CellScoreReport: Generate a CellScore report
In CellScore: Tool for Evaluation of Cell Identity from Transcription Profiles

Description Usage Arguments Value See Also Examples

This function will generates a CellScore report for each study and transition that can be saved as a pdf. The report includes: 1) scatterplot of the donor-like and target-like scores of relevant test samples and the standards; 2) a density plot of the test and standard cellscores; 3) a rugplot of the cellscores, focussing on the test samples; 4) a heatmap of the OnOff Marker genes for all standards and test samples.

1	CellScoreReport(cellscore, cell.change, marker.genes, eset)

`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.
`marker.genes`	a data.frame of marker genes as generated by function OnOff()
`eset`	an ExpressionSet containing data matrices of normalized expression data, present/absent calls, a gene annotation data.frame and a phenotype data.frame.

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

CellScore for details on CellScore, and hgu133plus2CellScore for details on the specific ExpressionSet object that shoud be provided as an input.

## 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)

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

   ## Make a report and save it the current working directory
   pdf("TestReport.pdf", width=8, height=12)
   CellScoreReport(cellscore, cell.change, group.OnOff$markers, eset.sub)
   dev.off()
}