This vignette describes the implemented functionality in the pRolocGUI
package. The package is based on the MSnSet
class definitions of MSnbase
(Gatto and Lilley, 2012) and on the functions defined in the package pRoloc
(Breckels, Gatto, Christoforou, Groen, et al., 2013; Gatto, Breckels, Wieczorek, Burger, et al., 2014). pRolocGUI
is intended for the visualisation and analysis of proteomics data, especially for the analyses of LOPIT (Dunkley, Hester, Shadforth, Runions, et al., 2006) or PCP (Foster, Hoog, Zhang, Zhang, et al., 2006) experiments.
To achieve reactivity and interactivity, pRolocGUI
relies on the shiny
framework.
The implemented application facilitates a higher degree of interactivity with the underlying spatial proteomics data: The distributed functions pRolocVis
and pRolocComp
offer interactive Principal Component Analysis (PCA) plots and protein profile plots, as well as exploration of quantitative and qualitative meta-data. Key features of pRolocVis
and pRolocComp
are the identification of features in plots, a 'reverse search' based on querying meta-data which allows for highlighting the features on plots and an import/export functionality by using the FeaturesOfInterest
/FoICollection
infrastructure distributed by the MSnbase
package. Additionally, pRolocComp
allows for comparison of two comparable MSnSet
instances, e.g. this might be of great help for analyses of changes in protein localisation in different MSnSet
s.
We recommend some familiarity with the MSnSet
class (see ?MSnSet
for details) and the pRoloc
vignette (available with vignette("pRoloc-tutorial")
).
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 contact Laurent Gatto (lg390@cam.ac.uk) or Thomas Naake (naake@stud.uni-heidelberg.de) 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 list.
The Display selection widget is probably the most important implementation in pRolocVis /pRolocComp and allows for identifying features. You can do this by selecting points in the PCA plot, clicking on features in the tab protein profiles, using past searches and/or querying for features in the MSnSet data. In pRolocComp there is in addition the possibility to retrieve features from the summary matrix in the tab data . |
There are four (pRolocVis ) or five (pRolocComp ) check boxes in the Display selection widget which represent the before mentioned ways of searching features in the MSnSet . To activate the search for one specific method click on the check box left of its description. It is also possible to select more than one at a time which allows for greater flexibility with regard to information retrieval. To irreversibly reset the selection press Clear features (only shown when features are selected). |
### 3.1. PCA |
If you decide to identify proteins in the PCA plot, change to the tab PCA and start clicking on features in the PCA plot (tip: the zoom function may be of great expedient). When hovering over the PCA plot the feature meta-data of the nearest feature will be displayed below the plot. The check box will be checked when you start clicking in the PCA plot. As soon as you have clicked on a feature it will be marked with a black circle around it (or a blueish if colour is set to none ). If you have selected a feature by accident or want to deselect it, just click again on the feature and it will be deselected. |
Selecting features works also in pRolocComp : just click on features in one of the PCA plots will also highlight the same feature in the other plot if it is present. If you want to analyse features which are only common in the two MSnSet instances, go to tab Data and select common in the radio button list Features used. |
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There are two possibilities to deselect all selected features: If you decide to remove all your features click on Clear features (the button will only show up if features were already selected). Please keep in mind that this step once carried out is irreversible and will delete features selected in protein profiles , (summary matrix ) and query as well. Besides that you are also able to simply blind out the selected features by deselecting the check box left of PCA in the Display selection widget. Internally, the features are still stored, i.e. by clicking again on the check box you will see the selections again. Clicking on new proteins in the PCA plot will not check the check box again, so you have to do this manually. The features selected are shared between the different tabs. Click on the tabs quantitation and feature meta-data to have a look upon information about the selected features. For the case where you see all features in the data table change the radio buttons settings from all to selected at the lowermost widget in the sidebar. Here again, you can compose the features from different sources (PCA, protein profiles, saved searches and the text-based query search). |
If you display protein profiles in the tab protein profiles selected features will be displayed by black lines on all plots drawn. |
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### 3.2. protein profiles |
In principle the search for features in protein profiles is in accordance with the search in the PCA plot. Though, bear in mind that you are only able to select features when 1 is selected in the drop-down list number of plots to display. Hovering over the plot will display the feature meta-data of the nearest protein below the plot. Clicking on (or near) the points in the plot will select, clicking another time will deselect features. The features will only be shown when the check box left of protein profiles is activated. Note, that you can only select or deselect features whose protein profiles are displayed in a transparent manner on the plot. |
For pRolocComp selecting features works in the same way: click on features in one of the protein profile plots (make sure that 1 is selected in the drop-down list number of plots to display), thus highlighting the same feature in the other plot if the same feature name is present. If you want to analyse features which are only common in the two MSnSet instances, go to tab data and select common in the radio button list Features used. |
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### 3.3. saved searches |
Clicking on the check box to the left of saved searches will load the selected features of the class FeaturesOfInterest . These will be displayed in the PCA plot, in the plots for protein profiles (depending on the displayed features) and will be available in the tabs quantitation and feature meta-data for information retrieval. Add FeaturesOfInterest by clicking on the respective features in the tab search in the multiple drop-down list; thus accordingly altering the selected features in the Display selection widget context. Each FeaturesOfInterest instance will be highlighted in a different colour to distinguish easily between them. |
In pRolocVis /pRolocComp there is no functionality implemented to remove features from the object pRolocGUI_SearchResults in the global environment. The authors decided that it is not the task of a GUI to fulfil the requirements of this kind of data manipulation in a GUI, hence, the execution of removing features of interests belongs to the field of the users responsibility. |
### 3.4. summary matrix (pRolocComp only) |
In pRolocComp another way of selecting and displaying features is possible via the tab data. Features can be selected and internally stored by selecting "all"/a marker via the drop-down menu select marker - this will change the selected row - and one of the radio buttons underneath - this will change the column. By pressing the Submit selection button the features comprised in these categories will be stored internally. The button will be only shown when the features are not already stored internally and can be displayed/used in the other tabs. The selected row, column and the number of features which is comprised in these categories will be displayed in bold in the summary matrix. If features were submitted another button will be present which allows to remove the features from the internal selection, the Undo selection button. |
When submitting features from the columns unique1 and unique2 , the feature names will only be saved internally to the correspondent MSnSet and displayed there accordingly. This is done because it is possible that the same feature name exists in the other MSnSet but is not assigned to the organelle . |
As an example, for the two MSnSet instances tan2009r1 and tan2009r2 a selection of markers and markers as marker object 1 and marker object 2, when selecting mitochondrion in select marker and the radio button next to common will be equivalent to and will contain the following features |
The stored features will only displayed when the check box next to summary matrix in the Display selection(#display) widget is selected. |
### 3.5. query feature-meta data |
The Display selection widget offers the opportunity to query the feature meta-data of the MSnSet for levels. The drop-down list consists of the item protein , which will by definition the feature names and depending on the data accession number, protein ID, protein description, assigned markers (varying on the underlying MSnSet ). |
For demonstration purposes we will use pRolocVis to select and display features by using the query functionality. Keep in mind, to adjust the selection of the radio buttons next to the appropriate MSnSet when using pRolocComp : Accordingly to the selected MSnSet the list of feature variables is rendered. |
Let's assume we want to look at andy2011 which was derived from experiments of Breckels et al. (2013) for all proteins which are assigned by experimental evidence to the organelle plasma membrane . We ensure ourselves that andy2011 is selected in the tab data and change to a tab where the Display selection widget is loaded. We select marker in the upper drop-down list (for we are looking for organelles assigned to marker proteins). In the next drop-down list below we select PM which codes for plasma membrane . Next, we click on Submit selection, which will highlight all features which are assigned to PM for the variable name marker (the button only appears in the application when the corresponding proteins do not exist in the selection). To remove the selected features from the internal assignment we have to either reset the search by clicking on Clear features or click on Undo selection. The latter will only remove the current selection of features, while the former will clear all features (also these of PCA, protein profiles (and data). Of course, we can also add other features: If we want to add all features which are assigned to the Golgi apparatus we simply select Golgi in the lower drop-down list and click on Submit selection to save internally the selected features. |
It is relatively easy to find levels when the drop-down list for these levels. But how should we proceed when we want to look for a special protein, e.g. ACADV? The drop-down list for the variable name protein is very long and it is time consuming to scroll through the whole list and look for our protein of interest. Therefore, we can just enter ACADV in the text input field Search for in between the two drop-down lists and we will get the protein of interest (we are also able to query for protein names which have the string AC in their name which will limit the drop-down list to all proteins which have this specific string). By clicking on Submit selection we save internally the selected feature(s). |
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[6] L. Gatto and K. S. Lilley. "MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation". In: Bioinformatics 28.2 (2012), pp. 288-289. DOI: 10.1093/bioinformatics/btr645. URL: http://www.ncbi.nlm.nih.govpubmed/22113085.
[7] D. J. Tan, H. Dvinge, A. Christoforou, P. Bertone, et al. "Mapping organelle proteins and protein complexes in Drosophila melanogaster". In: J. Proteome Res. 8.6 (2009), pp. 2667-2678. DOI: 10.1021/pr800866n. URL: http://pubs.acs.org/doi/abs/10.1021/pr800866n.