a new environment, run:

mamba create --name myenvname bioconductor-spatialheatmap

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

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