R/doThePlotAggregatedDrugBank.R

In ZBMEDLABS/epilepsyontologysimilarities: Epilepsy Ontology Similarities

#' Create aggregated plot with mean and standard deviation of all similarity metrics against DrugBank
#'
#' @param cosinedrugbank data.frame containing the vector with cosine metrics for MeSH, EpSO, ESSO, and EPILONT against DrugBank
#' @param dicedrugbank data.frame containing the vector with dice metrics for MeSH, EpSO, ESSO, and EPILONT against DrugBank
#' @param jaccarddrugbank data.frame containing the vector with jaccard metrics for MeSH, EpSO, ESSO, and EPILONT against DrugBank
#'
#' @return aggdrug the plot with the aggregated graphs
#' @export
#'
#' @examples
#' \dontrun{
#' aggdrug <- aggregatedPlotDrugBank(cosinedrugbank, dicedrugbank, jaccarddrugbank)
#' }
aggregatedPlotDrugBank <- function (cosinedrugbank, dicedrugbank, jaccarddrugbank) {
  # DrugBank: darkturquoise     #00CED1
  # EpSO: blue        #0000ff
  # ESSO: orange      #FFA500
  # EPILONT: purple   #800080
  cols <- c(MeSH = "#00CED1", EpSO="#0000FF",ESSO="#FFA500",EPILONT="#800080")
  
  myalpha = shQuote("0.2")
  mymeansize = 3
  mysdsize = 1
  

  mesh_metrics <- create_metrics (cosinedrugbank$MeSH, dicedrugbank$MeSH, jaccarddrugbank$MeSH, jaccarddrugbank$Elements)
  mesh_stats <- create_stats(mesh_metrics)

  epso_metrics <- create_metrics (cosinedrugbank$EpSO, dicedrugbank$EpSO, jaccarddrugbank$EpSO, jaccarddrugbank$Elements)
  epso_stats <- create_stats(epso_metrics)

  esso_metrics <- create_metrics (cosinedrugbank$ESSO, dicedrugbank$ESSO, jaccarddrugbank$ESSO, jaccarddrugbank$Elements)
  esso_stats <- create_stats(esso_metrics)

  epi_metrics <- create_metrics (cosinedrugbank$EPILONT, dicedrugbank$EPILONT, jaccarddrugbank$EPILONT, jaccarddrugbank$Elements)
  epi_stats <- create_stats(epi_metrics)

  epimaxsd <- epi_stats$comean + epi_stats$cosd
  epimaxsd <- epimaxsd[epimaxsd > 1] <- 1
  
  aggdrug <- ggplot2::ggplot(
    data = mesh_stats, 
    ggplot2::aes_string(x="elements", y="comean", colour = shQuote("MeSH"))
  ) + 
    ggplot2::geom_step(size = mymeansize) +
    ggplot2::geom_ribbon(
      data = mesh_stats,
      ggplot2::aes_string(x = "elements", ymin = "comean - cosd", ymax = "comean + cosd", colour = shQuote("MeSH"), fill = shQuote("MeSH"), alpha = myalpha), show.legend = FALSE, size = mysdsize
    ) +
    ggplot2::geom_step(
      data = epso_stats,
      ggplot2::aes_string(x="elements", y="comean", colour = shQuote("EpSO")), size = mymeansize
    ) +
    ggplot2::geom_ribbon(
      data = epso_stats,
      ggplot2::aes_string(x = "elements", ymin = "comean - cosd", ymax = "comean + cosd", colour = shQuote("EpSO"), fill = shQuote("EpSO"), alpha = myalpha), show.legend = FALSE, size = mysdsize
    ) +
    ggplot2::geom_step(
      data = esso_stats,
      ggplot2::aes_string(x="elements", y="comean", colour = shQuote("ESSO")), size = mymeansize
    ) +
    ggplot2::geom_ribbon(
      data = esso_stats,
      ggplot2::aes_string(x = "elements", ymin = "comean - cosd", ymax = "comean + cosd", colour = shQuote("ESSO"), fill = shQuote("ESSO"), alpha = myalpha), show.legend = FALSE, size = mysdsize
    ) +
    ggplot2::geom_step(
      data = epi_stats, 
      ggplot2::aes_string(x="elements", y="comean", colour = shQuote("EPILONT")), size = mymeansize
    ) + 
    ggplot2::geom_ribbon(
      data = epi_stats, 
      ggplot2::aes_string(x = "elements", ymin = "comean - cosd", ymax = "epimaxsd", colour = shQuote("EPILONT"), fill = shQuote("EPILONT"), alpha = myalpha), show.legend = FALSE, size = mysdsize    
    ) + 
    ggplot2::coord_cartesian (xlim = c(0,2604), ylim = c(0.86125,1)) +
    ggplot2::scale_x_continuous(breaks = c(0, 500,1000,1500, 2000, 2604)) +
    ggplot2::scale_y_continuous(breaks = c(0.875, 0.9, 0.925, 0.95, 0.975, 1)) +
    ggplot2::theme_minimal () +
    ggplot2::theme(panel.grid.major = ggplot2::element_line(colour = "gray"),
                   panel.grid.minor.y = ggplot2::element_line(colour = "gray"),
                   legend.text=ggplot2::element_text(size=16, face = "bold"),
                   legend.position=c(0,1),
                   legend.justification=c(0, 0),
                   legend.direction="horizontal",
                   legend.title = ggplot2::element_blank(),
                   plot.title = ggplot2::element_text(size = 16, face = "bold"),
                   axis.title.x = ggplot2::element_text(size = 16, face = "bold"),
                   axis.title.y = ggplot2::element_text(size = 16, face = "bold"),
                   axis.text.x = ggplot2::element_text(size = 14),
                   axis.text.y = ggplot2::element_text(size = 14)) +
    ggplot2::labs (y="Mean", x="TopK", title = "", subtitle = "") +
#    ggplot2::labs (y="Mean", x="TopK", title = "Mean of Cosine, Dice, and Jaccard with Standard Deviation against DrugBank", subtitle = "") +
    ggplot2::scale_colour_manual(values=cols)+
    ggplot2::scale_fill_manual(values=cols) +
    ggplot2::scale_size_manual()
  return (aggdrug)
}