recipe bioconductor-gsbenchmark

Gene Set Benchmark

Homepage:

https://bioconductor.org/packages/3.18/data/experiment/html/GSBenchMark.html

License:

GPL-2

Recipe:

/bioconductor-gsbenchmark/meta.yaml

Benchmarks for Machine Learning Analysis of the Gene Sets. The package contains a list of pathways and gene expression data sets used in "Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC)" (2010) by Eddy et al.

package bioconductor-gsbenchmark

(downloads) docker_bioconductor-gsbenchmark

versions:
1.22.0-01.20.0-01.18.0-01.14.0-11.14.0-01.12.0-01.10.0-11.10.0-01.9.0-0

1.22.0-01.20.0-01.18.0-01.14.0-11.14.0-01.12.0-01.10.0-11.10.0-01.9.0-01.8.0-01.6.0-01.4.0-21.4.0-11.2.0-0

depends bioconductor-data-packages:

>=20231203

depends curl:

depends r-base:

>=4.3,<4.4.0a0

requirements:

Installation

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-gsbenchmark

and update with::

   mamba update bioconductor-gsbenchmark

To create a new environment, run:

mamba create --name myenvname bioconductor-gsbenchmark

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 quay.io/biocontainers/bioconductor-gsbenchmark:<tag>

(see `bioconductor-gsbenchmark/tags`_ for valid values for ``<tag>``)

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