extractTransitions {CellScore} | R Documentation |
This function extracts the values of the CellScore for all the test samples of a given set of (valid) cell transition. While it can be used as standalone, it serves as an internal function for several other CellScore functions.
extractTransitions(cellscore, cell.change)
cellscore |
a data.frame of CellScore values as calculated by the function 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. |
This function returns a data frame with the same columns as the input data frame cellscore, extended with additional column that is used as a single identifier of each valid cell transition. Technically, the output is subselection of the input data frame.
CellScore
for details on CellScore
calcualtion.
## 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$cosine.samples) ## Extract the scores for the transitions given in cell.change cellscore.cc <- extractTransitions(cellscore, cell.change) ## View the sub_cell_type1 in the extracted object, it should be the same ## as the test cell types named in cell.change table(cellscore.cc$sub_cell_type1) }