recipe bioconductor-proda

Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-proda/meta.yaml

Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.

package bioconductor-proda

(downloads) docker_bioconductor-proda

versions:

1.16.0-01.14.0-01.12.0-01.8.0-01.6.0-01.4.0-11.4.0-01.2.0-01.0.0-0

depends bioconductor-biocgenerics:

>=0.48.0,<0.49.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-extradistr:

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-proda

and update with::

   mamba update bioconductor-proda

To create a new environment, run:

mamba create --name myenvname bioconductor-proda

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-proda:<tag>

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

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