ProteoMM

DOI: 10.18129/B9.bioc.ProteoMM  

Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Bioconductor version: Release (3.16)

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 "4.2") 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 1.16.0
In Bioconductor since BioC 3.8 (R-3.5) (4.5 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_1.16.0.tar.gz
Windows Binary ProteoMM_1.16.0.zip
macOS Binary (x86_64) ProteoMM_1.16.0.tgz
macOS Binary (arm64) ProteoMM_1.16.0.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

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