ProteoMM

DOI: 10.18129/B9.bioc.ProteoMM    

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Bioconductor version: Release (3.15)

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 "4.2") and enter:

if (!require("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	1.14.0
In Bioconductor since	BioC 3.8 (R-3.5) (4 years)
License	MIT
Depends	R (>= 3.5)
Imports	gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics
LinkingTo
Suggests	BiocStyle, knitr, rmarkdown
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me	mi4p
Links To Me
Build Report

Package Archives

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

Source Package	ProteoMM_1.14.0.tar.gz
Windows Binary	ProteoMM_1.14.0.zip
macOS Binary (x86_64)	ProteoMM_1.14.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/ProteoMM
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/ProteoMM
Package Short Url	https://bioconductor.org/packages/ProteoMM/
Package Downloads Report	Download Stats