recipe bioconductor-bayseq

Empirical Bayesian analysis of patterns of differential expression in count data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/baySeq.html

License:

GPL-3

Recipe:

/bioconductor-bayseq/meta.yaml

This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

package bioconductor-bayseq

(downloads) docker_bioconductor-bayseq

versions:
2.36.0-02.31.0-12.31.0-02.28.0-02.26.0-02.24.0-12.24.0-02.22.0-02.20.0-0

2.36.0-02.31.0-12.31.0-02.28.0-02.26.0-02.24.0-12.24.0-02.22.0-02.20.0-02.18.0-12.16.0-02.14.0-02.12.0-02.10.0-0

depends bioconductor-edger:

>=4.0.0,<4.1.0

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends r-abind:

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-bayseq

and update with::

   mamba update bioconductor-bayseq

To create a new environment, run:

mamba create --name myenvname bioconductor-bayseq

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-bayseq:<tag>

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

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