pRolocVis: Interactive visualisation of spatial proteomics data

Description

These functions allow one to explore spatial proteomics data interactively.

Usage

pRolocVis(object, app, fcol, legend.cex = 1, ...)
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, ...)
pRolocVis_pca(object, fcol, foi, fig.height = "600px", fig.width = "100%",
  legend.width = "200%", legend.cex = 1, nchar = 40, all = TRUE, ...)

Arguments

object

An instance of class MSnSet, or an MSnSetList of length 2 if using "compare" application.

app

The type of application requested: "main" (default), "classify", "compare".See description below.

fcol

The feature meta-data label (fData column name). This will correspond to the prediction column if using "classify", or the markers (labelled data) to be plotted otherwise.

legend.cex

Point character expansion for the the legend. Default is 1.

...

Additional parameters passed to the respective app.

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

If using the compare app this is the feature meta-data label (fData column name) for the first dataset in the MSnSetList. Default is markers.

fcol2

If using the compare app this is the feature meta-data label (fData column name) for the second dataset in the MSnSetList. Default is markers.

foi

A FeaturesOfInterest or FoICollection object.

fig.height

Height of the figure. Default is "600px".

fig.width

Width of the figure. Default is "100px".

legend.width

Width of the legend. Default is "200%".

remap

A logical indicating whether the second dataset in the MSnSetList should be remapped to the first dataset. Default is

nchar

Maximum number of characters of the markers class names, before their names are truncated. Default is 10.

all

If TRUE all clusters are displayed on startup, if the total number of clusters is less than including 15. If FALSE or otherwise, only the first cluster in the list is displayed.

Value

For classify a numeric vector of thresholds, one per class, to use with getPredictions
For compare and main a character vector of the featureNames of the proteins selected is invisibly returned.
For pca a character of protein names, of the proteins selected upon application closure.

Details

The function pRolocVis is a wrapper for pRolocVis_main, pRolocVis_classify and pRolocVis_compare. These Shiny apps allow to explore and analyse interactively spatial proteomics data. The main 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.

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.

Examples

Run this code

library("pRoloc")
library("pRolocdata")
data(hyperLOPIT2015)
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)
}