recipe bioconductor-scgps

A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)






The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

package bioconductor-scgps

(downloads) docker_bioconductor-scgps



depends bioconductor-deseq2:


depends bioconductor-deseq2:


depends bioconductor-singlecellexperiment:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-caret:


depends r-dplyr:

depends r-dynamictreecut:

depends r-fastcluster:

depends r-ggplot2:


depends r-glmnet:


depends r-locfit:

depends r-rcpp:

depends r-rcpparmadillo:

depends r-rcppparallel:



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-scgps

and update with::

   mamba update bioconductor-scgps

To create a new environment, run:

mamba create --name myenvname bioconductor-scgps

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

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