R/CellScoreReport.R

In nmah/CellScore: Tool for Evaluation of Cell Identity from Transcription Profiles

# CellScoreReport.R

# from CSReport_v1.4.3.R

#' Generate a CellScore report
#'
#' 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.
#' @param cellscore a data.frame of CellScore values, as calculated by
#'   CellScore().
#' @param 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.
#' @param marker.genes a data.frame of marker genes as generated by function OnOff()
#' @param eset an ExpressionSet containing data matrices of normalized
#'   expression data, present/absent calls, a gene annotation data.frame and
#'   a phenotype data.frame.
#' @return This function outputs the plots on the active graphical device
#'   and returns invisibly NULL.
#' @keywords cellscore report
#' @seealso \code{\link[CellScore]{CellScore}} for details on CellScore, and
#'   \code{\link[hgu133plus2CellScore]{hgu133plus2CellScore}} for details on the
#'   specific ExpressionSet object that shoud be provided as an input.
#' @importClassesFrom Biobase ExpressionSet
#' @importMethodsFrom Biobase fData pData
#' @importFrom Biobase assayDataElement
#' @export
#' @examples
#' ## 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$general_cell_type %in% c("ESC", "EC", "FIB", "KER", 
#'                  "ASC", "NPC", "MSC", "iPS", "piPS")
#'    eset.sub <- eset[, sel.samples]
#'    cs <- CosineSimScore(eset.sub, cell.change, iqr.cutoff=0.1)
#'
#'    ## Generate the on/off scores for the combined data
#'    individ.OnOff <- OnOff(eset.sub, cell.change, out.put="individual")
#'
#'    ## Generate the CellScore values for all samples
#'    cellscore <- CellScore(data=eset.sub, transitions=cell.change, scores.onoff=individ.OnOff$scores,
#'                           scores.cosine=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()
#' }

CellScoreReport <- function(cellscore, cell.change, marker.genes, eset) {

    ############################################################################
    ## PART 0. Check function arguments
    ############################################################################
    fun.main <- deparse(match.call()[[1]])
    summarized_experiment <- .stopIfCantCoerceToSummarizedExperiment(eset, 'eset', fun.main)
    .stopIfNotDataFrame(cell.change, 'cell.change', fun.main)
    .stopIfNotDataFrame(cellscore, 'cellscore', fun.main)
    .stopIfNotDataFrame(marker.genes, 'marker.genes', fun.main)

    ############################################################################
    ## PART I. Extract and format the data for plotting
    ############################################################################
    ## Get the test CellScore from 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), ]

    ## Get the phenotype and the absent/present calls
    pdata <- data.frame(colData(summarized_experiment))
    calls <- assay(summarized_experiment, "calls")
    ## set rownames of calls to probeset ids (these are unique and
    ## less error prone than the current rownames)
    rownames(calls) <- rowData(summarized_experiment)[, "feature_id"]

    ############################################################################
    ## PART II. Plot
    ############################################################################
    old.par <- par(no.readonly = TRUE)
    lapply(unique(plot.data.ordered$plot_group),
           function(group){
               ## Get the scores for the given plot group
               test.data <-
                   plot.data.ordered[plot.data.ordered$plot_group %in% group, ]
               ## Page layout for plots
               layout(matrix(c(1,2,3,3,4,4), nrow=3, ncol=2, byrow=TRUE))
               ## Plots
               scatterplotDonorTargetTest(test.data, cellscore, FALSE)
               rugplotDonorTargetTest(test.data, cellscore)
               heatmapOnOffMarkers(test.data, marker.genes, pdata, calls)
    })

    ## Reset graphical parameters
    par(old.par)
    invisible()
}