Last updated: 2018-08-30

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    Rmd a9478f8 kevinlkx 2018-08-30 plot ATAC-seq footprint profiles using HAEC data

functions

## load and combine count matrices
load_combine_counts <- function(bam_basename, pwm_name, dir_count_matrix){
  cat("Loading count matrices ... \n")
  counts_fwd.df <- read.table(paste0(dir_count_matrix, "/", pwm_name, "/", pwm_name, "_", bam_basename, "_fwdcounts.m.gz"))
  counts_rev.df <- read.table(paste0(dir_count_matrix, "/", pwm_name, "/", pwm_name, "_", bam_basename, "_revcounts.m.gz"))
  
  ## the first 5 columns from "bwtool extract" are chr, start, end, name, and the number of data points
  counts_fwd.df <- counts_fwd.df[, -c(1:5)]
  counts_rev.df <- counts_rev.df[, -c(1:5)]
  
  colnames(counts_fwd.df) <- paste0("fwd", 1:ncol(counts_fwd.df))
  colnames(counts_rev.df) <- paste0("rev", 1:ncol(counts_rev.df))
  
  counts_combined.m <- as.matrix(cbind(counts_fwd.df, counts_rev.df))
  
  return(counts_combined.m)
}

## select candidate sites by mapability and PWM score cutoffs
select_sites <- function(sites.df, thresh_mapability=NULL, thresh_PWMscore=NULL, readstats_name=NULL){
  #  cat("loading sites ...\n")
  
  if(!is.null(thresh_mapability)){
    cat("Select candidate sites with mapability >=", thresh_mapability, "\n")
    idx_mapability <- (sites.df[,"mapability"] >= thresh_mapability)
  }else{
    idx_mapability <- rep(TRUE, nrow(sites.df))
  }
  
  if(!is.null(thresh_PWMscore)){
    cat("Select candidate sites with PWM score >=", thresh_PWMscore, "\n")
    idx_pwm <- (sites.df[,"pwm_score"] >= thresh_PWMscore)
  }else{
    idx_pwm <- rep(TRUE, nrow(sites.df))
  }
  
  if(!is.null(readstats_name)){
    readstats.df <- read.table(readstats_name, header = F)
    ## if the readstats.df contains chrY, then it means the cell type is male, then the candidate sites should contain chrY,
    ## otherwise, the cell type is female, then the candidate sites on chrY should be removed.
    if( "chrY" %in% readstats.df[,1] ){
      cat("include chrY sites \n")
      idx_chr <- (sites.df[,1] != "")
    }else{
      cat("chrY NOT in the bam file, filter out chrY sites \n")
      ## remove chrY from candidate (motif) sites
      idx_chr <- (sites.df[,1] != "chrY")
    }
    
  }else{
    idx_chr <- rep(TRUE, nrow(sites.df))
  }
  
  idx_select <- which(idx_mapability & idx_pwm & idx_chr)
  
  return(idx_select)
}


## plot footprint profiles for selected sites
plot_footprint_sites <- function(tf_name, pwm_name, treatment, flank = 100, thresh_mapability = NULL, thresh_PWMscore = 0, num_top_sites = 1000){
  
  filename_sites <- paste0(dir_sites, "/", pwm_name, "_flank", flank, "_fimo_sites.bed")
  
  sites.df <- read.table(filename_sites, header = T, comment.char = "!", stringsAsFactors = F)
  
  if(ver_genome == "hg38"){
    thresh_mapability <- NULL
    colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value")
  }else{
    colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value", "mapability")
  }
  
  readstats_name <-  paste0(dir_bam_idxstats, "/", "H1_nomito_rdup.idxstats.txt")
  
  idx_select <- select_sites(sites.df, thresh_mapability, thresh_PWMscore, readstats_name)
  
  sites.df <- sites.df[idx_select, c("chr", "start", "end", "name", "pwm_score", "strand", "p_value")]
  cat("Number of sites:", nrow(sites.df), "\n")
  
  counts_rep1.m <- load_combine_counts(paste0(treatment, "1_nomito_rdup"), pwm_name, dir_count_matrix)[idx_select,]
  counts_rep2.m <- load_combine_counts(paste0(treatment, "2_nomito_rdup"), pwm_name, dir_count_matrix)[idx_select,]
  counts_rep3.m <- load_combine_counts(paste0(treatment, "3_nomito_rdup"), pwm_name, dir_count_matrix)[idx_select,]
  counts_combined.m <- counts_rep1.m + counts_rep2.m + counts_rep3.m
  
  ## Clip extreme values
  counts_combined.m[counts_combined.m > max_cuts] <- max_cuts
  
  cat("Dimension of", dim(counts_combined.m), "\n")
  
  if(nrow(counts_combined.m) != nrow(sites.df)){
    stop("Sites not matched!")
  }
  
  ## plot ATAC-seq cut profiles of most accessible sites 
  order_selected <- order(rowSums(counts_combined.m), decreasing = T)[1:num_top_sites]
  counts_selected.m <- counts_combined.m[order_selected,]
  counts_profile <- apply(counts_selected.m, 2, mean)
  
  par(mfrow = c(1,2))
  counts <- counts_profile[1:(length(counts_profile)/2)]
  plot(counts, type = "l", col = "blue", xlab = "Relative position (bp)", ylab = "Average counts", 
       main = "", xaxt = "n")
  mtext(text = paste(tf_name, treatment, "forward strand"), side = 3, line = 1, cex = 1)
  axis(1,at=c(1, flank+1, length(counts)-flank, length(counts)), labels=c(-flank, '','' ,flank), 
       cex.axis = 1, tck=-0.03, tick = T, cex = 1)
  
  counts <- counts_profile[(length(counts_profile)/2+1): length(counts_profile)]
  
  plot(counts, type = "l", col = "red", xlab = "Relative position (bp)", ylab = "Average counts", 
       main = "", xaxt = "n")
  mtext(text = paste(tf_name, treatment, "reverse strand"), side = 3, line = 1, cex = 1)
  axis(1,at=c(1, flank+1, length(counts)-flank, length(counts)), labels=c(-flank, '','' ,flank), 
       cex.axis = 1, tck=-0.03, tick = T, cex = 1)
  
  
  ## save counts matrix
  saveRDS(counts_selected.m, paste0(dir_matrix_examples, "/", pwm_name, "_", treatment, "_reps_counts_selected_sites.rds"))
  
}

parameters

ver_genome <- "hg19"
flank <- 100
thresh_mapability <- 0.8
thresh_PWMscore <- 10
num_top_sites <- 1000
bam_name_list <- c("H1_nomito_rdup.bam", "N1_nomito_rdup.bam")
max_cuts <- 20 # Clip extreme values

dir_data <- "~/Dropbox/research/ATAC_DNase/"

dir_count_matrix <- paste0(dir_data, "/ATAC-seq_Olivia_Gray/results/ATAC-seq_count_matrix/")
dir_bam_idxstats <- paste0(dir_data, "/ATAC-seq_Olivia_Gray/ATAC-seq_BAMfiles/")
dir_sites <- paste0(dir_data, "/motif_sites_JASPAR2018/hg19/candidate_sites/1e-4/")

dir_matrix_examples <- paste0(dir_data, "/ATAC-seq_Olivia_Gray/results/ATAC-seq_count_matrix_examples/")
dir.create(dir_matrix_examples, showWarnings = F, recursive = T)

ATAC-seq footprint profiles for CTCF

tf_name <- "CTCF"
pwm_name <- "CTCF_MA0139.1_1e-4"

plot_footprint_sites(tf_name, pwm_name, treatment = "H", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 196871 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 196871 436 

plot_footprint_sites(tf_name, pwm_name, treatment = "N", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 196871 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 196871 436

ATAC-seq footprint profiles for HIF1A

tf_name <- "HIF1A"
pwm_name <- "HIF1A_MA1106.1_1e-4"

plot_footprint_sites(tf_name, pwm_name, treatment = "H", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 69233 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 69233 418 

plot_footprint_sites(tf_name, pwm_name, treatment = "N", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 69233 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 69233 418

ATAC-seq footprint profiles for MEF2D

tf_name <- "MEF2D"
pwm_name <- "MEF2D_MA0773.1_1e-4"

plot_footprint_sites(tf_name, pwm_name, treatment = "H", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 230812 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 230812 422 

plot_footprint_sites(tf_name, pwm_name, treatment = "N", flank, thresh_mapability, thresh_PWMscore, num_top_sites)
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10 
include chrY sites 
Number of sites: 230812 
Loading count matrices ... 
Loading count matrices ... 
Loading count matrices ... 
Dimension of 230812 422

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.16      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.21.0      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.1.7     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.9     tools_3.4.3      
[16] stringr_1.3.0     yaml_2.1.18       compiler_3.4.3   
[19] htmltools_0.3.6   knitr_1.20

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