recipe bioconductor-bayesspace

Clustering and Resolution Enhancement of Spatial Transcriptomes






Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

package bioconductor-bayesspace

(downloads) docker_bioconductor-bayesspace



depends bioconductor-biocfilecache:


depends bioconductor-biocsingular:


depends bioconductor-rhdf5:


depends bioconductor-s4vectors:


depends bioconductor-scater:


depends bioconductor-scran:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-assertthat:

depends r-base:


depends r-coda:

depends r-dirichletreg:

depends r-ggplot2:

depends r-matrix:

depends r-mclust:

depends r-purrr:

depends r-rcpp:


depends r-rcpparmadillo:

depends r-rcppdist:

depends r-rcppprogress:

depends r-rcurl:

depends r-scales:

depends r-xgboost:



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-bayesspace

and update with::

   mamba update bioconductor-bayesspace

To create a new environment, run:

mamba create --name myenvname bioconductor-bayesspace

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

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