rugplotDonorTargetTest {CellScore} | R Documentation |
This function is called by CellScoreReport to make a rugplot showing the CellScore of all test samples, in relation to the standards. Donor and target individual CellScore values are plotted in one horizontal lane, then test CellScore values are are in another horizontal lane. Z-score cutoffs based on the target standards are shown as dashed vertical lines.
rugplotDonorTargetTest(test.data, cellscore)
test.data |
a data.frame of CellScore values as calculated by CellScore(), for only plot group of test samples. |
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
This function outputs the plot on the active graphical device and returns invisibly NULL.
CellScore
for details on CellScore.
## 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, ] ## Plot rugplotDonorTargetTest(test.data, cellscore) } ## End(Not run)