pRolocVis {pRolocGUI} | R Documentation |
These functions allow one to explore spatial proteomics data interactively.
pRolocVis(object, app = "pca", fcol, ...) pRolocVis_aggregate(object, fcol, groupBy, fig.height = "600px", fig.width = "100%", legend.width = "200%", legend.cex = 1, nchar = 40, all = TRUE, mirrorX = FALSE, mirrorY = FALSE, ...) pRolocVis_classify(object, fcol, scol, mcol = "markers", legend.cex = 1, ...) pRolocVis_compare(object, fcol1, fcol2, foi, fig.height = "600px", fig.width = "100%", legend.width = "200%", legend.cex = 1, remap = TRUE, nchar = 40, all = TRUE, mirrorX = FALSE, mirrorY = FALSE, ...) pRolocVis_pca(object, fcol = "markers", foi, fig.height = "600px", fig.width = "100%", legend.width = "200%", legend.cex = 1, nchar = 40, all = TRUE, ...)
object |
An instance of class |
app |
The type of application requested: |
fcol |
The feature meta-data label ( |
... |
Additional parameters passed to |
groupBy |
The feature meta-data label ( |
fig.height |
Height of the figure. Default is |
fig.width |
Width of the figure. Default is |
legend.width |
Width of the legend. Default is
|
legend.cex |
Point character expansion for the the legend. Default is 1. |
nchar |
Maximum number of characters of the markers class names, before their names are truncated. Default is 10. |
all |
If |
mirrorX |
Should the first PC of the second |
mirrorY |
Should the second PC of the second |
scol |
The feature meta data column containing the classification scores. |
mcol |
The feature meta data column containing the labelled training data, for use with "classify". |
fcol1 |
In yhe |
fcol2 |
In the |
foi |
A |
remap |
A |
The function pRolocVis
is a wrapper for
pRolocVis_pca
, pRolocVis_classify
,pRolocVis_compare
.
and pRolocVis_aggregate
. These Shiny apps allow to explore and
analyse interactively spatial proteomics data.
The pca
Shiny app allows exploration of quantitative data
(1) visually through Principle Component Analysis (PCA), (2)
protein profiles, and (3) a searchable feature data table,
allowing visualisation of particular proteins of interest. (NOTE:
This
issue
on GitHub shows how to use pre-computed coodinates prior to
visualisation with the app.)
The classify
Shiny app is used to visualise classification
results and set user-specified thresholds for sub-cellular
location predictions.
The compare
Shiny app is meant for comparing protein
localisation between two conditions, or two different experiments,
replicates etc. Please note that passing the argument method
to ...
will not work as it is already specified internally.
The aggregation
Shiny app displays a scatter plot of the
maximum or mean distances within each feature (e.g. protein group)
according to its components (e.g. peptides) defined by the
groupBy
argument. A PCA plot of the components is also
displayed. It can be used for visualising peptides, PSMs or any
other features defined in the feature data of the MSnSet
and their distributions.
For the classify
app a numeric
vector of thresholds,
one per class, to use with getPredictions
For the pca
, compare
and aggregate
apps
a character
vector of featureNames
names of the
object
loaded that have been selected in the app
upon application closure.
Laurent Gatto, Lisa Breckels and Thomas Naake
The package vignette: vignette("pRolocGUI")
.
library("pRoloc") library("pRolocdata") data(hyperLOPIT2015) ## Load the PCA app if (interactive()) { pRolocVis(hyperLOPIT2015) } ## Load classification results from hyperLOPIT stored in fData if (interactive()) { myThreshold <- pRolocVis(hyperLOPIT2015, app = "classify", fcol = "svm.classification", scol = "svm.score") newPredictions <- getPredictions(hyperLOPIT2015, fcol = "svm.classification", scol = "svm.score", t = myThreshold) } ## Visualise the location and distribution of peptides per protein group data("hyperLOPIT2015ms2psm") if (interactive()) { ## Combine PSM data to peptides hl <- combineFeatures(hyperLOPIT2015ms2psm, groupBy = fData(hyperLOPIT2015ms2psm)$Sequence, fun = median) ## Visualise peptides according to protein group pRolocVis(hl, app = "aggregate", fcol = "markers", groupBy = "Protein.Group.Accessions") }