R/plot_celltype_annotations.R

In neurogenomics/CHAS: Cell type deconvolution of bulk tissue histone acetylomes from epigenetic data

#' Plot the proportions of peaks annotated to each cell type
#'
#' This function takes as input the output of the function CelltypeSpecificPeaks()
#' and uses the annotated bulk peaks to plot the proportion of each cell type.
#' @param annotatedPeaks The output list of data frames from the function CelltypeSpecificPeaks()
#' containing the annotated bulk peaks and the cell type-specific bulk peaks.
#' @return A stacked bar plot showing the proportion of each cell type in the bulk peak set.
#' @import ggplot2
#' @import RColorBrewer
#' @export

plot_celltype_annotations <- function(annotatedPeaks){
  allPeaks = annotatedPeaks[[2]]
  CellTable <- table(allPeaks$Annot)
  PropTable <- data.frame(prop.table(CellTable)*100)
  PropTable$Source <- "Peaks"
  PropTable$Var1 <- factor(PropTable$Var1,
                           c(sort(unique(allPeaks$Annot[!allPeaks$Annot=="Multiple" & !allPeaks$Annot=="Other"])),
                             "Multiple", "Other"))
  plot <- ggplot(PropTable, aes(x=Source, y=Freq, fill=Var1)) +
    geom_bar(stat="identity") + theme_classic() +
    scale_fill_manual(values=c("#446455", "#FDD262", "#46ACC8", "#F4B5BD",
                               brewer.pal(8, "Accent")[0:(nrow(PropTable)-6)],"#E58601","#DDDDDD")) +
    ylab("Proportion of peaks specific to each cell type (%)") + xlab("Peaks") +
    theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
    guides(fill=guide_legend(title="Cell type"))
  return(plot)
}