scatterplotDonorTargetTest {CellScore} | R Documentation |
This function is called by CellScoreReport to make a scatterplot of test and standard samples (donor and target).
scatterplotDonorTargetTest(test.data, cellscore, index.plot = FALSE)
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. |
This function outputs the plot on the active graphical device and returns invisibly NULL.
## 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)