pRoloc: A unifying bioinformatics framework for spatial proteomics

The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.

Annotating spatial proteomics data A transfer learning algorithm for spatial proteomics Bayesian Analysis of Spatial Proteomics data using pRoloc Machine learning techniques available in pRoloc Using pRoloc for spatial proteomics data analysis

Vignettes Man pages API and functions Files

Package details
Author	Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek
Bioconductor views	Classification Clustering ImmunoOncology MassSpectrometry Proteomics QualityControl
Maintainer	Laurent Gatto <[email protected]>
License	GPL-2
Version	1.24.0
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")`