ProteoMM

DOI: 10.18129/B9.bioc.ProteoMM    

This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see ProteoMM.

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Bioconductor version: 3.13

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>

Installation

To install this package, start R (version "4.1") and enter:

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

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")

 

HTML	R Script	Multi-Dataset Model-based Differential Expression Proteomics Platform
PDF		Reference Manual
Text		NEWS

Details

biocViews	DifferentialExpression, ImmunoOncology, MassSpectrometry, Normalization, Proteomics, Software
Version	1.10.0
In Bioconductor since	BioC 3.8 (R-3.5) (3 years)
License	MIT
Depends	R (>= 3.5)
Imports	gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics
LinkingTo
Suggests	BiocStyle, knitr, rmarkdown
SystemRequirements
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URL
Depends On Me
Imports Me
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Build Report

Package Archives

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

Source Package	ProteoMM_1.10.0.tar.gz
Windows Binary	ProteoMM_1.10.0.zip
macOS 10.13 (High Sierra)	ProteoMM_1.10.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/ProteoMM
Source Repository (Developer Access)	git clone [email protected]:packages/ProteoMM
Package Short Url	https://bioconductor.org/packages/ProteoMM/
Package Downloads Report	Download Stats