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ProteoMM

This is the development version of ProteoMM; for the stable release version, see ProteoMM.

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform


Bioconductor version: Development (3.19)

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).

Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed

Maintainer: Yuliya V Karpievitch <yuliya.k at gmail.com>

Citation (from within R, enter citation("ProteoMM")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("ProteoMM")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("ProteoMM")
Multi-Dataset Model-based Differential Expression Proteomics Platform HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews DifferentialExpression, ImmunoOncology, MassSpectrometry, Normalization, Proteomics, Software
Version 1.21.0
In Bioconductor since BioC 3.8 (R-3.5) (5.5 years)
License MIT
Depends R (>= 3.5)
Imports gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics
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Suggests BiocStyle, knitr, rmarkdown
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package ProteoMM_1.21.0.tar.gz
Windows Binary ProteoMM_1.21.0.zip
macOS Binary (x86_64) ProteoMM_1.21.0.tgz
macOS Binary (arm64) ProteoMM_1.21.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/ProteoMM
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/ProteoMM
Bioc Package Browser https://code.bioconductor.org/browse/ProteoMM/
Package Short Url https://bioconductor.org/packages/ProteoMM/
Package Downloads Report Download Stats