recipe bioconductor-escape

Easy single cell analysis platform for enrichment






A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells.

package bioconductor-escape

(downloads) docker_bioconductor-escape



depends bioconductor-biocparallel:


depends bioconductor-gseabase:


depends bioconductor-gsva:


depends bioconductor-matrixgenerics:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends bioconductor-ucell:


depends r-base:


depends r-broom:

depends r-data.table:

depends r-dplyr:

depends r-ggplot2:

depends r-ggridges:

depends r-matrix:

depends r-msigdbr:

depends r-patchwork:

depends r-reshape2:

depends r-rlang:

depends r-stringr:



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-escape

and update with::

   mamba update bioconductor-escape

To create a new environment, run:

mamba create --name myenvname bioconductor-escape

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

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