The pRolocGUI package is an interactive interface to explore and visualise experimental mass spectrometry-based spatial proteomics data. It relies on the shiny framework for interactive visualisation, the MSnbase package to handle data and metadata and the pRoloc software for spatial proteomics specific data matters. Example spatial data is available in the pRolocdata experiment package.

The pRoloc suite set of software are distributed as part of the R/Bioconductor project and are developed at the Computational Proteomics Unit at the Cambridge Centre for Proteomics labs, at the University of Cambridge.

This document describes the installation of the software, followed by a basic quick start guide for using pRolocGUI to search and visualise the organelle map. Please refer to the respective documentation and vignettes for full details about the software.

If you use these open-source software for your research, please cite:

Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley KS. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics. 2014 May 1;30(9):1322-4. doi:10.1093/bioinformatics/btu013. Epub 2014 Jan 11. PubMed PMID: 24413670; PubMed Central PMCID: PMC3998135.

Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS, Trotter MW. The effect of organelle discovery upon sub-cellular protein localisation. J Proteomics. 2013 Mar 21. doi:pii: S1874-3919(13)00094-8. 10.1016/j.jprot.2013.02.019. [Epub ahead of print] PubMed PMID: 23523639.

Gatto L., Breckels L.M., Burger T, Nightingale D.J.H., Groen A.J., Campbell C., Mulvey C.M., Christoforou A., Ferro M., Lilley K.S. 'A foundation for reliable spatial proteomics data analysis' Mol Cell Proteomics. 2014 May 20.

Github	Bioconductor devel	Bioconductor release

pRolocGUI is written in the R programming language. Before installing the software you need to download R and (optionally) RStudio.

1. Download the latest R release for your operating system from the R website and install it.

2. Optional, but recommended. Download and install the RStudio IDE. RStudio provides a good code editor and excellent integration with the R terminal.

3. Start R or RStudio.

4. Install the Bioconductor packages pRoloc, pRolocdata and pRolocGUI:

pRolocGUI requires R >= 3.1.1 and Bioconductor version >= 3.0. In an R console, type

source("http://www.bioconductor.org/biocLite.R")
biocLite(c("pRoloc", "pRolocdata", "pRolocGUI"))

The development code on github can be installed using biocLite (or install_github). New pre-release features might not be documented or thoroughly tested and could substantially change prior to release. Use at your own risks.

biocLite("ComputationalProteomicsUnit/pRolocGUI")

Before using a package's functionality, it needs to be loaded:

library("pRolocGUI")

We first load data from Christoforou et al 2016 distributed in the pRolocdata package:

library("pRolocdata")
data(hyperLOPIT2015)

There are 3 different visualisation applications currently available: main, classification and compare. These apps are launched using the pRolocVis function and passing object, which is an MSnSet containing the data one wishes to interrogate. One may also specify which app they wish to use by using the app argument, see ?pRolocVis for more details. The default app that is loaded if app is not specified is the main application:

pRolocVis(hyperLOPIT2015)

The graphical interface is described in details in the package vignette that is included in the package itself (get it by typing vignette("pRolocGUI") in R), available by clicking the ? once the interface is loaded or can be consulted online.

• The Bioconductor support forum
• Open a pRolocGUI GitHub issue (requires a free GitHub account).

0. An introduction to Bioconductor
1. A brief introduction to pRolocGUI
2. Downloading and install R
3. Using RStudio
4. Installing the pRolocGUI interface
5. Starting pRolocGUI - This tutorial is for the older legacy applications. New videos will appear shortly for the new applications.
6. Using pRolocGUI to explore and visualise experimental spatial proteomics data - This tutorial is for the older legacy applications. New videos will appear shortly for the new applications.

Tutorial playlist.

• Teaching material
• R and Bioconductor for proteomics web page and package.