recipe bioconductor-beer

Bayesian Enrichment Estimation in R






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

(downloads) docker_bioconductor-beer



depends bioconductor-biocparallel:


depends bioconductor-edger:


depends bioconductor-phipdata:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-cli:

depends r-progressr:

depends r-rjags:



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<tag>

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

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