scatterplotDonorTargetTest: scatterplotDonorTargetTest

In CellScore: Tool for Evaluation of Cell Identity from Transcription Profiles

Description

This function is called by CellScoreReport to make a scatterplot of test and standard samples (donor and target).

Usage

scatterplotDonorTargetTest(test.data, cellscore, index.plot = FALSE)

Arguments

test.data	a data.frame of CellScore values as calculated by CellScore(), for a group of test samples.
cellscore	a data.frame of CellScore values as calculated by CellScore().
index.plot	a logical variable, with TRUE meaning sample index should be plotted for easy identification of spots. Default is FALSE.

Value

This function outputs the plot on the active graphical device and returns invisibly NULL.

Examples

## Not run: 
## Load the expression set for the standard cell types
library(Biobase)
library(hgu133plus2CellScore) # eset.std

## Locate the external data files in the CellScore package
rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore")
tsvdata.path <- system.file("extdata", "cell_change_test.tsv",
                             package = "CellScore")

if (file.exists(rdata.path) && file.exists(tsvdata.path)) {

   ## Load the expression set with normalized expressions of 48 test samples
   load(rdata.path)

   ## Import the cell change info for the loaded test samples
   cell.change <- read.delim(file= tsvdata.path, sep="\t",
                             header=TRUE, stringsAsFactors=FALSE)

   ## Combine the standards and the test data
   eset <- combine(eset.std, eset48)

   ## Generate the on/off scores for the combined data
   individ.OnOff <- OnOff(eset, cell.change, out.put="individual")

   ## Generate cosine similarity for the combined data
   ## NOTE: May take 1-2 minutes on the full eset object
   cs <- CosineSimScore(eset, cell.change, iqr.cutoff=0.05)

   ## Generate the CellScore values for all samples
   cellscore <- CellScore(eset, cell.change, individ.OnOff$scores,
                          cs$cosine.samples)
   ## Get the CellScore fvalues rom valid transitions defined by cell.change
   ## table
   plot.data <- extractTransitions(cellscore, cell.change)

   ## Define a plot group variable
   plot.data$plot_group <- paste(plot.data$experiment_id,
                                plot.data$cxkey.subcelltype, sep="_")
   ## Sort the scores 1) by target 2) by donor 3) by study
   plot.data.ordered <- plot.data[order(plot.data$target,
                                       plot.data$donor_tissue,
                                       plot.data$experiment_id), ]

   ## How many plot_groups are there?
   table(plot.data$plot_group)

   ## pick one plot_group to plot
   group <- unique(plot.data$plot_group)[4]

   ## Select scores for only one plot group
   test.data <- plot.data.ordered[plot.data.ordered$plot_group %in% group, ]

   ## save current graphical parameters
 old.par <- par(no.readonly=TRUE)

   ## Plot: this will plot a 2-paneled plot
 par(mfrow=c(1,2))
   scatterplotDonorTargetTest(test.data, cellscore, FALSE)
 
   ## Reset graphical parameters
   par(old.par)
   
}

## End(Not run)

CellScore documentation built on Nov. 8, 2020, 8:11 p.m.