recipe bioconductor-scone

Single Cell Overview of Normalized Expression data






SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

package bioconductor-scone

(downloads) docker_bioconductor-scone



depends bioconductor-aroma.light:


depends bioconductor-biocparallel:


depends bioconductor-edger:


depends bioconductor-limma:


depends bioconductor-matrixgenerics:


depends bioconductor-rhdf5:


depends bioconductor-ruvseq:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-boot:

depends r-class:

depends r-cluster:

depends r-compositions:

depends r-diptest:

depends r-fpc:

depends r-gplots:

depends r-hexbin:

depends r-matrixstats:

depends r-mixtools:

depends r-rarpack:

depends r-rcolorbrewer:



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-scone

and update with::

   mamba update bioconductor-scone

To create a new environment, run:

mamba create --name myenvname bioconductor-scone

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

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