R/GEX_volcano.R

In Platypus: Single-Cell Immune Repertoire and Gene Expression Analysis

#'Flexible wrapper for GEX volcano plots
#'
#'@description Plots a volcano plot from the output of the FindMarkers function from the Seurat package or the GEX_cluster_genes function alternatively.
#' @param DEGs.input Either output data frame from the FindMarkers function from the Seurat package or GEX_cluster_genes list output.
#' @param input.type Character specifing the input type as either "findmarkers" or "cluster.genes". Defaults to "cluster.genes"
#' @param condition.1 either character or integer specifying ident.1 that was used in the FindMarkers function from the Seurat package. Should be left empty when using the GEX_cluster_genes output.
#' @param condition.2 either character or integer specifying ident.2 that was used in the FindMarkers function from the Seurat package. Should be left empty when using the GEX_cluster_genes output.
#' @param explicit.title logical specifying whether the title should include logFC information for each condition.
#' @param RP.MT.filter Boolean. Defaults to TRUE. Whether to exclude ribosomal and mitochondrial genes.
#' @param color.p.threshold numeric specifying the adjusted p-value threshold for geom_points to be colored. Default is set to 0.01.
#' @param color.log.threshold numeric specifying the absolute logFC threshold for geom_points to be colored. Default is set to 0.25.
#' @param label.p.threshold numeric specifying the adjusted p-value threshold for genes to be labeled via geom_text_repel. Default is set to 0.001.
#' @param label.logfc.threshold numeric specifying the absolute logFC threshold for genes to be labeled via geom_text_repel. Default is set to 0.75.
#' @param n.label.up numeric specifying the number of top upregulated genes to be labeled via geom_text_repel. Genes will be ordered by adjusted p-value. Overrides the "label.p.threshold" and "label.logfc.threshold" parameters.
#' @param n.label.down numeric specifying the number of top downregulated genes to be labeled via geom_text_repel. Genes will be ordered by adjusted p-value. Overrides the "label.p.threshold" and "label.logfc.threshold" parameters.
#' @param by.logFC logical. If set to TRUE n.label.up and n.label.down will label genes ordered by logFC instead of adjusted p-value.
#' @param maximum.overlaps integer specifying removal of labels with too many overlaps. Default is set to Inf.
#' @param plot.adj.pvalue logical specifying whether adjusted p-value should by plotted on the y-axis.
#' @return Returns a volcano plot from the output of the FindMarkers function from the Seurat package, which is a ggplot object that can be modified or plotted. Infinite p-values are set defined value of the highest -log(p) + 100.
#' @export
#' @examples
#' \donttest{
#' try({
#' GEX_volcano(findmarkers.output = FindMarkers.Output
#' , condition.1 = "cluster1", condition.2 = "cluster2"
#' , maximum.overlaps = 20)
#'
#' GEX_volcano(findmarkers.output = FindMarkers.Output
#' , condition.1 = "cluster1", condition.2 = "cluster2"
#' , n.label.up = 50, n.label.down = 20)
#'
#' GEX_volcano(findmarkers.output = GEX_cluster_genes.Output
#' , cluster.genes.output =TRUE)
#' })
#'}
GEX_volcano <- function(DEGs.input,
                        input.type,
                        condition.1,
                        condition.2,
                        explicit.title,
                        RP.MT.filter,
                        color.p.threshold,
                        color.log.threshold,
                        label.p.threshold,
                        label.logfc.threshold,
                        n.label.up,
                        n.label.down,
                        by.logFC,
                        maximum.overlaps,
                        plot.adj.pvalue) {

  avg_logFC <- NULL
  minus.log10p <- NULL
  genes <- NULL
  p_val_adj <- NULL
  minus.log10p_adj <- NULL
  avg_logFC <- NULL
  SYMBOL <- NULL



  ###
  if(missing(DEGs.input)){
    stop("Please provide either a list output of GEX_cluster genes or a table output of FindMarkers for this function")}
  if(missing(input.type)){
    input.type <- "cluster.genes"
    message("Using GEX_cluster_genes list as input. Specify 'findmakers' as input type to use a table output of the Seurat Findmarkers function")}
  if(!input.type %in% c("cluster.genes", "findmarkers")){
    stop("Please specifiy either 'findmakers' or 'cluster.genes' depending on the input type")}
  if(missing(condition.1)){condition.1 <- ""}
  if(missing(condition.2)){condition.2 <- ""}
  if(condition.1 =="" &  condition.2 == "" & input.type == "findmarkers"){
    warning("Conditions not provided and will not be displayed in plot title")}
  if(missing(explicit.title)){
    explicit.title <- TRUE}
  if(missing(RP.MT.filter)){
    RP.MT.filter <- TRUE}
  if(missing(color.p.threshold)){color.p.threshold <- 0.01}
  if(missing(color.log.threshold)){color.log.threshold <- 0.25}
  if(missing(label.p.threshold)){label.p.threshold <- 0.001}
  if(missing(label.logfc.threshold)){label.logfc.threshold <- 0.75}
  if(missing(n.label.up)){n.label.up <- FALSE}
  if(missing(n.label.down)){n.label.down <- FALSE}
  if(missing(by.logFC)){
    by.logFC <- FALSE}
  if(missing(maximum.overlaps)){
    maximum.overlaps <- Inf}
  if(missing(plot.adj.pvalue)){
    plot.adj.pvalue <- FALSE}

  platypus.version <- "Does not matter"

  ###
  if(n.label.up == FALSE & n.label.down == FALSE){
  }

  class_up <- as.character(class(n.label.up))
  class_down <- as.character(class(n.label.down))

  if(class_down != class_up){
    temp <- list(n.label.up, n.label.down)
    n.genes <- as.numeric(Filter(is.numeric, temp))
    n.label.up <- n.genes
    n.label.down <- n.genes
  } # Assuming the same number of up- and downregulated genes are to be labeled if only one is specified

  ###


  findmarkers.output <- DEGs.input
  if(input.type == "findmarkers") cluster.genes.output <- FALSE
  if(input.type == "cluster.genes") cluster.genes.output <- TRUE


  if(cluster.genes.output == FALSE) {

    if(RP.MT.filter ==TRUE){
      exclude <- c()
      for (i in 1:3) {
        exclude <- c(exclude, which(stringr::str_detect(rownames(findmarkers.output), c("MT-", "RPL", "RPS")[i])))
      }
      if(length(exclude) > 0){
      findmarkers.output <- findmarkers.output[-exclude,]}
    }

    findmarkers.output$genes <- rownames(findmarkers.output)

    if(plot.adj.pvalue == FALSE) {
      findmarkers.output$minus.log10p <- -log10(findmarkers.output$p_val)
      findmarkers.output$minus.log10p[which(findmarkers.output$minus.log10p == Inf)] <- findmarkers.output$minus.log10p[which(sort(findmarkers.output$minus.log10p, decreasing = TRUE) != Inf)[1]]+100 #calculating -log10p and setting the ones that are Inf to defined value

      if(n.label.up == FALSE & n.label.down == FALSE) {
        output.plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p, label = genes)) + ggplot2::geom_point() +
          ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
          ggrepel::geom_text_repel(data =subset(findmarkers.output, abs(avg_logFC) > label.logfc.threshold & p_val_adj < label.p.threshold),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(p-value)")
      }

      if(inherits(n.label.up,"numeric") & inherits(n.label.down,"numeric")) {
        if(by.logFC == FALSE) {
          findmarkers.output <- findmarkers.output[order(findmarkers.output$p_val_adj),]
          posFC_genes <- findmarkers.output$genes[which(findmarkers.output$avg_logFC > 0)][1:n.label.up]
          negFC_genes <- findmarkers.output$genes[which(findmarkers.output$avg_logFC < 0)][1:n.label.down]
        }
        if(by.logFC == TRUE) {
          l <- dim(findmarkers.output)[1]
          findmarkers.output <- findmarkers.output[order(findmarkers.output$avg_logFC),]
          posFC_genes <- findmarkers.output$genes[1:n.label.up]
          negFC_genes <- findmarkers.output$genes[(l-n.label.down-1):l]
        }
        label.genes <- c(posFC_genes,negFC_genes)

        output.plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p, label = genes)) + ggplot2::geom_point() +
          ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
          ggrepel::geom_text_repel(data =subset(findmarkers.output, genes%in%label.genes),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(p-value)")
      }

    }
    if(plot.adj.pvalue == TRUE) {
      findmarkers.output$minus.log10p_adj <- -log10(findmarkers.output$p_val_adj)
      findmarkers.output$minus.log10p_adj[which(findmarkers.output$minus.log10p_adj == Inf)] <- findmarkers.output$minus.log10p_adj[which(sort(findmarkers.output$minus.log10p_adj, decreasing = TRUE) != Inf)[1]]+100 #calculating -log10p and setting the ones that are Inf to defined value

      if(n.label.up == FALSE & n.label.down == FALSE) {
        output.plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p_adj, label = genes)) + ggplot2::geom_point() +
          ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
          ggrepel::geom_text_repel(data =subset(findmarkers.output, abs(avg_logFC) > label.logfc.threshold & p_val_adj < label.p.threshold),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(adj.p-value)")
      }

      if(inherits(n.label.up,"numeric") & inherits(n.label.down,"numeric")) {
        if(by.logFC == FALSE) {
          findmarkers.output <- findmarkers.output[order(findmarkers.output$p_val_adj),]
          posFC_genes <- findmarkers.output$genes[which(findmarkers.output$avg_logFC > 0)][1:n.label.up]
          negFC_genes <- findmarkers.output$genes[which(findmarkers.output$avg_logFC < 0)][1:n.label.down]
        }
        if(by.logFC == TRUE) {
          l <- dim(findmarkers.output)[1]
          findmarkers.output <- findmarkers.output[order(findmarkers.output$avg_logFC),]
          posFC_genes <- findmarkers.output$genes[1:n.label.up]
          negFC_genes <- findmarkers.output$genes[(l-n.label.down-1):l]
        }
        label.genes <- c(posFC_genes,negFC_genes)
        output.plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p_adj, label = genes)) + ggplot2::geom_point() +
          ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
          ggrepel::geom_text_repel(data =subset(findmarkers.output, genes%in%label.genes),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(adj.p-value)") + cowplot::theme_cowplot()#+ ggplot2::ylim(-10, max(findmarkers.output$minus.log10p_adj)+ 30)
      }
    }

    if(explicit.title == FALSE) {
      output.plot <- output.plot + ggplot2::ggtitle(paste(condition.2, "vs.", condition.1))
    }
    if(explicit.title ==TRUE){
      output.plot <- output.plot + ggplot2::ggtitle(paste(condition.2 , "(up=-FC)", "vs.", condition.1, "(up=+FC)"))
    }
  }

  if(cluster.genes.output == TRUE){

    output.plot <- list()

    if(is.list(DEGs.input)){
      DEGs.input.length <- length(DEGs.input)
    }else{
      DEGs.input.length <- 1
    }

    for (i in 1:DEGs.input.length) {

      if(is.list(DEGs.input)){
        findmarkers.output <- DEGs.input[[i]]
      }else{
        findmarkers.output <- DEGs.input
      }

      if(RP.MT.filter ==TRUE){
        exclude <- c()
        for (j in c("MT-", "RPL", "RPS")) {
          exclude <- c(exclude, stringr::str_which(rownames(findmarkers.output), j))
        }
        if(length(exclude) != 0){
        findmarkers.output <- findmarkers.output[-exclude,]
        }
      }

      if(plot.adj.pvalue == FALSE) {
        findmarkers.output$minus.log10p <- -log10(findmarkers.output$p_val)
        findmarkers.output$minus.log10p[which(findmarkers.output$minus.log10p == Inf)] <- findmarkers.output$minus.log10p[which(sort(findmarkers.output$minus.log10p, decreasing = TRUE) != Inf)[1]]+100 #calculating -log10p and setting the ones that are Inf to defined value

        if(n.label.up == FALSE & n.label.down == FALSE) {
          cluster_plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p, label = SYMBOL)) + ggplot2::geom_point() +
            ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
            ggrepel::geom_text_repel(data =subset(findmarkers.output, abs(avg_logFC) > label.logfc.threshold & p_val_adj < label.p.threshold),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(p-value)")
        }

        if(inherits(n.label.up,"numeric") & inherits(n.label.down,"numeric")) {
          if(by.logFC == FALSE) {
            findmarkers.output <- findmarkers.output[order(findmarkers.output$p_val_adj),]
            posFC_genes <- findmarkers.output$SYMBOL[which(findmarkers.output$avg_logFC > 0)][1:n.label.up]
            negFC_genes <- findmarkers.output$SYMBOL[which(findmarkers.output$avg_logFC < 0)][1:n.label.down]
          }
          if(by.logFC == TRUE) {
            l <- dim(findmarkers.output)[1]
            findmarkers.output <- findmarkers.output[order(findmarkers.output$avg_logFC),]
            posFC_genes <- findmarkers.output$SYMBOL[1:n.label.up]
            negFC_genes <- findmarkers.output$SYMBOL[(l-n.label.down-1):l]
          }
          label.genes <- c(posFC_genes,negFC_genes)


          cluster_plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p, label = SYMBOL)) + ggplot2::geom_point() +
            ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
            ggrepel::geom_text_repel(data =subset(findmarkers.output, SYMBOL%in%label.genes),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(p-value)")
        }
      }

      if(plot.adj.pvalue == TRUE) {
        findmarkers.output$minus.log10p_adj <- -log10(findmarkers.output$p_val_adj)
        findmarkers.output$minus.log10p_adj[which(findmarkers.output$minus.log10p_adj == Inf)] <- findmarkers.output$minus.log10p_adj[which(sort(findmarkers.output$minus.log10p_adj, decreasing = TRUE) != Inf)[1]]+100 #calculating -log10p and setting the ones that are Inf to defined value

        if(n.label.up == FALSE & n.label.down == FALSE) {
          cluster_plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p_adj, label = SYMBOL)) + ggplot2::geom_point() +
            ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
            ggrepel::geom_text_repel(data =subset(findmarkers.output, abs(avg_logFC) > label.logfc.threshold & p_val_adj < label.p.threshold),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot()+ ggplot2::ylab("-log10(adj.p-value)")
        }

        if(inherits(n.label.up,"numeric") & inherits(n.label.down,"numeric")) {
          if(by.logFC == FALSE) {
            findmarkers.output <- findmarkers.output[order(findmarkers.output$p_val_adj),]
            posFC_genes <- findmarkers.output$SYMBOL[which(findmarkers.output$avg_logFC > 0)][1:n.label.up]
            negFC_genes <- findmarkers.output$SYMBOL[which(findmarkers.output$avg_logFC < 0)][1:n.label.down]
          }
          if(by.logFC == TRUE) {
            l <- dim(findmarkers.output)[1]
            findmarkers.output <- findmarkers.output[order(findmarkers.output$avg_logFC),]
            posFC_genes <- findmarkers.output$SYMBOL[1:n.label.up]
            negFC_genes <- findmarkers.output$SYMBOL[(l-n.label.down-1):l]
          }
          label.genes <- c(posFC_genes,negFC_genes)

          cluster_plot <- ggplot2::ggplot(findmarkers.output, ggplot2::aes(x=avg_logFC, y=minus.log10p_adj, label = SYMBOL)) + ggplot2::geom_point() +
            ggplot2::geom_point(data = subset(findmarkers.output, abs(avg_logFC) > color.log.threshold & p_val_adj < color.p.threshold), col= "darkred") +
            ggrepel::geom_text_repel(data =subset(findmarkers.output, SYMBOL%in%label.genes),  color = 'black', hjust = 0, direction = "y", max.overlaps = maximum.overlaps) + cowplot::theme_cowplot() + ggplot2::ylab("-log10(adj.p-value)")
        }
      }

      if(explicit.title == FALSE) {
        output.plot[[i]] <- cluster_plot + ggplot2::ggtitle(paste("All clusters vs.", "cluster", i-1))
      }
      if(explicit.title ==TRUE){
        output.plot[[i]] <- cluster_plot + ggplot2::ggtitle(paste("All clusters (up=-FC)", "vs.", "cluster", i-1,"(up=+FC)"))
      }
    }
  }
  return(output.plot)
}