recipe bioconductor-beer

Bayesian Enrichment Estimation in R

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/beer.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-beer/meta.yaml

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

package bioconductor-beer

versions:

1.10.0-0,  1.6.0-0,  1.4.0-0,  1.2.0-0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0

depends bioconductor-edger:

>=4.4.0,<4.5.0

depends bioconductor-phipdata:

>=1.14.0,<1.15.0

depends bioconductor-summarizedexperiment:

>=1.36.0,<1.37.0

depends r-base:

>=4.4,<4.5.0a0

depends r-cli:

depends r-progressr:

depends r-rjags:

requirements:

additional platforms:

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-beer

and update with::

   mamba update bioconductor-beer

To create a new environment, run:

mamba create --name myenvname bioconductor-beer

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-beer:<tag>

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

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Link to this page

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