1 Foreword

pRolocGUI is under active development; current functionality is evolving and new features will be added. This software is free and open-source. You are invited to open issues in the Github pRolocGUI repository in case you have any questions, suggestions or have found any bugs or typos. To reach a broader audience for more general questions about proteomics analyses using R consider of writing to the Bioconductor Support Forum.

2 Introduction

This vignette describes the implemented functionality of the pRolocGUI package. The package is based on the MSnSet class definitions of MSnbase and on the functions defined in the pRoloc package. pRolocGUI is intended for, but not limited to, the interactive visualisation and analysis of quantitative spatial proteomics data. To achieve reactivity and interactivity, pRolocGUI relies on the shiny framework. We recommend some familiarity with the MSnSet class (see ?MSnSet for details) and the pRoloc vignette (see vignette("pRoloc-tutorial")) before using pRolocGUI.

There are 3 applications distributed with pRolocGUI which are wrapped and launched by the pRolocVis function. These 3 applications are called according to the argument app in the pRolocVis function which may be one of “explore”, “compare” or “aggregate”.

  • The explore application launches a interactive spatial map (dimensionality reduction) of the data, with an alternate profiles tab for visualisation of protein profiles. There is a searchable data table for the identification of proteins of interest and functionality to download figures and export proteins of interest.
  • The compare application features the same functionality as the explore app but allows the comparison of two MSnSet instances, e.g. this might be of help for the analyses of changes in protein localisation in different conditions.
  • The aggregate application allows users to load peptide or PSM level data and look at the relationship between peptides and proteins (following aggregation).

2.1 Getting started

Once R is started, the first step to enable functionality of the package is to load it, as shown in the code chunk below. We also load the pRolocdata data package, which contains quantitative proteomics datasets.


We begin by loading the dataset hyperLOPIT2015 from the pRolocdata data package. The data was produced from using the hyperLOPIT technology on mouse E14TG2a embryonic stem cells (Christoforou et al 2016). For more background spatial proteomics data anlayses please see Gatto et al 2010, Gatto et al 2014 and also the pRoloc tutorial vignette.


To load one of the applications using the pRolocVis function and view the data you are required to specify a minimum of one key argument, object, which is the data to display and must be of class MSnSet (or a MSnSetList of length 2 for the compare application). Please see vignette("pRoloc-tutorial") or vignette("MSnbase-io") for importing and loading data. The argument app tells the pRolocVis function what type of application to load. One can choose from: "explore" (default), "compare" or "aggregate". The optional argument fcol is used to specify the feature meta-data label(s) (fData column name(s)) to be plotted, the default is markers (i.e. the labelled data). For the the compare app this can be a character of length 2, where the first element is the label for dataset 1 and the second element is for dataset 2 (if only one element is provide this label will be used for both datasets, more detail is provided in the examples further below.)

For example, to load the default pRolocVis application:

pRolocVis(object = hyperLOPIT2015, fcol = "markers")

Launching any of the pRolocVis applications will open a new tab in a separate pop-up window, and then the application can be opened in your default Internet browser if desired, by clicking the ‘open in browser’ button in the top panel of the window.

To stop the applications from running press Esc or Ctrl-C in the console (or use the “STOP” button when using RStudio) and close the browser tab, where pRolocVis is running.

2.2 Which app should I use?

There are 3 different applications, each one designed to address a different specific user requirement.

  • The explore app is intended for exploratory data analysis, which features a clickable interface and zoomable spatial map. The default spatial map is in the form of a PCA plot, but many other dimensionality reduction techniques are supported including t-SNE and MDS among others. If you would like to search for a particular protein or set of proteins this is the application to use. This app also features a protein profiles tab, designed for examining the patterns of user-specified sets of proteins. For example, if one has several overlapping sub-cellular clusters in their data, as highlighted by the PCA plot or otherwise, one can check for separation in all data dimensions by examining the protein profile patterns. Proteins that co-localise are known to exhibit similar distributions (De Duve’s principale).

  • The comparison application may be of interest if a user wishes to examine two replicate experiments, or two experiments from different conditions etc. Two spatial maps are loaded side-by-side and one can search and identify common proteins between the two data sets. As per the default application there is also a protein profiles tab to allow one to look at the patterns of protein profiles of interest in each dataset.

  • The aggregate app is for examining the effect that peptide or PSM aggregation may have on the protein level data.

3 The explore application

The explore (default) app is characterised by an interactive and searchable spatial map, by default this is a Principal Components Analysis (PCA) plot. PCA is an ordinance method that can be used to transform a high-dimensional dataset into a smaller lower-dimenensional set of uncorrelated variables (principal components), such that the first principal component has the largest possible variance to account for as much variability in the data as possible. Each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to the preceding components. Thus, PCA is particularly useful for visualisation of multidimensional data in 2-dimensions, wherein all the proteins can be plotted on the same figure. Other dimensionality reduction methods are supported such as t-SNE, among others (please see ?plot2D and the argument method)

The application is subdivided in to different tabs: (1) Spatial Map, (2) Profiles, (3) Profiles (by class), (4) Table Selection, (5) Sample info and (6) Colour picker. A searchable data table containing the experimental feature meta-data is permanantly dispalyed at the bottom of the screen for ease. You can browse between the tabs by simply clicking on them at the top of the screen.

To run the explore application using pRolocVis:

pRolocVis(object = hyperLOPIT2015, fcol = "markers")

The explore Tab

Viewing The Spatial Map tab is characterised by its main panel which shows a PCA plot for the selected MSnSet. By default a PCA plot is used to display the data and the first two principal components are plotted. The left sidebar panel controls what class labels (sub-cellular compartments) to highlight on the PCA plot. Labels can be selected by clicking on and off the coloured data class names, or removed/highlighted by clicking the “Select/clear all” button. The right sidebar contains the map controls. This features a ‘transparancy’ slider to control the opacity of the highlighted data points, and other buttons which are in detail below.

Searching Below the spatial map is a searchable data table containing the fetaure meta data (fData). For LOPIT experiments, such as the one used in this example, this may contain protein accession numbers, protein entry names, protein description, the number of quantified peptides per protein, and columns containing sub-cellular localisation information.

One can search for proteins of interest by using the white search box, above the table. Searching is done by partial pattern matching with table elements. Any matches or partial text matches that are found are highlighted in the data table. The search supports batch searching so users can paste their favourite sets of proteins, protein accessions/keywords must be separated by spaces.