recipe bioconductor-nnsvg

Scalable identification of spatially variable genes in spatially-resolved transcriptomics data






Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.

package bioconductor-nnsvg

(downloads) docker_bioconductor-nnsvg



depends bioconductor-biocparallel:


depends bioconductor-singlecellexperiment:


depends bioconductor-spatialexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-brisc:

depends r-matrix:

depends r-matrixstats:



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-nnsvg

and update with::

   mamba update bioconductor-nnsvg

To create a new environment, run:

mamba create --name myenvname bioconductor-nnsvg

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

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