pRoloc: A unifying bioinformatics framework for spatial proteomics
Version 1.16.1

This package implements pattern recognition techniques on quantitiative mass spectrometry data to infer protein sub-cellular localisation.

Package overview A transfer learning algorithm for spatial proteomics HUPO 2011 poster: pRoloc -- A unifying bioinformatics framework for organelle proteomics HUPO 2014 poster: A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data Machine learning techniques available in pRoloc pRoloc tutorial Annotating spatial proteomics data

Vignettes Man pages API and functions Files

Package details
Author	Laurent Gatto and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek
Bioconductor views	Classification Clustering MassSpectrometry Proteomics QualityControl
Maintainer	Laurent Gatto <lg390@cam.ac.uk>
License	GPL-2
Version	1.16.1
URL	https://github.com/lgatto/pRoloc
Package repository	View on Bioconductor
Installation	Install the latest version of this package by entering the following in R: `source("https://bioconductor.org/biocLite.R") biocLite("pRoloc")`