recipe bioconductor-spatialfeatureexperiment

Integrating SpatialExperiment with Simple Features in sf






A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

package bioconductor-spatialfeatureexperiment

(downloads) docker_bioconductor-spatialfeatureexperiment



depends bioconductor-biocgenerics:


depends bioconductor-biocneighbors:


depends bioconductor-biocparallel:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-spatialexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-matrix:

depends r-rjson:

depends r-rlang:

depends r-sf:

depends r-spdep:


depends r-terra:



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-spatialfeatureexperiment

and update with::

   mamba update bioconductor-spatialfeatureexperiment

To create a new environment, run:

mamba create --name myenvname bioconductor-spatialfeatureexperiment

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

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