recipe bioconductor-cellbench

Construct Benchmarks for Single Cell Analysis Methods






This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods.

package bioconductor-cellbench

(downloads) docker_bioconductor-cellbench



depends bioconductor-biocfilecache:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-singlecellexperiment:


depends r-assertthat:

depends r-base:


depends r-dplyr:

depends r-glue:

depends r-lubridate:

depends r-magrittr:

depends r-memoise:

depends r-purrr:


depends r-rappdirs:

depends r-rlang:

depends r-tibble:

depends r-tidyr:

depends r-tidyselect:



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-cellbench

and update with::

   mamba update bioconductor-cellbench

To create a new environment, run:

mamba create --name myenvname bioconductor-cellbench

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

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