recipe bioconductor-proteomm

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Homepage:

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

License:

MIT

Recipe:

/bioconductor-proteomm/meta.yaml

ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009).

package bioconductor-proteomm

versions:

1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0, 

1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0,  1.4.0-0,  1.2.0-1,  1.0.0-0

depends bioconductor-biomart:

>=2.62.0,<2.63.0

depends r-base:

>=4.4,<4.5.0a0

depends r-gdata:

depends r-ggplot2:

depends r-ggrepel:

depends r-gtools:

depends r-matrixstats:

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-proteomm

and update with::

   mamba update bioconductor-proteomm

To create a new environment, run:

mamba create --name myenvname bioconductor-proteomm

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-proteomm:<tag>

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

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