pRolocGUI - Interactive visualisation of spatial proteomics data

Lisa Breckels1, Thomas Naake and Laurent Gatto2

1Cambridge Centre for Proteomcs, Cambridge, UK
2de Duve Institute, UCLouvain, Belgium

Package

pRolocGUI 2.0.0

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.

library("pRolocGUI")
library("pRolocdata")

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.

data(hyperLOPIT2015) 

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 pca 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 PCA 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.

Searching the datatable

To select/unselect a protein of interest one can simply click/unclick on the corresponding entry in the table or double click directly on a protein of interest on the interactive PCA plot. If a protein(s) in the table is clicked and selected the row in the table will turn grey and the protein(s) will be highlighted on the PCA plot by a dark grey circle(s), if the ‘Show labels’ box is checked (the default) in the right sidebar panel the protein names for the selected protein(s) will also be shown on the plot. Any selected proteins on the plot or in the table can be cleared at any time by clicking the ‘Clear selection’ button in the right sidebar panel.

Saving selected proteins Once proteins have been highlighted in the table and/or the plot they can be exported using the “Save selection” button in the right sidebar. This will download the ids (as defined by featureNames in the MSnSet object) of the current protein selection to a .csv file.

Zooming If a user wishes to examine a protein(s) in more detail, one can zoom in on specific points by hovering the mouse over the plot, then clicking and drawing a (square) brush and then clicking the ‘Zoom/reset button’ in the right sidebar to zoom to the brushed area. This process can be repeated until the desired level of zoom is reached. The plot can be resetted to the original size by clicking the ‘Zoom/reset button’ once again.

Downloading figures All visualisations in the app (the map and two profile plots) can be downloaded as high resolution PDFs by clicking the “Download Plot” button in the right sidebar panel.

Hiding the sidebar panels The left and right sidebar panels can be shown/hidden at any time by clicking the icons in the main dashboard.

Hiding the sidebars

The profiles tabs There are two profiles tabs in pRolocGUI which display the protein profile quantitation data that is stored in the exprs data slot of the MSnSet. For the hyperLOPIT2015 dataset this is the relative abundances of each protein across the 20 fractions (2 x 10-plex replicates).

The first “Profiles” tab shows two ribbons plots, one for each dataset. As per the Spatial Map tab, the plot is updated according to the input classes selected in the sidebar panel on the left. A ribbon is plotted for each each sub-cellular class between the 5th and 95th percentile value per channel. The mean class profile is also highlighted by a bold line. Unknown/unlabelled profiles are shown as dark gray lines.

The profiles tab is useful to look for discrimination between
different sub-cellular niches in an easy and direct manor where all proteins belonging to the same sub-cellular niche/data cluster (as specified by fcol when the app is launched) are loaded together. The protein distribution patterns can then be examined on a group by group basis. Proteins of interest can be searched in the data table and once clicked, the distribution(s) of selected protein(s) are shown by dotted black lines.

There is a second profiles tab called “Profiles (by class)” which shows the protein profiles faceted by their class labels. This static plot can be useful when comparing the trend between classes, especially when two or more classes have very similar trends.

Profiles faceted by subcellular class

Table Selection The Table Selection tab provides an interface for data table column selection. Multiple columns can be selected on and off by clicking/unclicking the checkboxes that correspond to the columns in the data table.

Customising the table

Sample Information The tab “Sample Info” stores any sample information that is stored in the pData slot of the MSnSet.

the Sample Info Tab

Colour Picker This tab provides an interface to select and set colours for the class labels.

The colour picker

4 The compare application

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 Map plots are loaded side-by-side (the default method is PCA) and one can search and identify common proteins between the two data sets.

A MSnSetList of length 2 must be supplied as input, containing the two datasets one wishes to compare. In the example below we load two replicate datasets of mouse embryonic stem cells produced using the hyperLOPIT technology.

data(hyperLOPIT2015ms3r1)
data(hyperLOPIT2015ms3r2) 
mydata <- MSnSetList(list(hyperLOPIT2015ms3r1, hyperLOPIT2015ms3r2))
pRolocVis(mydata, app = "compare", fcol = "markers") 

This will load the datasets hyperLOPIT2015ms3r1 and hyperLOPIT2015ms3r2 side by side and use the column name called markers for the colour labelling in both plots.

If we pass a fcol of length 2 to the app we can specify different feature data columns by which to the label the dataset. For example, in the proceeding example we load data from a LOPIT-DC experiment, lopitdcU2OS2018, and then a hyperLOPIT experiment, hyperLOPITU2OS2018. If we wish to display the feature data contained in the column called markers for hyperLOPITU2OS2018, but a different set of features for lopitdcU2OS2018, called final.assignment we would specify this using fcol as follows.

data("hyperLOPITU2OS2018")
data("lopitdcU2OS2018")
xx <- MSnSetList(list(hyperLOPITU2OS2018, lopitdcU2OS2018))
if (interactive()) {
  pRolocVis(xx, app = "compare", fcol = c("markers", "final.assignment"))
}

The compare app has the same functionality as the explore application for protein profile visualisation,interactive searchable datatable that allows both batch import and export, colour selection and options to download the visualisations. Visualisations and tables that appear in each tab are loaded side-by-side, one per dataset.

5 The aggregate application

The aggregate app allows users to look both the peptide (and/or PSM) and/or protein level data together and explore the effects of PSM/protein aggregation to protein and identify protein groups with interesting expression patterns.

To run the aggregate app we first load a PSM level dataset from pRolocdata. The dataset hyperLOPIT2015ms2psm contains PSM level intensity data, where each row corresponds to one PSM and each column is the TMT-plex. Please see ?hyperLOPIT2015ms2psm for more information.

We can launch the pRolocVis function and look at the PSM data without aggregating to peptide

## load PSM data
data("hyperLOPIT2015ms2psm")

## Visualise the PSMs per to protein group
pRolocVis(hyperLOPIT2015ms2psm, app = "aggregate", fcol = "markers",
          groupBy = "Protein.Group.Accessions")

Or we can first aggregate from PSM to peptide and then launch the app to look at the relationships between peptide level data and protein groups. For this latter case we can use the combineFeatures function from MSnbase.

## Combine PSM data to peptides
hl <- combineFeatures(hyperLOPIT2015ms2psm,
                      groupBy = fData(hyperLOPIT2015ms2psm)$Sequence,
                      method = median)

## Visualise peptides according to protein group
pRolocVis(hyperLOPIT2015ms2psm, app = "aggregate", fcol = "markers",
          groupBy = "Protein.Group.Accessions")

The aggregate app

The main body of the app contains (1) a aggvar distance plot and a (2) PCA plot of the PSMs/peptides. The aggvar distance plot shows the (log10) number of features (in this example peptides) per protein group and the aggregation summarising distance per protein group. The app uses the function aggvar from MSnbase package.

As described in the ?aggvar documentation, the app, can take max or mean as a function, and this can be selected in the left sidebar panel. By default, on loading the max is calculated. Using max as a function, one can help identify protein groups with single extreme outliers, such as, for example, a mis-identified peptide that was erroneously assigned to that protein group. The mean can also be used as a function to identify more systematic inconsistencies where, for example, the subsets of peptide (or PSM) feautres correspond to proteins with different expression patterns.

Examining peptides and proteins

Both the aggvar plot and PCA plot are interactive, and similarly to the other pRolocVis apps you can click individual proteins or peptides in either the aggvar or PCA plot, to search and highlight peptides and proteins of interest. When a protein group is clicked in the left plot, the peptides and associated protein group are automatically shown on the right PCA plot.

As previously mentioned aggvar can use either max or mean as a function. In the left sidebar there is a drop down menu for users to try each method.

6 References

Gatto L., Vizcaíno J.A., Hermjakob H., Huber W. and Lilley K.S. Organelle proteomics experimental designs and analysis Proteomics, 10:22, 3957-3969, 2010.

Gatto L., Breckels L.M., Burger T., Nightingale D., Groen A.J., Campbell C., Nikolovski N., Mulvey C.M., Christoforou A., Ferro M., Lilley K.S. A foundation for reliable spatial proteomics data analysis, Mol Cell Proteomics. 2014 Aug;13(8):1937-52.

Christoforou A., Mulvey C.M., Breckels L.M., Hayward P.C., Geladaki E., Hurrell T., et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nat Commun. 2016 Jan 12;7:9992.