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
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.
This vignette describes the implemented functionality of the
pRolocGUI package. The package is based on the
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
pRoloc vignette (see
vignette("pRoloc-tutorial")) before using
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
function which may be one of “pca”, “classify” or
pcaapplication launches a Principal Components Analysis (PCA) plot of the data, with an alternate profiles tab for visualisation of protein profiles, it also features a searchable data table for the identification of proteins of interest.
classifyapplication has been designed to view machine learning classification results according to user-specified thresholds for the assignment of sub-cellular location.
compareapplication allows the comparison of two comparable
MSnSetinstances, e.g. this might be of help for the analyses of changes in protein localisation in different conditions.
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
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
object, which is the data to display and must be of
MSnSet (or a
length 2 for the
compare application). Please see
importing and loading data. The argument
app tells the
function what type of application to load. One can choose
"compare". The optional
fcol2 for the compare app)
can be used which allows the user 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 PCA and compare
For the classification app one must specify the prediction column i.e. the
feature meta-data label that corresponds to the column containing the
classification results, generated from running a supervised machine
learning analysis (see below).
For example, to load the default
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
Ctrl-C in the
console (or use the “STOP” button when using RStudio) and close the
browser tab, where
pRolocVis is running.
There are 3 different applications, each one designed to address a different specific user requirement.
pRoloctutorial for details on spatial proteomics data analysis).
pca, default, application is characterised by an interactive and
searchable 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.
The application is subdivided in to three tabs: (1) PCA, (2) Profiles, and (3) Table Selection. 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
pca application using
pRolocVis(object = hyperLOPIT2015, fcol = "markers")
Viewing The PCA 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 sidebar panel controls what features to highlight on the PCA plot.
Under the ‘Labels’ menu, input can be selected by clicking on and off
the data class names, or by typing and searching in the white input
box. Selected items can then be deleted, by clicking on the name of the
class and pressing the delete button on your keyboard. The PCA plot will
then be updated accordingly. Below the select box is a ‘transparancy’ slider
bar which controls the opacity of the highlighted data classes and two action
buttons ‘Zoom/reset plot’ and ‘Clear selection’, which are described below.