Package Recipe 'bioconductor-proteomm'

recipe bioconductor-proteomm

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Homepage:: https://bioconductor.org/packages/3.20/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:

1.28.0-0, 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,

1.28.0-0, 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:

on bioconductor-biomart >=2.66.0,<2.67.0
on r-base >=4.5,<4.6.0a0
on r-gdata
on r-ggplot2
on r-ggrepel
on r-gtools
on r-matrixstats

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-proteomm

to add into an existing workspace instead, run:

pixi add bioconductor-proteomm

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-proteomm

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-proteomm

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-proteomm:<tag>

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-proteomm/README.html)