README.md
In lgatto/pRolocGUI: Interactive visualisation of spatial proteomics data

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Exploring and visualising spatial proteomics data

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 by Lisa Breckels at the Cambridge Centre for Proteomics at the University of Cambridge and by Laurent Gatto, director of the Computational Biology and Bioinformatics (CBIO) group at UCLouvain, in Belgium.

This document describes the installation of the software, followed by a basic quick start guide for using pRolocGUI to search and visualise spatial proteomics data. 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. PMID:24413670; 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. 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.

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

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install(c("pRoloc", "pRolocdata", "pRolocGUI"))

Development version

The development code on github can also be installed using BiocManager::install (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.

BiocManager::install("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: explore, compare and aggregate. 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 explore application:

pRolocVis(hyperLOPIT2015)

Screenshot - PCA

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.