recipe bioconductor-scater

Single-Cell Analysis Toolkit for Gene Expression Data in R







biotools: scater

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

package bioconductor-scater

(downloads) docker_bioconductor-scater



depends bioconductor-beachmat:


depends bioconductor-biocgenerics:


depends bioconductor-biocneighbors:


depends bioconductor-biocparallel:


depends bioconductor-biocsingular:


depends bioconductor-delayedarray:


depends bioconductor-matrixgenerics:


depends bioconductor-s4vectors:


depends bioconductor-scuttle:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-ggbeeswarm:

depends r-ggplot2:

depends r-ggrastr:

depends r-ggrepel:

depends r-matrix:

depends r-pheatmap:

depends r-rcolorbrewer:

depends r-rcppml:

depends r-rlang:

depends r-rtsne:

depends r-uwot:

depends r-viridis:



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-scater

and update with::

   mamba update bioconductor-scater

To create a new environment, run:

mamba create --name myenvname bioconductor-scater

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

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