R/plotting_functions.R

In SCOPRO: Score Projection Between in 'Vivo' and in 'Vitro' Datasets

#' plot_score_genes
#'
#' @inheritParams SCOPRO
#' @inheritParams select_top_markers
#' @param markers_to_plot Character vector with the names of the genes to plot.
#' @param label_1 Character value. Label for the in vitro dataset
#' @param label_2 Character value. Label for the in vivo dataset
#' @param final_name Character vector with the names of the genes to show in the plot.
#' @param max_size Numeric value, specifying the size of the dot.
#' @param text_size Numeric value, specifying the size of the text in the plot.
#' @param title_name Character value.

#' @return ggplot2::ggplot2 object showing balloon plot.
#' @author Gabriele Lubatti \email{gabriele.lubatti@@helmholtz-muenchen.de}
#'
#'
#'
#' @export plot_score_genes
#'


plot_score_genes <- function(markers_to_plot ,label_1, label_2, norm_vitro,norm_vivo, cluster_vitro,cluster_vivo, final_name, max_size = 9, text_size= 9.5, title_name)
{

  markers_to_plot <- markers_to_plot[markers_to_plot %in% row.names(norm_vitro)]



  all_markers_common_name <- final_name

  label_mouse <- c(rep(label_1,length(colnames(norm_vitro))),rep(label_2,length(colnames(norm_vivo))))
  mouse_norm_common=cbind(norm_vitro[markers_to_plot,],norm_vivo[markers_to_plot,])




  cluster_vitro_name <- paste(label_1, factor(cluster_vitro), sep="-")
  cluster_vivo_name <- paste(label_2, factor(cluster_vivo), sep="-")
  mouse_norm_common <- cbind(norm_vitro[markers_to_plot, ], norm_vivo[markers_to_plot, ])
  label_mouse <- c(cluster_vitro_name, cluster_vivo_name)
  out_2 <- plot_balons(mouse_norm_common, label_mouse,markers_to_plot, length(markers_to_plot), length(colnames(mouse_norm_common)), max_size = max_size,text_size = text_size, all_markers_common_name, keep_order = FALSE)
  out_2 <- out_2 + ggplot2::ggtitle(title_name)

  return(out_2)
}


#' plot_score
#'
#' @inheritParams SCOPRO
#' @inheritParams select_top_markers
#' @inheritParams  select_common_genes
#' @inheritParams plot_score_genes
#' @param y_name Character value
#' @param fill_name Character value.

#' @return ggplot2::ggplot2 object.
#' @author Gabriele Lubatti \email{gabriele.lubatti@@helmholtz-muenchen.de}
#'
#'
#'
#' @export plot_score
#'

plot_score <- function(SCOPRO_output, marker_stages, marker_stages_filter, selected_stages, name_vivo, y_name, fill_name, title_name){
  final_score <- c(as.vector(unlist(SCOPRO_output[[5]])))
  label <- c(names(unlist(SCOPRO_output[[5]])))
  df <- data.frame(final_score,label)

  index_specific <- which(selected_stages %in% name_vivo)
  marker_specific <- marker_stages[[index_specific]]
  p <- ggplot2::ggplot(df, ggplot2::aes(x = label, y = final_score, fill = label)) +
    ggplot2::geom_bar(stat = "identity") + ggplot2::theme_minimal()
  p + ggplot2::theme( axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1),
             axis.title.x = ggplot2::element_blank()) + ggplot2::ylab(y_name) + ggplot2::labs(fill=fill_name) +  ggplot2::ylim(c(0,1)) + ggplot2::ggtitle(paste0(title_name," (",sum(marker_stages_filter %in% marker_specific)," genes)",sep=" "))
}

#' plot_balons
#' @noRd
#'
plot_balons <- function(norm_counts, final_cluster, genes_to_plot, max_number, total_cells, max_size=5, text_size=7,label_final, keep_order=TRUE){

  fattore <- factor(final_cluster,levels=unique(final_cluster))
  if (keep_order == FALSE){
    fattore <- factor(final_cluster)}
  livelli <- levels(factor(fattore))



  all_markers <- genes_to_plot

  all_markers <- lapply(all_markers, function(x) {x[x!=0]})


  all_markers_values <- rep(list(0),length(livelli))
  for ( i in 1:length(livelli)){
    gene_values_0 <- apply(norm_counts[genes_to_plot,final_cluster==livelli[i]], 1, mean)
    all_markers_values[[i]] <- gene_values_0
  }


  values <- rep(list(0),length(livelli))
  for ( i in 1:length(livelli)){
    valore=apply(norm_counts[genes_to_plot,final_cluster==livelli[i]],1,function(x){sum(x>0.1)/length(x)})
    values[[i]]=valore
  }

  cluster_label_all=rep(list(0),length(livelli))
  for ( i in 1:length(livelli)){
    cluster_label_all[[i]] <- rep(livelli[i],length(genes_to_plot))
  }


  all_markers_plot <- genes_to_plot
  cluster_label_all <- unlist(cluster_label_all)
  values <- unlist(values)
  all_markers_values <- unlist(all_markers_values)
  balloon_melted <- data.frame(all_markers_plot,cluster_label_all,values,label_final)
  colnames(balloon_melted) <- c("genes_plot","cluster_label_all","values","label_all_final")

  p <- ggplot2::ggplot(balloon_melted, ggplot2::aes(x =factor(cluster_label_all,levels=unique(cluster_label_all)), y = factor(balloon_melted[,4],levels=(unique(label_final)))))
  p + ggplot2::geom_point( ggplot2::aes(size=values,colour=as.numeric(all_markers_values))) + ggplot2::theme(panel.background=ggplot2::element_blank(), panel.border = ggplot2::element_rect(colour = "blue", fill=NA, size=3))+ggplot2::scale_size_area(max_size=max_size)+ ggplot2::scale_colour_gradient(low = "grey", high = "brown", na.value = NA)+ggplot2::labs(colour="Mean",size="Value")+ggplot2::xlab("Cluster")+ggplot2::ylab("Markers")+ggplot2::theme(text = ggplot2::element_text(size=text_size),axis.text.x = ggplot2::element_text(angle=45,vjust=1,hjust=1),axis.title.x=ggplot2::element_blank())
}