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, Lilley, and Trotter, 2013; Gatto, Breckels, Wieczorek, Burger, and Lilley, 2014).
pRolocGUI is intended for the visualisation and analysis of proteomics data,
especially for the analyses of
LOPIT (Dunkley, Hester, Shadforth, Runions, Weimar, Hanton, Griffin, Bessant, Brandizzi, Hawes, Watson, Dupree, and Lilley, 2006)
or PCP (Foster, Hoog, Zhang, Zhang, Xie, Mootha, and Mann, 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 MSnSets.
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.
Currently, pRolocGUI distributes the pRolocVis and pRolocCompapplication.
The function pRolocVis needs an object of class MSnSet or
a list of MSnSet objects as an argument, while pRolocComp needs a list
containing two instances of class MSnSet.
To prepare the environment to run a pRolocVis/pRolocComp session,
the pRolocGUI package and for demonstration purposes four example MSnSets
are loaded to the environment. The example data sets are available from the
pRolocdata (Gatto and Breckels, 2014) experiment
package and are derived from experiments andy2011 from
Breckels, Gatto, Christoforou, et al. (2013), tan2009r1 and tan2009r2,
the first and second replicate from Tan, Dvinge, Christoforou, Bertone, Arias, and Lilley (2009)
and dunkley2006 from Dunkley, Hester, Shadforth, et al. (2006).
library("pRolocGUI")
data(andy2011, package = "pRolocdata")
data(tan2009r1, package = "pRolocdata")
data(tan2009r2, package = "pRolocdata")
data(dunkley2006, package = "pRolocdata")
pRolocVis needs an object of class MSnSet as an argument. We can launch the
application with an MSnSet by assigning it to the argument object:
pRolocVis(object = andy2011)
Alternatively, to upload multiple objects of class MSnSet, pRolocVis
accepts both lists with named and unnamed objects. This allows for analysis
of multiple data sets without stopping the application from running.
The names of objects of lists will appear in the drop-down menu in the
data tab, while lists with unnamed objects will have a drop-down menu
with automatically named entries as a consequence, i.e. object 1 ... object n,
where n is the length of the list.
namedVis <- list(andy2011 = andy2011, tan2009r1 = tan2009r1, dunkley2006 = dunkley2006) unnamedVis <- list(andy2011, tan2009r1, dunkley2006) pRolocVis(object = namedVis) pRolocVis(object = unnamedVis)
pRolocComp requires a list of two MSnSets which can be named or unnamed.
namedComp <- list(tan2009r1 = tan2009r1, tan2009r2 = tan2009r2) unnamedComp <- list(tan2009r1, tan2009r2) pRolocComp(object = namedComp) pRolocComp(object = unnamedComp)
N.B.! It is also possible to use a partly named list in pRolocVis and
pRolocComp.
Launching pRolocVis or pRolocComp will open a new tab in your default
Internet browser.
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/pRolocComp is running.
pRolocVis and pRolocCompTo optimise ease of use the interfaces of pRolocVis and pRolocComp are
subdivided in seven tabs:
You browse through the tabs by simply clicking on them. Each tab selected will have a different kind of appearance while some (PCA, protein profiles, quantitation and feature meta-data) share a common feature in the sidebar, the Display selection widget (see section 3. Display selection widget for further details).
In case you have a question and want to consult the vignette for a certain
issue (e.g. regarding PCA tab or on how to use the Display selection
widget) click on ? which will open the vignette in a new browser tab in the
corresponding section.
In general, the tabs for pRolocVis and pRolocComp will look alike. The tab
data however differs for the two applications. While in pRolocVis this
tab allows for the selection of the used MSnSet and the upload of .Rda or
.Rdata files, in pRolocComp there is the possibility to subset the used
MSnSets (in terms of using common, unique and common & unique features in the
two MSnSets used) as well as to submit features for selection. See
2.7. data for further details.
Fig. 1: Vignette of pRolocVis and pRolocComp
The tab PCA is characterised by its main panel which shows a
PCA plot for the selected MSnSet in the case of pRolocVis
and two PCA plots for pRolocVis. The sidebar panel is divided into
Display selection and Plot.
Fig. 2: Appearance of PCA tab for pRolocVis (andy2011)
For pRolocComp the plot whose appearance is going to altered has to be selected
by selecting the appropriate radio button left of the name of
the MSnSet instance in the sidebar panel.
In addition, pRolocComp offers the possibility to mirror the PCA plot of the
second object. By clicking on x-axis below mirror 2nd object features
are mirrored along the x-axis, while clicking on y-axis mirrors along the
y-axis. This may be important when you compare experiments whose PCA analysis
have different signs.
Fig. 3: Radio buttons to select MSnSet in
pRolocComp. Argument object is a named list with MSnSet instances
tan2009r1 and tan2009r2
The manipulation of the plots works the same way for pRolocVis and for
pRolocComp:
The Display selection widget is described below. The
Plot compartment enables to adjust the appearance of the PCA plot
in the main panel. We are able to colour features (proteins) in
matters of common properties by changing the drop-down list
colour. These properties are the MSnSet's feature
variables. For example if we upload the andy2011 data set in pRolocVis,
and select the colour markers, the features in the PCA plot will be coloured
according to their organelle affiliation. As soon as we select another
colour than none, two (or three) new items will be added to the
Plot widget:
(1) symbol type: By selecting one of the feature variables of the
MSnSet in the drop-down list of symbol type the symbol type of
the features in the plot will be changed.
(2) legend and position of legend: By clicking on the check box to the left of legend a legend is added to the plot and by choosing one of the items in the drop-down list position of legend below its position will be changed.
(3) point size: This drop-down list might appear when numeric feature variable have been identified. The default 1 allows for an unaltered display of the plot, while selecting other items in the list renders the features in the PCA plot according to their numerical value in the variable label (for example classification scores).
Fig. 4: Appearance of PCA tab (andy2011). markers
used for colours, legend added.
By changing the drop-down lists of the items PC along x axis and PC along y axis the x-values and y-values, respectively, the plot will be rendered according to the new principal components.
To zoom in and out drag and drop the little arrows of the slider of the items zoom x-axis and zoom y-axis. This may be of great help when you want to identify points in dense clusters.
By clicking on Download Plot in the main panel below the PCA plot will open a dialog window with an interface on showing or saving the PCA plot as it is displayed in the main panel.
For pRolocVis the tab protein profiles shows the protein profiles in
the main panel (with an option of exporting the plot as it is shown in the main
panel by clicking on the button Download Plot) - for pRolocComp it shows
the plots for the two MSnSet instances (the corresponding plot(s) for the
first element in the list will be displayed on the left,
for the second element on the right). In the sidebar panel there is the
Display selection widget and the Plot widget.
Have a look on section 3. Display selection widget if you want to retrieve information about how to use the Display selection widget.
The Plot widget helps to manipulate the plots shown in the main
panel (in pRolocComp one has first to select the appropriate
radio button next to the MSnSet instance in the sidebar
panel). Let's assume we want to have a look upon the protein profiles
for the proteins from which we know that they belong to the organelles
Endoplasmic reticulum, the Golgi apparatus, Mitochondrion and the
plasma membrane for the andy2011 MSnSet. This is done in pRolocVis, but
works in the same way in pRolocComp. We have four organelles to look
at, so we select 4 (in pRolocComp there is either the possibility to
select 1 or 2 plots per MSnSet) in the drop-down list
number of plots to display. We will select the feature variable
markers in the drop-down list feature(s) in and select ER
(coding for Endoplasmic reticulum)
in the drop-down list underneath (assigned to). To
display the next plot we have to change the slider Selected plot
to position 2. Accordingly to our question we will
change the second drop-down list to Golgi (coding for Golgi apparatus). We
proceed with the two remaining organelles as described before by
changing firstly the slider to the next position and by changing the
drop-down lists accordingly to the organelles we want to
display. Please be aware that it is possible to "go" back to a plot to
change its parameter.
Fig. 5: Appearance of protein profiles tab in pRolocVis
showing protein profiles of organelles/compartments
Endoplasmic reticulum, Golgi apparatus, Mitochondrion and plasma
membrane of markers (andy2011)
The tab quantitation displays the quantitation data for the proteins as a data table.
In the main panel you can change the number of proteins shown per page and search both for proteins (or for the quantitation data). Also, you may sort the proteins by name or the quantitation data by clicking on the arrows on the top of the data table.
In the sidebar panel the Display selection widget is located as well as
radio buttons to display all data or just selected features
(see 3. Display selection for further details). In pRolocComp
there is also another well panel to select the appropriate
radio button next to the name of the MSnSet to show the
respective quantitation data.
Fig. 6: Appearance of quantitation tab
(andy2011) in pRolocVis. Features shown originate from selection made in
the PCA and protein profiles plots
The tab feature meta-data displays the feature meta-data for the proteins as a data table.
The layout of the tab is similar to the quantitation tab
and allows for sorting and querying the feature meta-data of the
selected MSnSet.
The sidebar comprises the Display selection widget and radio buttons to
show all or only selected features (see 3. Display selection
for further details). In pRolocComp there is in addition a set of two
radio buttons which allow to switch the MSnSet shown,
thus, the feature meta-data will be rendered to the selected MSnSet.
Fig. 7: Appearance of feature meta-data tab
(andy2011) in pRolocVis. Features shown originate from selection made in
protein profiles plot
The tab sample meta-data displays the sample meta-data for the experiment, the name of the isotopes used for tagging and the associated fractions.
In pRolocComp select the appropriate radio button next to
the object name in sidebar panel to display the corresponding sample meta-data.
Fig. 8: Appearance of sample meta-data tab
(andy2011) in pRolocVis
The appearance and operation are identical for pRolocVis and pRolocGUI.
pRolocVis and pRolocComp allow to use past search results to display in the
PCA plot, protein profiles and in the tabs quantitation and
feature meta-data (see 3. Display selection
for further details if this is your intention).
This ability requires the object
pRolocGUI_SearchResults in the global environment which is of
class FeaturesOfInterest or FoICollection (enter
?FeaturesOfInterest in the console for further details).
In case this objects exists it will automatically be loaded to
pRolocVis/pRolocComp and its content is displayed in the tab search.
Use the drop-down list in the main panel to browse through the
different features of the FoICollection. To select features and display them
in multiple tabs add them to the field in the multiple drow-down list.
If no object called pRolocGUI_SearchResults exists in the global
environment you still have the possibility to assign FeaturesOfInterest
to an FoICollection which will be assigned to the global environment when
exiting pRolocVis/pRolocComp.
To save features of interest to the object internally you need
to select features and add these to the FoICollection by entering an
appropriate description in the text field (on the sidebar panel, which will
be useful to trace back to the underlying features and does not exist yet in
the FoICollection). Add the selected features to the object
pRolocGUI_SearchResults in the global environment by clicking on
Create new features of interest. You only have the possibility to
add selected features to the FoICollection when you have entered an
appropriate description, i.e. one that doesn't exist yet in the
FoICollection and if you have selected FeaturesOfInterest, otherwise
the button does not show up in the application.
When exiting pRolocVis/pRolocComp the FoICollection will be assigned to
the object pRolocGUI_SearchResults in the global environment.
To create an example object pRolocGUI_SearchResults containing the
first ten features of tan2009r1 run the following commands in the console.
Both traceable and non-traceable FeaturesOfInterest/FoICollection are
usable by pRolocVis/pRolocComp.
data(tan2009r1, package = "pRolocdata")
pRolocGUI_SearchResults <- FoICollection()
newFeat <- FeaturesOfInterest(description = "test_01",
fnames = featureNames(tan2009r1)[1:10],
object = tan2009r1)
pRolocGUI_SearchResults <- addFeaturesOfInterest(newFeat, pRolocGUI_SearchResults)
Fig. 9: Appearance of search tab (andy2011) in
pRolocVis. Search result pRolocVis_Test1 contains one feature of interest
(PGAP1_Human)
The tab data is the last tab for pRolocVis and pRolocComp.
Fig. 10: Appearance of data tab in pRolocVis
For pRolocVis the drop-down menu lists all the names of assigned
MSnSet objects to the function. Depending if a named list -
containing MSnSet instances - is uploaded or not these names will be
used or automatic names will be created (object 1 ... object n, where
n is the length of the list). In addition, the entry upload is
listed at the bottom of the drop-down menu. Selecting upload allows
to use a MSnSet available in a .Rda or .Rdata file after
uploading it to the application. Clicking on Browse... will open
a dialog window with which you can select a file containing a saved
MSnSet and load it to pRolocVis. Make sure if you want to use a
MSnSet from a .Rda or .Rdata file to select upload in the
drop-down menu.
Fig. 11: File upload in pRolocVis
pRolocVis will print a message if there are any conflicts with the
uploaded file. If so, either the assigned object itself or the first
element in the list will be used instead of the uploaded .Rda or
.Rdata file.
When analysing multiple data sets in one pRolocVis session the selected
features will be (irreversibly) deleted when changing from one
MSnSet to the other! It is therefore highly recommended if this
behaviour is not intended to save selected features by using the
functionality to save features in a FoICollection first before changing the
MSnSet (see 2.6. search for further details).
For pRolocComp the main panel shows a summary matrix of common and unique
feature names consisting of common and unique features
for all feature names of the two MSnSet instances (row all) and for
distinct subsets which are defined by the feature variable names. The feature
variable names can be defined by choosing appropriately the drop-down menus
marker object 1 and marker object 2 which are located in the section
Summary matrix in the sidebar panel (enter ?FeatComp to retrieve more
information about the summary matrix). The matrix in the main panel will show
only features for distinct subsets when both drop-down menus do not have none
as their value, otherwise only the row all will be printed. Given
tan2009r1 and tan2009r2 as input for pRolocComp, when selecting markers
both for marker object 1 and marker object 2 the matrix will consist
of six rows (all, unknown, ER, mitochondrion, Golgi, PM). The table
can be interpreted as follows: For the row all we have 545 common features,
i.e. there are 545 features in the two MSnSets which have identical feature
names. In tan2009r1 however, there are 343 feature names which do not exist
in tan2009r1 and 326 feature names are unique for tan2009r2. For markers
the matrix tells us that features were assigned to 4 organelles on an
experimental evidence (ER, mitochondrion, Golgi, PM) for 833 features
in both MSnSets the affiliation to a certain organelle is unknown. In total
20 features are assigned to ER for tan2009r1, therefrom 16 features
are also present in tan2009r2, however, 4 are unique to tan2009r1. The
interpretation of the other rows can be deduced in the same way.
Features can be selected and displayed in the other tabs, e.g. highlighted on the PCA plot. The selection is conducted in the section Selection. It is made by selecting a marker via the drop-down menu select marker and the radio buttons underneath. The selected row, column and the number of features which is comprised in these categories will be displayed in bold in the summary matrix. By pressing Submit selection the features will be saved internally, thus, available for displaying/use in other tabs. The button Submit selection will be only shown when the features are not already stored internally). If the features are selected there will be the button Undo selection which allows to remove features from the internal selection.
Fig. 12: Appearance of data tab in pRolocComp. The row
ER and column unique1 which comprises 4 features are selected
The tab Data in pRolocCompalso allows for subsetting the data sets by
choosing accordingly common, unique and common & unique (default) in the
section Submit MSnSets. The subset is made on the basis of the feature
names of the two MSnSet instances. Selecting common will use only features
which occur in both MSnSets, unique will use no common features and
common & unique will use all feature names for each MSnSet.
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 pRolocCompthere 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).
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.
Fig. 13: Display of selected features in PCA plot
(andy2011) for pRolocVis. The features selected originate from selection
in the PCA plot
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.
Fig. 14: Display of selected features in protein
profiles plot for pRolocVis. The features selected originate from
selection in PCA plot (andy2011)
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.
Fig. 15: Display of selected features in protein
profiles plot for pRolocVis. The features selected originate from selection
in protein profiles plot (andy2011)
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
FeaturesOfInterestinstance 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.
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 MSnSetinstances 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
data(tan2009r1, package = "pRolocdata") data(tan2009r2, package = "pRolocdata") featcomp <- compfnames(x = tan2009r1, y = tan2009r2, fcol1 = "markers", fcol2 = "markers", verbose = FALSE) ## the fourth element in the list bears information about features which are assigned to ## "mitochondrion" in "markers", we can access all common features for tan2009r1 and tan2009r2 by feat <- slot(featcomp[[4]], "common") feat
## [1] "FBgn0010217" "FBgn0016041" "FBgn0011211" "FBgn0011787" "FBgn0038519" ## [6] "FBgn0016120" "FBgn0019957" "FBgn0019644" "FBgn0010100"
markers there are nine common
features comprised in the two data sets which are assigned to Mitochondrion in
the column markers of the feature meta-data. Going to the tab
feature meta-data will list these features as well as meta-data.
The stored features will only displayed when the check box next to
summary matrix in the Display selection(#display) widget is selected.
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).
Fig. 16: Query for proteins in pRolocVis which contain the
string "AC". This narrows the features in the drop-down list accordingly
(andy2011)
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of organelle discovery upon sub-cellular protein localisation".
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10.1016/j.jprot.2013.02.019.
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(2006), pp. 187-199. DOI: 10.1016/j.cell.2006.03.022.
[4] L. Gatto and L. M. Breckels. pRolocdata: Data accompanying the pRoloc package. R package version 1.2.0. 2014.
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