K562_GAT-analysis.R

# This R script will read blocks of GAT shuffling results (in whitelisted genome (WL), open chromatin (DHS), OQS and 'open' OQS) combine results with ENCODE meta data


# ======= Libraries and working directory
library(data.table)
library(dplyr)
library(ggplot2)
library(ggsignif)
library(ggpubr)
library(scales)

setwd('ENCODE_Analysis/')


# ==== Script parameters
plotdata <- 1
savetables <- 1


# ===  list of segments that were shuffled in different backgrounds
GAT_segments <- c('BG4_Rerun') 

# ====== Parameters for Filtering the data ====
qValue_cutoff <-  0.05
PEAK_NUMBER_Cutoff <- 100 

Explicit_exclusion <- c("ENCFF782GWS") # ("HNRNPF", "RBM15", "RBM17", "RBM34") Datasets have been flagged in original paper (Cell 2019 (10.1016/j.cell.2019.06.001)), but not yet on ENCODE


# ====== Load meta data
ENCODE_bed_meta <- read.table(file = "K562_Rerun_Jan2019/ENCODE_K562_Jan2019_BED_Meta.tsv", sep = '\t', header = TRUE)
ENCODE_bed_meta$Experiment.target <- gsub("-human", "", ENCODE_bed_meta$Experiment.target)
ENCODE_bed_meta <- ENCODE_bed_meta[rowSums(is.na(ENCODE_bed_meta))  != ncol(ENCODE_bed_meta), ] # Remove empty rows


#read in peak numbers
Peak_Number <- read.table(file="K562_Rerun_Jan2019/GAT_Rerun2019_output/K562_Jan2019_NumberOfPeaks.txt", sep= '\t', header = FALSE)
colnames(Peak_Number)[1] <- "ENCODE_ID"
colnames(Peak_Number)[2] <- "Peaks"
Peak_Number$ENCODE_ID <- sapply(as.vector(Peak_Number$ENCODE_ID), function(x) {gsub("\\..*","",x)}) # strip extension
ENCODE_bed_meta$Peaks <- Peak_Number$Peaks[match(ENCODE_bed_meta$File.accession, Peak_Number$ENCODE_ID)]


# =====================================================================================================================================================
# == Cycle Through blocks of GAT results as defined in GAT_segments
# =====================================================================================================================================================
for (i in 1:length(GAT_segments))
{
  Gat_Seg <- (GAT_segments)[i]
  
  
  # =====================================================================================================================================================
  # Load result tables for four GAT runs using different workplaces (Whitelisted genome, DNAse-hypersensitivity sites, Observed Quadruplexes, OQs at DHS)
  # =====================================================================================================================================================
  
  # ====== shuffle in whitelisted genome  
  Gat_WL <- read.table(file =paste("K562_Rerun_Jan2019/GAT_Rerun2019_output/GAT_K562_", Gat_Seg, "_Jan2019.WL.tsv",  sep="") , sep = '\t', header = TRUE) 
  Gat_WL <- Gat_WL[, 2:11]
  colnames(Gat_WL)[1] <- "ENCODE_ID"
  Gat_WL$ENCODE_ID <- gsub(".cut.bed.gz", "", Gat_WL$ENCODE_ID)
  ###Sort by rank and include and extra column 'rank' to see how high the marker scored
  Gat_WL <- Gat_WL[order(Gat_WL$fold, decreasing = TRUE), ]
  Gat_WL$rank <- seq.int(nrow(Gat_WL))
  
  
  # ===== shuffle in  DHS (open chromatin)
  Gat_DHS <- read.table(file = paste("K562_Rerun_Jan2019/GAT_Rerun2019_output/GAT_K562_", Gat_Seg, "_Jan2019.DHS.tsv",  sep=""), sep = '\t', header = TRUE)
  Gat_DHS <- Gat_DHS[, 2:11]
  colnames(Gat_DHS)[1] <- "ENCODE_ID"
  Gat_DHS$ENCODE_ID <- gsub(".cut.bed.gz", "", Gat_DHS$ENCODE_ID)
  ###Sort by rank and include and extra column 'rank' to see how high the marker scored
  Gat_DHS <- Gat_DHS[order(Gat_DHS$fold, decreasing = TRUE), ]
  Gat_DHS$rank <- seq.int(nrow(Gat_DHS))
  
  
  # ========  shuffle in observed Quadruplexes
  Gat_OQs <- read.table(file = paste("K562_Rerun_Jan2019/GAT_Rerun2019_output/GAT_K562_", Gat_Seg, "_Jan2019.OQS.tsv",  sep=""), sep = '\t', header = TRUE)
  Gat_OQs <- Gat_OQs[, 2:11]
  colnames(Gat_OQs)[1] <- "ENCODE_ID"
  Gat_OQs$ENCODE_ID <- gsub(".cut.bed.gz", "", Gat_OQs$ENCODE_ID)
  ###Sort by rank and include and extra column 'rank' to see how high the marker scored
  Gat_OQs <- Gat_OQs[order(Gat_OQs$fold, decreasing = TRUE), ]
  Gat_OQs$rank <- seq.int(nrow(Gat_OQs))
  
  
  # ==== shuffle in observed Quadruplexes at DHS
  Gat_openOQs <- read.table(file = paste("K562_Rerun_Jan2019/GAT_Rerun2019_output/GAT_K562_", Gat_Seg, "_Jan2019.opOQS.tsv",  sep=""), sep = '\t', header = TRUE)
  Gat_openOQs <- Gat_openOQs[, 2:11]
  colnames(Gat_openOQs)[1] <- "ENCODE_ID"
  Gat_openOQs$ENCODE_ID <- gsub(".cut.bed.gz", "", Gat_openOQs$ENCODE_ID)
  ###Sort by rank and include and extra column 'rank' to see how high the marker scored
  Gat_openOQs <- Gat_openOQs[order(Gat_openOQs$fold, decreasing = TRUE), ]
  Gat_openOQs$rank <- seq.int(nrow(Gat_openOQs))
  
  # ============================================================================================================================================================
  # === Generate a Merged data sheet comprising ENCODE meta data and GAT analysis for the different runs ====
  # ============================================================================================================================================================
  
  Merged_all <- ENCODE_bed_meta
  
  #whitelisted genome
  Merged_all$WL_observed <- Gat_WL$observed[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_expected <- Gat_WL$expected[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_CI95low <- Gat_WL$CI95low[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_CI95high <- Gat_WL$CI95high[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_stddev <- Gat_WL$stddev[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_fold <- Gat_WL$fold[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_l2fold <- Gat_WL$l2fold[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_pvalue <- Gat_WL$pvalue[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_qvalue <- Gat_WL$qvalue[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  Merged_all$WL_rank <- Gat_WL$rank[match(Merged_all$File.accession, Gat_WL$ENCODE_ID)]
  
  #DHS (open chromatin)
  Merged_all$DHS_observed <- Gat_DHS$observed[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_expected <- Gat_DHS$expected[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_CI95low <- Gat_DHS$CI95low[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_CI95high <- Gat_DHS$CI95high[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_stddev <- Gat_DHS$stddev[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_fold <- Gat_DHS$fold[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_l2fold <- Gat_DHS$l2fold[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_pvalue <- Gat_DHS$pvalue[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_qvalue <- Gat_DHS$qvalue[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  Merged_all$DHS_rank <- Gat_DHS$rank[match(Merged_all$File.accession, Gat_DHS$ENCODE_ID)]
  
  #observed Quadruplexes
  Merged_all$OQs_observed <- Gat_OQs$observed[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_expected <- Gat_OQs$expected[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_CI95low <- Gat_OQs$CI95low[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_CI95high <- Gat_OQs$CI95high[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_stddev <- Gat_OQs$stddev[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_fold <- Gat_OQs$fold[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_l2fold <- Gat_OQs$l2fold[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_pvalue <- Gat_OQs$pvalue[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_qvalue <- Gat_OQs$qvalue[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  Merged_all$OQs_rank <- Gat_OQs$rank[match(Merged_all$File.accession, Gat_OQs$ENCODE_ID)]
  
  #observed Quadruplexes at DHS
  Merged_all$opOQs_observed <- Gat_openOQs$observed[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_expected <- Gat_openOQs$expected[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_CI95low <- Gat_openOQs$CI95low[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_CI95high <- Gat_openOQs$CI95high[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_stddev <- Gat_openOQs$stddev[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_fold <- Gat_openOQs$fold[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_l2fold <- Gat_openOQs$l2fold[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_pvalue <- Gat_openOQs$pvalue[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_qvalue <- Gat_openOQs$qvalue[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  Merged_all$opOQs_rank <- Gat_openOQs$rank[match(Merged_all$File.accession, Gat_openOQs$ENCODE_ID)]
  
  Merged_all <- Merged_all[rowSums(is.na(Merged_all)) != ncol(Merged_all), ] # remove empty rows 
  
  # export all data unfiltered
  if (savetables)
  {
    write.csv(Merged_all, file =paste("K562_Rerun_Jan2019/GAT_analysis/GAT_K562_", Gat_Seg, "_Jan2019_Gat-Analysis_all.csv",  sep=""))
  }
  
  
  #====== FILTER  q-values 
  # Most filtering has been performed prior to downloading the bed-files. 
  # keep cases where shuffling was successfull in case of DHS as this will be the reference and at least 100 peaks to remove low quality ChIP-seq maps
  GAT_FILTERED <- Merged_all[ Merged_all$DHS_qvalue < qValue_cutoff , ]
  GAT_FILTERED <- Merged_all[Merged_all$Peaks > PEAK_NUMBER_Cutoff, ]
  
 #Remove data sets that have been explicitly flagged in publications, but not yet by ENCODE
  GAT_FILTERED <- GAT_FILTERED[!(GAT_FILTERED$File.accession  %in% Explicit_exclusion), ]
  
  
  # Update relative ranking after removing datasets
  GAT_FILTERED <- GAT_FILTERED[order(GAT_FILTERED$WL_fold, decreasing = TRUE), ]
  GAT_FILTERED$WL_rank <- seq.int(nrow(GAT_FILTERED))
  GAT_FILTERED <- GAT_FILTERED[order(GAT_FILTERED$DHS_fold, decreasing = TRUE), ]
  GAT_FILTERED$DHS_rank <- seq.int(nrow(GAT_FILTERED))
  GAT_FILTERED <- GAT_FILTERED[order(GAT_FILTERED$OQs_fold, decreasing = TRUE), ]
  GAT_FILTERED$OQs_rank <- seq.int(nrow(GAT_FILTERED))
  GAT_FILTERED <- GAT_FILTERED[order(GAT_FILTERED$opOQs_fold, decreasing = TRUE), ]
  GAT_FILTERED$opOQs_rank <- seq.int(nrow(GAT_FILTERED))
  
  if(savetables)
  {
    write.csv(GAT_FILTERED, file =paste("K562_Rerun_Jan2019/GAT_analysis/GAT_K562_", Gat_Seg, '_', PEAK_NUMBER_Cutoff, "Peaks_Jan2019_Gat-Analysis_filt.csv",  sep=""))
  }
  
  
  # ===========Trimmed and Condensed version of filterd===================================
  # Generate a trimmed  version only containing most relevant parameters
  GAT_FILTERED_trim <- GAT_FILTERED[,  c("File.accession", "Experiment.accession", "Experiment.target", "Peaks", "WL_fold", "WL_qvalue", "WL_rank", "DHS_fold", "DHS_qvalue", "DHS_rank", "OQs_fold", "OQs_qvalue", "OQs_rank", "opOQs_fold", "opOQs_qvalue", "opOQs_rank")] 
  GAT_FILTERED_trim$average_rank <- rowMeans(GAT_FILTERED_trim[, c("WL_rank",  "DHS_rank", "OQs_rank", "opOQs_rank")])
  GAT_FILTERED_trim <- GAT_FILTERED_trim[order(GAT_FILTERED_trim$average_rank, decreasing = FALSE), ]
  
  if(savetables)
  {
    write.csv(GAT_FILTERED_trim, file =paste("K562_Rerun_Jan2019/GAT_analysis/GAT_K562_", Gat_Seg, '_', PEAK_NUMBER_Cutoff, "Peaks_Jan2019_Gat-Analysis_trim.csv",  sep=""))
  }
  
  
  
  # ====== Aggregate by Target
  # Comparing enrichment over different cell lines as different experiments in K562 resulted in different enrichments. Aggregate by target with both by highest and  average fold enrichment per target.
  # Tagged and fusion proteins will be treated as unmodified proteins
  
  # remove information on tag
  ENCODE_bed_meta$Experiment.target <- gsub("-human", "", ENCODE_bed_meta$Experiment.target)
  
  
  
  GAT_FILTERED_trim_by_Target <- GAT_FILTERED_trim[, c("Experiment.target", "WL_fold", "DHS_fold", "OQs_fold", "opOQs_fold")]
  # remove information on tag
  GAT_FILTERED_trim_by_Target$Experiment.target <- gsub("eGFP-", "", GAT_FILTERED_trim_by_Target$Experiment.target)
  GAT_FILTERED_trim_by_Target$Experiment.target <- gsub("3xFLAG-", "" , GAT_FILTERED_trim_by_Target$Experiment.target)
  
  # aggregate and keep maximum enrichment
  GAT_FILTERED_trim_by_Target_max <- aggregate(GAT_FILTERED_trim_by_Target[,2:5], by = list(Exper_Target_List=GAT_FILTERED_trim_by_Target$Experiment.target), FUN = max , simplify = TRUE)
  
  GAT_FILTERED_trim_by_Target_max <- GAT_FILTERED_trim_by_Target_max[order(GAT_FILTERED_trim_by_Target_max$WL_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_max$WL_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_max))
  GAT_FILTERED_trim_by_Target_max <- GAT_FILTERED_trim_by_Target_max[order(GAT_FILTERED_trim_by_Target_max$DHS_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_max$DHS_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_max))
  GAT_FILTERED_trim_by_Target_max <- GAT_FILTERED_trim_by_Target_max[order(GAT_FILTERED_trim_by_Target_max$OQs_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_max$OQs_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_max))
  GAT_FILTERED_trim_by_Target_max <- GAT_FILTERED_trim_by_Target_max[order(GAT_FILTERED_trim_by_Target_max$opOQs_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_max$opOQs_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_max))
  
  
  # aggregate using the mean enrichment
  GAT_FILTERED_trim_by_Target_mean <- aggregate(GAT_FILTERED_trim_by_Target[,2:5], by = list(Exper_Target_List=GAT_FILTERED_trim_by_Target$Experiment.target), FUN = mean , simplify = TRUE)
  
  GAT_FILTERED_trim_by_Target_mean <- GAT_FILTERED_trim_by_Target_mean[order(GAT_FILTERED_trim_by_Target_mean$WL_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_mean$WL_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_mean))
  GAT_FILTERED_trim_by_Target_mean <- GAT_FILTERED_trim_by_Target_mean[order(GAT_FILTERED_trim_by_Target_mean$DHS_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_mean$DHS_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_mean))
  GAT_FILTERED_trim_by_Target_mean <- GAT_FILTERED_trim_by_Target_mean[order(GAT_FILTERED_trim_by_Target_mean$OQs_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_mean$OQs_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_mean))
  GAT_FILTERED_trim_by_Target_mean <- GAT_FILTERED_trim_by_Target_mean[order(GAT_FILTERED_trim_by_Target_mean$opOQs_fold, decreasing = TRUE), ]
  GAT_FILTERED_trim_by_Target_mean$opOQs_rank <- seq.int(nrow(GAT_FILTERED_trim_by_Target_mean))
  
  if (savetables)
  {
    write.csv(GAT_FILTERED_trim_by_Target_max, file =paste("K562_Rerun_Jan2019/GAT_analysis/GAT_K562_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-max.csv",  sep=""))
    write.csv(GAT_FILTERED_trim_by_Target_mean, file =paste("K562_Rerun_Jan2019/GAT_analysis/GAT_K562_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.csv",  sep=""))
  }
  
  # ==========
  # Plot Data  
  # =========
  if (plotdata)
  {
    #for plots remove all the additional tag information: eGFP, FLAG, etc.
    GAT_FILTERED_trim$Experiment.target <- gsub("eGFP-", "", GAT_FILTERED_trim$Experiment.target)
    GAT_FILTERED_trim$Experiment.target <- gsub("3xFLAG-", "" , GAT_FILTERED_trim$Experiment.target)
    
    # assign colors based on known features
    Known_G4_proteins <- read.csv(file = "K562_Rerun_Jan2019/GAT_analysis/G4IPD_Oct2019.csv", header = F)
    temp <- GAT_FILTERED_trim
    temp$G4IPD <- ifelse(temp$Experiment.target %in% Known_G4_proteins$V1, "#aad2a5", "grey50")
    
    # highlight candidates that are not considered "canonical TFs" (see http://humantfs.ccbr.utoronto.ca/) 
    Known_TF <- read.csv(file = "Human_TFs_Dataset/DatabaseExtract_v_1.01.csv", header = T)
    Known_TF <- Known_TF[, c("HGNC.symbol", "Is.TF." )]
    Known_TF <- Known_TF[Known_TF$Is.TF. == "No",]
    temp$Is.TF <- ifelse(temp$Experiment.target %in% Known_TF$HGNC.symbol, "#E98B0B", "grey50")
    
    
    # =====
    # =DHS
    # =====
    
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    
    # all
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(DHS_rank)), y=DHS_fold)) + 
      geom_bar(stat="identity", fill=rev(temp_plot$G4IPD) ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      scale_x_discrete(labels="") +
      geom_hline(yintercept=5, linetype="dashed", color = "grey")+
      geom_hline(yintercept=10, linetype="dashed", color = "grey")+
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_all_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 22/2.54, limitsize = FALSE)
    
    
    # top 20
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    temp_plot <- temp_plot[1:20,]
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(DHS_rank)), y=DHS_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_top20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
    
    # bottom 20
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    temp_plot <- tail(temp_plot, 20)
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(DHS_rank)), y=DHS_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) +
      #annotation_logticks(sides= "l") + # unfortunately, this seems incompatible with coord_flip() -> have to edit this manually in illustrator :/
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_bottom20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
    
    
    
    
    #####################
    ### other orientation
    
    # all
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    
    gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
      geom_bar(stat="identity", fill=(temp_plot$G4IPD) ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
      scale_x_discrete(labels="") +
      geom_hline(yintercept=5, linetype="dashed", color = "grey30", size = 0.3 )+
      geom_hline(yintercept=10, linetype="dashed", color = "grey30", size = 0.3)
    plot(gg)
    
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_all_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_rotated.pdf",  sep=""), width = 20/2.54, height = 6/2.54, limitsize = FALSE)
    
    # top 20
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    temp_plot <- temp_plot[1:20,]
    
    gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
      geom_bar(stat="identity",color="black", fill=(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      theme(axis.text.x = element_text(angle = 90))+
      scale_x_discrete(labels=(temp_plot$Experiment.target)) 
    plot(gg)
    
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_top20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_rotated.pdf",  sep=""), width = 9/2.54, height = 6/2.54, limitsize = FALSE)
    
    
    
    
    # bottom 20
    temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
    temp_plot <- tail(temp_plot, 20)
    
    gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
      annotation_logticks(sides= "l")+
      geom_bar(stat="identity",color="black", fill=(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in DHS") +
      theme_minimal() +
      theme(axis.text.x = element_text(angle = 90))+
      scale_x_discrete(labels=(temp_plot$Experiment.target)) +
      scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) 
    # unfortunately, this seems incompatible with coord_flip() -> have to edit this manually in illustrator :/
    plot(gg)
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_bottom20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_rotated.pdf",  sep=""), width = 9/2.54, height = 6/2.54, limitsize = FALSE)
    
    
                                          
#############################
## Highlight non-canonical TFs
                                       
                                          
# all
temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]

gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
  geom_bar(stat="identity", fill=(temp_plot$Is.TF) ) +
  xlab("") +
  ylab("fold enrichment in DHS") +
  theme_minimal() +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  scale_x_discrete(labels="") +
  geom_hline(yintercept=5, linetype="dashed", color = "grey30", size = 0.3 )+
  geom_hline(yintercept=10, linetype="dashed", color = "grey30", size = 0.3)
plot(gg)

ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_all_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_TF.pdf",  sep=""), width = 20/2.54, height = 6/2.54, limitsize = FALSE)

# top 20
temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
temp_plot <- temp_plot[1:20,]

gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
  geom_bar(stat="identity",color="black", fill=(temp_plot$Is.TF), width=0.8 ) +
  xlab("") +
  ylab("fold enrichment in DHS") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))+
  scale_x_discrete(labels=(temp_plot$Experiment.target)) 
plot(gg)

ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_top20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_TF.pdf",  sep=""), width = 9/2.54, height = 6/2.54, limitsize = FALSE)




# bottom 20
temp_plot <- temp[order(temp$DHS_fold, decreasing = TRUE), ]
temp_plot <- tail(temp_plot, 20)

gg <- ggplot(temp_plot, aes(x=as.factor((DHS_rank)), y=DHS_fold)) + 
  annotation_logticks(sides= "l")+
  geom_bar(stat="identity",color="black", fill=(temp_plot$Is.TF), width=0.8 ) +
  xlab("") +
  ylab("fold enrichment in DHS") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))+
  scale_x_discrete(labels=(temp_plot$Experiment.target)) +
  scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) 
plot(gg)
ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/DHS_bottom20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean_TF.pdf",  sep=""), width = 9/2.54, height = 6/2.54, limitsize = FALSE)                                     
                                          
                                          
    
    
    # =====
    # =opOQS
    # =====
    
    temp_plot <- temp[order(temp$opOQs_fold, decreasing = TRUE), ]
    
    # all
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(opOQs_rank)), y=opOQs_fold)) + 
      geom_bar(stat="identity", fill=rev(temp_plot$G4IPD) ) +
      xlab("") +
      ylab("fold enrichment in opOQs") +
      theme_minimal() +
      scale_x_discrete(labels="") +
      geom_hline(yintercept=5, linetype="dashed", color = "grey")+
      geom_hline(yintercept=10, linetype="dashed", color = "grey")+
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/opOQs_all_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 22/2.54, limitsize = FALSE)
    
    
    # top 20
    temp_plot <- temp[order(temp$opOQs_fold, decreasing = TRUE), ]
    temp_plot <- temp_plot[1:20,]
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(opOQs_rank)), y=opOQs_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in opOQs") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/opOQs_top20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
    
    # bottom 20
    temp_plot <- temp[order(temp$opOQs_fold, decreasing = TRUE), ]
    temp_plot <- tail(temp_plot, 20)
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(opOQs_rank)), y=opOQs_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in opOQs") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) +
      #annotation_logticks(sides= "l") + # unfortunately, this seems incompatible with coord_flip() -> have to edit this manually in illustrator :/
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/opOQs_bottom20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
    
    # =====
    # = WL
    # =====
    
    temp_plot <- temp[order(temp$WL_fold, decreasing = TRUE), ]
    
    # all
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(WL_rank)), y=WL_fold)) + 
      geom_bar(stat="identity", fill=rev(temp_plot$G4IPD) ) +
      xlab("") +
      ylab("fold enrichment in WL") +
      theme_minimal() +
      scale_x_discrete(labels="") +
      geom_hline(yintercept=100, linetype="dashed", color = "grey")+
      geom_hline(yintercept=200, linetype="dashed", color = "grey")+
      geom_hline(yintercept=300, linetype="dashed", color = "grey")+
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/WL_all_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 22/2.54, limitsize = FALSE)
    
    
    # top 20
    temp_plot <- temp[order(temp$WL_fold, decreasing = TRUE), ]
    temp_plot <- temp_plot[1:20,]
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(WL_rank)), y=WL_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in WL") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/WL_top20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
    
    # bottom 20
    temp_plot <- temp[order(temp$WL_fold, decreasing = TRUE), ]
    temp_plot <- tail(temp_plot, 20)
    
    gg <- ggplot(temp_plot, aes(x=as.factor(rev(WL_rank)), y=WL_fold)) + 
      geom_bar(stat="identity",color="black", fill=rev(temp_plot$G4IPD), width=0.8 ) +
      xlab("") +
      ylab("fold enrichment in WL") +
      theme_minimal() +
      scale_x_discrete(labels=rev(temp_plot$Experiment.target)) +
      scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) +
      #annotation_logticks(sides= "l") + # unfortunately, this seems incompatible with coord_flip() -> have to edit this manually in illustrator :/
      coord_flip()
    ggsave(file =paste("K562_Rerun_Jan2019/GAT_analysis/figures/WL_bottom20_", Gat_Seg, "_Jan2019_Gat-Analysis_byTarget-mean.pdf",  sep=""), width = 6/2.54, height = 8/2.54, limitsize = FALSE)
    
  }

}