R/VDJ_Vgene_usage_stacked_barplot.R

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

#'V(D)J gene usage stacked barplots
#'
#' @description Produces a stacked barplot with the fraction of the most frequently used IgH and IgK/L Vgenes. This function can be used in combination with the VDJ_Vgene_usage_barplot to vizualize V gene usage per sample and among samples.
#' @param VDJ Either (for platypus version "v2") output from VDJ_analyze function. This should be a list of clonotype dataframes, with each list element corresponding to a single VDJ repertoire, OR (for platypus version "v3") the the VDJ matrix output of the VDJ_GEX_matrix() function (normally VDJ.GEX.matrix.output[[1]])
#' @param group.by Character. Defaults to "sample_id". Column name of VDJ to group plot by.
#' @param HC.gene.number Numeric value indicating the top genes to be dispayed. If this number is higher than the total number of unique HC V genes in the VDJ repertoire, then this number is equal to the number of unique HC V genes.
#' @param Fraction.HC Numeric value indicating the minimum fraction of clones expressing a particular HC V gene. If the usage of a particular gene is below this value, then this gene is excluded. If the usage of a particular gene is above this value even in one sample, then this gene is included in the analysis. Default value is set to 0, thus all genes are selected.
#' @param LC.Vgene Logical indicating whether to make a barplot of the LC V gene distribution. Default is set to FALSE.
#' @param LC.gene.number Numeric value indicating the top genes to be dispayed. If this number is higher than the total number of unique LC V genes in the VDJ repertoire, then this number is equal to the number of unique LC V genes.
#' @param Fraction.LC Numeric value indicating the minimum fraction of clones expressing a particular LC V gene. If the usage of a particular gene is below this value, then this gene is excluded. If the usage of a particular gene is above this value even in one sample, then this gene is included in the analysis. Default value is set to 0, thus all genes are selected.
#' @param platypus.version Set according to input format to either "v2" or "v3". Defaults to "v3"
#' @param is.bulk logical value indicating whether the VDJ input was generated from bulk-sequencing data using the bulk_to_vgm function. If is.bulk = T, the VDJ_Vgene_usage_stacked_barplot function is compatible for use with bulk data. Defaults to False (F).
#' @return Returns a list of ggplot objects which show the stacked distribution of IgH and IgK/L V genes for the most used V genes. Returns an empty plot if the Fraction.HC or Fraction.LC that were selected were too high, resulting in the exclusion of all the genes.
#' @export
#' @examples
#' #Platypus v3
#' example.vdj.vgene_usage <- VDJ_Vgene_usage_stacked_barplot(
#' VDJ = Platypus::small_vgm[[1]], LC.Vgene = TRUE
#' ,HC.gene.number = 15, Fraction.HC = 1, platypus.version = "v3")
#'

VDJ_Vgene_usage_stacked_barplot <- function(VDJ,
                                            group.by,
                                            HC.gene.number,
                                            Fraction.HC,
                                            LC.Vgene,
                                            LC.gene.number,
                                            Fraction.LC,
                                            platypus.version,
                                            is.bulk){

  Vgene <- NULL
  Percentage <- NULL
  Nr_of_VDJ_chains <- NULL
  Nr_of_VJ_chains <- NULL
  sample_id <- NULL
  Sample <- NULL
  Frequency <- NULL

  if(missing(is.bulk)) is.bulk <- F
  if (missing(Fraction.HC)) Fraction.HC <- 0
  if (missing(HC.gene.number)) HC.gene.number <- 10
  if (missing(LC.Vgene)) LC.Vgene <- FALSE
  if (missing(Fraction.LC)) Fraction.LC <- 0
  if (missing(LC.gene.number)) LC.gene.number <- 10

  #naming compatibility
  VDJ.matrix <- VDJ
  VDJ <- NULL

  Vgene_usage_plot <- list()
  HC_Vgene_usage <- list()
  HC_Vgene_usage_top <- list()
  HC_Vgene_usage_fraction <- list()


  if(missing(platypus.version)) platypus.version <- "v3"

  if(platypus.version == "v2"){

    clonotype.list <- VDJ.matrix

  for (i in 1:length(clonotype.list)){
    #Calculate the V gene usage frequencies for each sample on the clonal lebel
    HC_Vgene_usage[[i]] <- as.data.frame(table(clonotype.list[[i]]$HC_vgene))
    colnames(HC_Vgene_usage[[i]]) <- c("Vgene", "Frequency")
    for (j in 1:nrow(HC_Vgene_usage[[i]])){
      HC_Vgene_usage[[i]]$Percentage[j] <- HC_Vgene_usage[[i]]$Frequency[j]/sum(HC_Vgene_usage[[i]]$Frequency)*100
    }

    #Rank based on most used V gene per sample
    ranks <- order(-HC_Vgene_usage[[i]]$Frequency)
    HC_Vgene_usage[[i]] <- HC_Vgene_usage[[i]][ranks,]
    #Find the genes which are used more than Fraction.HC of the time
    HC_Vgene_usage_fraction[[i]] <- HC_Vgene_usage[[i]][which(HC_Vgene_usage[[i]]$Percentage > Fraction.HC),]
  }
  #Find the union of unique gene names between all the samples
  HC_Vgene_names <- do.call("rbind",HC_Vgene_usage_fraction)
  HC_Vgene_names <- as.vector(unique(HC_Vgene_names$Vgene))

  #Filter out all those genes that are used less than the Fraction.HC in all samples
  for(i in 1:length(clonotype.list)){
    HC_Vgene_usage[[i]] <- HC_Vgene_usage[[i]][HC_Vgene_usage[[i]]$Vgene %in% HC_Vgene_names,]
  }

  #Determine the mean gene usage between the samples

  HC_Vgene_usage_all <- do.call("rbind", HC_Vgene_usage)
  unique_Vgene <- unique(HC_Vgene_usage_all$Vgene)

  HC_Vgene_usage_all_unique <- data.frame("Vgene"= rep("", length(unique_Vgene)), "Frequency"= rep(0, length(unique_Vgene)))
  HC_Vgene_usage_all_unique$Vgene <- unique_Vgene

  for (i in 1:length(unique_Vgene)){
    HC_Vgene_usage_all_unique[i,"Percentage"] <- mean(HC_Vgene_usage_all[which(HC_Vgene_usage_all$Vgene == HC_Vgene_usage_all_unique$Vgene[i]),]$Percentage)
  }
  # Rank the V genes based on the most used ones
  ranks <- order(-HC_Vgene_usage_all_unique$Frequency)
  HC_Vgene_usage_all_unique <- HC_Vgene_usage_all_unique[ranks,]
  top_Vgenes <- HC_Vgene_usage_all_unique$Vgene[1:HC.gene.number]

  for(i in 1:length(clonotype.list)){
    HC_Vgene_usage_top[[i]] <- data.frame("Vgene"=rep("", length(top_Vgenes)), "Frequency"=rep(0, length(top_Vgenes)))
    HC_Vgene_usage_top[[i]]$Vgene <- top_Vgenes
    for (j in 1:nrow(HC_Vgene_usage_top[[i]])){
      if (HC_Vgene_usage_top[[i]]$Vgene[j] %in% HC_Vgene_usage[[i]]$Vgene == TRUE){
        HC_Vgene_usage_top[[i]]$Frequency[j] <- HC_Vgene_usage[[i]][which(HC_Vgene_usage[[i]]$Vgene == paste0(HC_Vgene_usage_top[[i]]$Vgene[j])),]$Frequency
      }
    }
    for (j in 1:nrow(HC_Vgene_usage_top[[i]])){
      HC_Vgene_usage_top[[i]]$Percentage[j] <- HC_Vgene_usage_top[[i]]$Frequency[j]/sum(HC_Vgene_usage_top[[i]]$Frequency)*100
    }
  }

  #Assign the sample id
  for (i in 1:length(HC_Vgene_usage_top)){
    HC_Vgene_usage_top[[i]]$Sample <- as.character(i)
  }

  #And then bind together for plotting
  plotting_df <- do.call("rbind",HC_Vgene_usage_top)
  #Take out the NAs introduced because of the HC.gene.number and the Fraction.HC.
  plotting_df <- plotting_df[!is.na(plotting_df$Vgene), ]

  Vgene_usage_plot[[1]] <- ggplot2::ggplot(plotting_df, ggplot2::aes(fill = Vgene, y=Frequency, x=Sample)) +
    ggplot2::geom_bar(position="fill", stat="identity", color="black", width = 0.7) +
    ggplot2::theme_bw() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1)) +
    ggplot2::ylab("% of unique clones") + ggplot2::scale_y_continuous(expand = c(0,0))

  if(LC.Vgene ==TRUE){

    LC_Vgene_usage <- list()
    LC_Vgene_usage_top <- list()
    LC_Vgene_usage_fraction <- list()

    for (i in 1:length(clonotype.list)){

      LC_Vgene_usage[[i]] <- as.data.frame(table(clonotype.list[[i]]$LC_vgene))
      colnames(LC_Vgene_usage[[i]]) <- c("Vgene", "Frequency")
      for (j in 1:nrow(LC_Vgene_usage[[i]])){
        LC_Vgene_usage[[i]]$Percentage[j] <- LC_Vgene_usage[[i]]$Frequency[j]/sum(LC_Vgene_usage[[i]]$Frequency)*100
      }

      #Rank based on most used V gene
      ranks <- order(-LC_Vgene_usage[[i]]$Frequency)
      LC_Vgene_usage[[i]] <- LC_Vgene_usage[[i]][ranks,]

      LC_Vgene_usage_fraction[[i]] <- LC_Vgene_usage[[i]][which(LC_Vgene_usage[[i]]$Percentage > Fraction.LC),]

    }

    #Find the union of unique gene names between all the samples
    LC_Vgene_names <- do.call("rbind",LC_Vgene_usage_fraction)
    LC_Vgene_names <- as.vector(unique(LC_Vgene_names$Vgene))

    #Filter out all those genes that are used less than the Fraction.HC in all samples
    for(i in 1:length(clonotype.list)){
      LC_Vgene_usage[[i]] <- LC_Vgene_usage[[i]][LC_Vgene_usage[[i]]$Vgene %in% LC_Vgene_names,]
    }

    LC_Vgene_usage_all <- do.call("rbind", LC_Vgene_usage)
    unique_Vgene <- unique(LC_Vgene_usage_all$Vgene)

    LC_Vgene_usage_all_unique <- data.frame("Vgene"= rep("", length(unique_Vgene)), "Frequency"= rep(0, length(unique_Vgene)))
    LC_Vgene_usage_all_unique$Vgene <- unique_Vgene

    for (i in 1:length(unique_Vgene)){
      LC_Vgene_usage_all_unique[i,"Percentage"] <- mean(LC_Vgene_usage_all[which(LC_Vgene_usage_all$Vgene == LC_Vgene_usage_all_unique$Vgene[i]),]$Percentage)
    }
    # Rank the V genes based on the most used ones
    ranks <- order(-LC_Vgene_usage_all_unique$Frequency)
    LC_Vgene_usage_all_unique <- LC_Vgene_usage_all_unique[ranks,]
    top_Vgenes <- LC_Vgene_usage_all_unique$Vgene[1:LC.gene.number]

    for(i in 1:length(clonotype.list)){
      LC_Vgene_usage_top[[i]] <- data.frame("Vgene"=rep("", length(top_Vgenes)), "Frequency"=rep(0, length(top_Vgenes)))
      LC_Vgene_usage_top[[i]]$Vgene <- top_Vgenes
      for (j in 1:nrow(LC_Vgene_usage_top[[i]])){
        if (LC_Vgene_usage_top[[i]]$Vgene[j] %in% LC_Vgene_usage[[i]]$Vgene == TRUE){
          LC_Vgene_usage_top[[i]]$Frequency[j] <- LC_Vgene_usage[[i]][which(LC_Vgene_usage[[i]]$Vgene == paste0(LC_Vgene_usage_top[[i]]$Vgene[j])),]$Frequency
        }
      }
      for (j in 1:nrow(LC_Vgene_usage_top[[i]])){
        LC_Vgene_usage_top[[i]]$Percentage[j] <- LC_Vgene_usage_top[[i]]$Frequency[j]/sum(LC_Vgene_usage_top[[i]]$Frequency)*100
      }
    }

    #Assign the sample id
    for (i in 1:length(LC_Vgene_usage_top)){
      LC_Vgene_usage_top[[i]]$Sample <- as.character(i)
    }

    #And then bind together
    plotting_df <- do.call("rbind",LC_Vgene_usage_top)
    plotting_df <- plotting_df[!is.na(plotting_df$Vgene), ]

    Vgene_usage_plot[[1]] <- ggplot2::ggplot(plotting_df, ggplot2::aes(fill = Vgene, y=Frequency, x=Sample)) +
      ggplot2::geom_bar(position="fill", stat="identity", color="black", width = 0.7) +
      ggplot2::theme_bw() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1)) +
      ggplot2::ylab("% of unique clones") + ggplot2::scale_y_continuous(expand = c(0,0))

  }

  return(Vgene_usage_plot)

  } else if(platypus.version == "v3"){

    if(is.bulk == F){
      #filtering for max 1VDJ 1VJ chain
      VDJ.matrix <- subset(VDJ.matrix, Nr_of_VDJ_chains == 1 & Nr_of_VJ_chains == 1)
    }

    if(missing(group.by)) group.by <- "sample_id"
    if(group.by != "sample_id"){
      if(group.by %in% names(VDJ.matrix)){
        VDJ.matrix$sample_id <- as.character(VDJ.matrix[,group.by])
        if(any(is.na(VDJ.matrix$sample_id)) == T){
          VDJ.matrix <- VDJ.matrix[!is.na(VDJ.matrix$sample_id),]
          warning(paste0("Filtered out cells with 'NA' in grouping column"))
        }
        message(paste0("Grouping by: ", group.by))
      } else {
        warning(paste0("Group_id '",group.by, "' was not found in VDJ. Grouping by 'sample_id'"))}
    }

    clonotype.list <- list()
    for(i in 1:length(unique(VDJ.matrix$sample_id))){
      clonotype.list[[i]] <- subset(VDJ.matrix, sample_id == unique(VDJ.matrix$sample_id)[i])
      #get unique clones
      clonotype.list[[i]] <- clonotype.list[[i]][duplicated(clonotype.list[[i]]$clonotype_id) == F,]
    }
    names(clonotype.list) <- unique(VDJ.matrix$sample_id)
    message(paste0("Sample order: ", paste0(unique(VDJ.matrix$sample_id), collapse = " ; ")))

    if(LC.Vgene == F){

    Vgene_usage_plot <- list()
    HC_Vgene_usage <- list()
    HC_Vgene_usage_top <- list()
    HC_Vgene_usage_fraction <- list()


    for (i in 1:length(clonotype.list)){
      #Calculate the V gene usage frequencies for each sample on the clonal lebel
      HC_Vgene_usage[[i]] <- as.data.frame(table(clonotype.list[[i]]$VDJ_vgene))
      colnames(HC_Vgene_usage[[i]]) <- c("Vgene", "Frequency")
      for (j in 1:nrow(HC_Vgene_usage[[i]])){
        HC_Vgene_usage[[i]]$Percentage[j] <- HC_Vgene_usage[[i]]$Frequency[j]/sum(HC_Vgene_usage[[i]]$Frequency)*100
      }

      #Rank based on most used V gene per sample
      ranks <- order(-HC_Vgene_usage[[i]]$Frequency)
      HC_Vgene_usage[[i]] <- HC_Vgene_usage[[i]][ranks,]
      #Find the genes which are used more than Fraction.HC of the time
      HC_Vgene_usage_fraction[[i]] <- HC_Vgene_usage[[i]][which(HC_Vgene_usage[[i]]$Percentage > Fraction.HC),]
    }
    #Find the union of unique gene names between all the samples
    HC_Vgene_names <- do.call("rbind",HC_Vgene_usage_fraction)
    HC_Vgene_names <- as.vector(unique(HC_Vgene_names$Vgene))

    #Filter out all those genes that are used less than the Fraction.HC in all samples
    for(i in 1:length(clonotype.list)){
      HC_Vgene_usage[[i]] <- HC_Vgene_usage[[i]][HC_Vgene_usage[[i]]$Vgene %in% HC_Vgene_names,]
    }

    #Determine the mean gene usage between the samples

    HC_Vgene_usage_all <- do.call("rbind", HC_Vgene_usage)
    unique_Vgene <- unique(HC_Vgene_usage_all$Vgene)

    HC_Vgene_usage_all_unique <- data.frame("Vgene"= rep("", length(unique_Vgene)), "Frequency"= rep(0, length(unique_Vgene)))
    HC_Vgene_usage_all_unique$Vgene <- unique_Vgene

    for (i in 1:length(unique_Vgene)){
      HC_Vgene_usage_all_unique[i,"Percentage"] <- mean(HC_Vgene_usage_all[which(HC_Vgene_usage_all$Vgene == HC_Vgene_usage_all_unique$Vgene[i]),]$Percentage)
    }
    # Rank the V genes based on the most used ones
    ranks <- order(-HC_Vgene_usage_all_unique$Frequency)
    HC_Vgene_usage_all_unique <- HC_Vgene_usage_all_unique[ranks,]
    top_Vgenes <- HC_Vgene_usage_all_unique$Vgene[1:HC.gene.number]

    for(i in 1:length(clonotype.list)){
      HC_Vgene_usage_top[[i]] <- data.frame("Vgene"=rep("", length(top_Vgenes)), "Frequency"=rep(0, length(top_Vgenes)))
      HC_Vgene_usage_top[[i]]$Vgene <- top_Vgenes
      for (j in 1:nrow(HC_Vgene_usage_top[[i]])){
        if (HC_Vgene_usage_top[[i]]$Vgene[j] %in% HC_Vgene_usage[[i]]$Vgene == TRUE){
          HC_Vgene_usage_top[[i]]$Frequency[j] <- HC_Vgene_usage[[i]][which(HC_Vgene_usage[[i]]$Vgene == paste0(HC_Vgene_usage_top[[i]]$Vgene[j])),]$Frequency
        }
      }
      for (j in 1:nrow(HC_Vgene_usage_top[[i]])){
        HC_Vgene_usage_top[[i]]$Percentage[j] <- HC_Vgene_usage_top[[i]]$Frequency[j]/sum(HC_Vgene_usage_top[[i]]$Frequency)*100
      }
    }

    #Assign the sample id
    for (i in 1:length(HC_Vgene_usage_top)){
      HC_Vgene_usage_top[[i]]$Sample <-  unique(VDJ.matrix$sample_id)[i]
    }

    #And then bind together for plotting
    plotting_df <- do.call("rbind",HC_Vgene_usage_top)
    #Take out the NAs introduced because of the HC.gene.number and the Fraction.HC.
    plotting_df <- plotting_df[!is.na(plotting_df$Vgene), ]

    Vgene_usage_plot[[1]] <- ggplot2::ggplot(plotting_df, ggplot2::aes(fill = Vgene, y=Frequency, x=Sample)) +
      ggplot2::geom_bar(position="fill", stat="identity", color="black", width = 0.7) + cowplot::theme_cowplot() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1)) +
      ggplot2::ylab("% of unique clones") + ggplot2::scale_y_continuous(expand = c(0,0)) #+ ggplot2::ggtitle(paste0("IgH V gene stacked"))


    }else if(LC.Vgene ==TRUE){

      LC_Vgene_usage <- list()
      LC_Vgene_usage_top <- list()
      LC_Vgene_usage_fraction <- list()

      for (i in 1:length(clonotype.list)){

        LC_Vgene_usage[[i]] <- as.data.frame(table(clonotype.list[[i]]$VJ_vgene))
        colnames(LC_Vgene_usage[[i]]) <- c("Vgene", "Frequency")
        for (j in 1:nrow(LC_Vgene_usage[[i]])){
          LC_Vgene_usage[[i]]$Percentage[j] <- LC_Vgene_usage[[i]]$Frequency[j]/sum(LC_Vgene_usage[[i]]$Frequency)*100
        }

        #Rank based on most used V gene
        ranks <- order(-LC_Vgene_usage[[i]]$Frequency)
        LC_Vgene_usage[[i]] <- LC_Vgene_usage[[i]][ranks,]

        LC_Vgene_usage_fraction[[i]] <- LC_Vgene_usage[[i]][which(LC_Vgene_usage[[i]]$Percentage > Fraction.LC),]

      }

      #Find the union of unique gene names between all the samples
      LC_Vgene_names <- do.call("rbind",LC_Vgene_usage_fraction)
      LC_Vgene_names <- as.vector(unique(LC_Vgene_names$Vgene))

      #Filter out all those genes that are used less than the Fraction.HC in all samples
      for(i in 1:length(clonotype.list)){
        LC_Vgene_usage[[i]] <- LC_Vgene_usage[[i]][LC_Vgene_usage[[i]]$Vgene %in% LC_Vgene_names,]
      }

      LC_Vgene_usage_all <- do.call("rbind", LC_Vgene_usage)
      unique_Vgene <- unique(LC_Vgene_usage_all$Vgene)

      LC_Vgene_usage_all_unique <- data.frame("Vgene"= rep("", length(unique_Vgene)), "Frequency"= rep(0, length(unique_Vgene)))
      LC_Vgene_usage_all_unique$Vgene <- unique_Vgene

      for (i in 1:length(unique_Vgene)){
        LC_Vgene_usage_all_unique[i,"Percentage"] <- mean(LC_Vgene_usage_all[which(LC_Vgene_usage_all$Vgene == LC_Vgene_usage_all_unique$Vgene[i]),]$Percentage)
      }
      # Rank the V genes based on the most used ones
      ranks <- order(-LC_Vgene_usage_all_unique$Frequency)
      LC_Vgene_usage_all_unique <- LC_Vgene_usage_all_unique[ranks,]
      top_Vgenes <- LC_Vgene_usage_all_unique$Vgene[1:LC.gene.number]

      for(i in 1:length(clonotype.list)){
        LC_Vgene_usage_top[[i]] <- data.frame("Vgene"=rep("", length(top_Vgenes)), "Frequency"=rep(0, length(top_Vgenes)))
        LC_Vgene_usage_top[[i]]$Vgene <- top_Vgenes
        for (j in 1:nrow(LC_Vgene_usage_top[[i]])){
          if (LC_Vgene_usage_top[[i]]$Vgene[j] %in% LC_Vgene_usage[[i]]$Vgene == TRUE){
            LC_Vgene_usage_top[[i]]$Frequency[j] <- LC_Vgene_usage[[i]][which(LC_Vgene_usage[[i]]$Vgene == paste0(LC_Vgene_usage_top[[i]]$Vgene[j])),]$Frequency
          }
        }
        for (j in 1:nrow(LC_Vgene_usage_top[[i]])){
          LC_Vgene_usage_top[[i]]$Percentage[j] <- LC_Vgene_usage_top[[i]]$Frequency[j]/sum(LC_Vgene_usage_top[[i]]$Frequency)*100
        }
      }

      #Assign the sample id
      for (i in 1:length(LC_Vgene_usage_top)){
        LC_Vgene_usage_top[[i]]$Sample <- unique(VDJ.matrix$sample_id)[i]
      }

      #And then bind together
      plotting_df <- do.call("rbind",LC_Vgene_usage_top)
      plotting_df <- plotting_df[!is.na(plotting_df$Vgene), ]

      Vgene_usage_plot[[1]] <- ggplot2::ggplot(plotting_df, ggplot2::aes(fill = Vgene, y=Frequency, x=Sample)) +
        ggplot2::geom_bar(position="fill", stat="identity", color="black", width = 0.7) + cowplot::theme_cowplot() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1)) +
        ggplot2::ylab("% of unique clones") + ggplot2::scale_y_continuous(expand = c(0,0)) #+ ggplot2::ggtitle(paste0("IgK/L V gene stacked"))

    }

  }
}