R/plotMafSummary.R

In maftools: Summarize, Analyze and Visualize MAF Files

#' Plots maf summary.
#'
#' @description Plots maf summary.
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param dashboard If FALSE plots simple summary instead of dashboard style.
#' @param titvRaw TRUE. If false instead of raw counts, plots fraction.
#' @param log_scale FALSE. If TRUE log10 transforms Variant Classification, Variant Type and Variants per sample sub-plots.
#' @param rmOutlier If TRUE removes outlier from boxplot.
#' @param addStat Can be either mean or median. Default NULL.
#' @param showBarcodes include sample names in the top bar plot.
#' @param color named vector of colors for each Variant_Classification.
#' @param textSize font size if showBarcodes is TRUE. Default 0.8
#' @param titleSize font size for title and subtitle. Default c(10, 8)
#' @param fs base size for text. Default 1
#' @param top include top n genes dashboard plot. Default 10.
#' @param titvColor colors for SNV classifications.
#' @examples
#' laml.maf <- system.file("extdata", "tcga_laml.maf.gz", package = "maftools")
#' laml <- read.maf(maf = laml.maf, useAll = FALSE)
#' plotmafSummary(maf = laml, addStat = 'median')
#' @return Prints plot.
#' @import RColorBrewer
#' @seealso \code{\link{read.maf}} \code{\link{MAF}}
#' @export

plotmafSummary = function(maf, rmOutlier = TRUE, dashboard = TRUE, titvRaw = TRUE, log_scale = FALSE,
                          addStat = NULL, showBarcodes = FALSE, fs = 1,
                          textSize = 0.8, color = NULL, titleSize = c(1, 0.8), titvColor = NULL, top = 10){


  addStat.opts = c('mean', 'median')
  if(!is.null(addStat)){
    if(length(addStat) > 1){
      stop('addStat can only be either mean or median.')
    }

    if(! addStat %in% addStat.opts){
      stop('addStat can only be either mean or median.')
    }
  }


  if(dashboard){
    #Plot in dashboard style
    pie = FALSE
     dashboard(maf = maf, color = color, rmOutlier = TRUE, log_conv = log_scale,
                          titv.color = titvColor, fontSize = fs, titleSize = titleSize, sfs = statFontSize,
                          n = top, donut = pie, rawcount = titvRaw, stat = addStat, barcodes = showBarcodes, barcodeSize = textSize)

  }else{

    if(is.null(color)){
      #hard coded color scheme if user doesnt provide any
      col = get_vcColors()
    }else{
      col = color
    }

    vcs = getSampleSummary(maf)
    vcs = vcs[,colnames(vcs)[!colnames(x = vcs) %in% c('total', 'Amp', 'Del', 'CNV_total')], with = FALSE]

    vcs = vcs[,c(1,order(colSums(x = vcs[,2:(ncol(vcs)), with =FALSE]), decreasing = TRUE)+1), with =FALSE] #order based on most event
    vcs.m = data.table::melt(data = vcs, id = 'Tumor_Sample_Barcode')
    colnames(vcs.m) = c('Tumor_Sample_Barcode', 'Variant_Classification', 'N')

    data.table::setDF(vcs)
    rownames(x = vcs) = vcs$Tumor_Sample_Barcode
    vcs = vcs[,-1]
    vcs = t(vcs)

    #--------------------------- variant per sample plot -----------------

    graphics::layout(mat = matrix(c(1, 1, 2, 2, 3, 3), nrow = 3, byrow = TRUE), heights = c(4, 4, 3))
    if(showBarcodes){
      par(mar = c(7, 2, 3, 1))
      b = barplot(vcs, col = col[rownames(vcs)], border = NA, axes = FALSE, names.arg =  rep("", ncol(vcs)))
      axis(side = 1, at = b, labels = colnames(vcs), font = 2, cex.axis = textSize, las = 2, lwd = 1)

    }else{
      par(mar = c(2, 2, 3, 1))
      b = barplot(vcs, col = col[rownames(vcs)], border = NA, axes = FALSE, names.arg =  rep("", ncol(vcs)))
    }

    axis(side = 2, at = as.integer(seq(0, max(colSums(vcs)), length.out = 4)), lwd = 2, font = 2, las = 2,
         line = -0.3, hadj = 0.6, cex.axis = fs)
    title(main = "Variants per sample", adj = 0, cex.main = titleSize[1], font = 2, line = 2)

    if(!is.null(addStat)){
      if(addStat == 'mean'){
        med.line = round(maf@summary[nrow(maf@summary),Mean], 2)
        df = data.frame(y = c(med.line), x = as.integer(0.8*nrow(getSampleSummary(maf))), label = c(paste('Mean: ', med.line, sep='')))
      }else if(addStat == 'median'){
        med.line = round(maf@summary[nrow(maf@summary),Median], 2)
        df = data.frame(y = c(med.line), x = as.integer(0.8*nrow(getSampleSummary(maf))), label = c(paste('Median: ', med.line, sep='')))
      }
    }else{
      med.line = round(max(maf@summary[,Median], na.rm = TRUE), 2)
    }

    title(main = paste0("Median: ", med.line), adj = 0, cex.main = titleSize[2], font = 2, line = 1)
    lines(x = c(1, b[length(b)]), y = c(med.line, med.line), col = "maroon", lwd = 2, lty = 2)

    #--------------------------- vc summary plot -----------------
    par(mar = c(2, 2, 2, 1))
    boxH = vcs.m[,boxplot.stats(N)$stat[5], by = .(Variant_Classification)]
    colnames(boxH)[ncol(boxH)] = 'boxStat'
    b = boxplot(N ~ Variant_Classification, data = vcs.m, col = col[levels(vcs.m$Variant_Classification)],
            axes = FALSE, outline = FALSE, lwd = 1, border = grDevices::adjustcolor(col = "black", alpha.f = 0.6))
    axis(side = 2, at = as.integer(seq(0, max(boxH[,boxStat], na.rm = TRUE), length.out = 4)),
         lwd = 2, font = 2, cex.axis = fs, las = 2)
    title(main = "Variant Classification summary", adj = 0, cex.main = fs, font = 2, line = 1)

    plot.new()
    par(mar = c(4, 2.5, 0.5, 2))
    legend(x = "top", legend = names(col[levels(vcs.m$Variant_Classification)]),
           fill = col[levels(vcs.m$Variant_Classification)],
           bty = "n", ncol = 3, cex = fs)
  }
}