RugplotCellScore {CellScore} | R Documentation |
This function will plot a rugplot of all CellScore values for each transition selected in the cell.change data frame. The function will only plot the scores for the test samples (annotated by the cellscore$column sub_cell_type1). Standards are not included. Samples are coloured by a secondary property, which must be a single column in the cellscore data frame.
RugplotCellScore(cellscore, cell.change, colour.by = NULL)
cellscore |
a data.frame of CellScore values as calculated by CellScore(). |
cell.change |
a data frame containing three columns, one for the start (donor) test and target cell type. Each row of the data. frame describes one transition from the start to a target cell type. |
colour.by |
the name of the column in the cellscore argument that contains the secondary property. |
This function outputs the plot on the active graphical device and returns invisibly NULL.
CellScore
for details on CellScore.
## Load the expression set for the standard cell types library(Biobase) library(hgu133plus2CellScore) # eset.std ## Locate the external data files in the CellScore package rdata.path <- system.file("extdata", "eset48.RData", package = "CellScore") tsvdata.path <- system.file("extdata", "cell_change_test.tsv", package = "CellScore") if (file.exists(rdata.path) && file.exists(tsvdata.path)) { ## Load the expression set with normalized expressions of 48 test samples load(rdata.path) ## Import the cell change info for the loaded test samples cell.change <- read.delim(file= tsvdata.path, sep="\t", header=TRUE, stringsAsFactors=FALSE) ## Combine the standards and the test data eset <- combine(eset.std, eset48) ## Generate cosine similarity for the combined data ## NOTE: May take 1-2 minutes on the full eset object ## so we subset it for 4 cell types pdata <- pData(eset) sel.samples <- pdata$general_cell_type %in% c("ESC", "EC", "FIB", "KER", "ASC", "NPC", "MSC", "iPS", "piPS") eset.sub <- eset[, sel.samples] cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1) ## Generate the on/off scores for the combined data individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual") ## Generate the CellScore values for all samples cellscore <- CellScore(eset.sub, cell.change, individ.OnOff$scores, cs$cosine.samples) ## Rugplot of CellScore, colour samples by transition induction method RugplotCellScore(cellscore, cell.change, "transition_induction_method") }