recipe bioconductor-lisaclust

lisaClust: Clustering of Local Indicators of Spatial Association



GPL (>=2)



lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

package bioconductor-lisaclust

(downloads) docker_bioconductor-lisaclust



depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-spatialexperiment:


depends bioconductor-spicyr:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-class:

depends r-concaveman:

depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-pheatmap:

depends r-purrr:

depends r-spatstat.explore:

depends r-spatstat.geom:

depends r-spatstat.random:

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-lisaclust

and update with::

   mamba update bioconductor-lisaclust

To create a new environment, run:

mamba create --name myenvname bioconductor-lisaclust

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

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