DNase footprint profiles using ENCODE DNase-seq data

Last updated: 2018-08-30

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File	Version	Author	Date	Message
Rmd	2783ac9	kevinlkx	2018-08-30	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	dcc47c4	kevinlkx	2018-08-29	Build site.
Rmd	eed77b5	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	7adcba7	kevinlkx	2018-08-29	Build site.
Rmd	ed49eff	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	57479c5	kevinlkx	2018-08-29	Build site.
Rmd	bb65514	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	98209cf	kevinlkx	2018-08-29	Build site.
Rmd	2f894eb	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	0e20593	kevinlkx	2018-08-29	Build site.
Rmd	a233f11	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	b182c05	kevinlkx	2018-08-29	Build site.
Rmd	09836f9	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data
html	35c5feb	kevinlkx	2018-08-29	Build site.
Rmd	f318dd5	kevinlkx	2018-08-29	plot DNase footprint profiles of CTCF and REST using ENCODE data

functions

##### Functions #####
## load and combine count matrices
load_combine_counts <- function(tf_name, pwm_name, dir_count_matrix){
  cat("Loading count matrices ... \n")
  counts_fwd.df <- read.table(paste0(dir_count_matrix, "/", tf_name, "/", pwm_name, "_hg19_dnase_fwdcounts.m.gz"))
  counts_rev.df <- read.table(paste0(dir_count_matrix, "/", tf_name, "/", pwm_name, "_hg19_dnase_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")
  cat("Select candidate sites \n")
  
  if(!is.null(thresh_mapability) || !is.na(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) || !is.na(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)
}

parameters

ver_genome <- "hg19"
flank <- 100
thresh_mapability <- 0.8
thresh_PWMscore <- 10
num_top_sites <- 1000 # plot top sites
max_cuts <- 20 # Clip extreme values
dir_data <- "~/Dropbox/research/ATAC_DNase/"

CTCF in GM12878 cell type

load DNase footprint data

cell_type <- "GM12878"
tf_name <- "CTCF"
pwm_name <- "CTCF_MA0139.1_1e-5"

dir_count_matrix <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_tagcount_matrix/")
dir_sites_chip <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/ChIPSeq/")

dir_matrix_examples <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_count_matrix_examples/")
dir.create(dir_matrix_examples, showWarnings = F, recursive = T)

filename_sites <- paste0(dir_sites_chip, "/", "chipseq_", cell_type, "_", pwm_name, "_flank", flank, "_exp1.totalcount")

sites.df <- read.table(filename_sites, header = T, comment.char = "!", stringsAsFactors = F)
sites.df <- sites.df[, c("chr", "start", "end", "site", "pwmScore", "strand", "pValue", "mapability", "ChIP_mean")]
colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value", "mapability", "ChIP")

idx_select <- select_sites(sites.df, thresh_mapability, thresh_PWMscore)

Select candidate sites 
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10

sites.df <- sites.df[idx_select, ]
cat("Number of sites:", nrow(sites.df), "\n")

Number of sites: 54859

counts_combined.m <- load_combine_counts(tf_name, pwm_name, dir_count_matrix)

Loading count matrices ...

counts_combined.m <- counts_combined.m[idx_select,]

## Clip extreme values
counts_combined.m[counts_combined.m > max_cuts] <- max_cuts

cat("Dimension of", dim(counts_combined.m), "\n")

Dimension of 54859 436

if(nrow(counts_combined.m) != nrow(sites.df)){
  stop("Sites not matched!")
}

plot footprint profiles of highest occupancy

order_selected <- order(sites.df$ChIP, 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-4-1.png:

Version	Author	Date
35c5feb	kevinlkx	2018-08-29

## save counts matrix
saveRDS(counts_selected.m, paste0(dir_matrix_examples, "/", pwm_name, "_", cell_type, "_dnase_counts_selected_sites.rds"))

plot footprint 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-5-1.png:

Version	Author	Date
35c5feb	kevinlkx	2018-08-29

CTCF in K562 cell type

load DNase footprint data

cell_type <- "K562"
tf_name <- "CTCF"
pwm_name <- "CTCF_MA0139.1_1e-5"

dir_count_matrix <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_tagcount_matrix/")
dir_sites_chip <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/ChIPSeq/")
dir_matrix_examples <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_count_matrix_examples/")
dir.create(dir_matrix_examples, showWarnings = F, recursive = T)

filename_sites <- paste0(dir_sites_chip, "/", "chipseq_", cell_type, "_", pwm_name, "_flank", flank, "_exp1.totalcount")

sites.df <- read.table(filename_sites, header = T, comment.char = "!", stringsAsFactors = F)
sites.df <- sites.df[, c("chr", "start", "end", "site", "pwmScore", "strand", "pValue", "mapability", "ChIP_mean")]
colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value", "mapability", "ChIP")

idx_select <- select_sites(sites.df, thresh_mapability, thresh_PWMscore)

Select candidate sites 
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10

sites.df <- sites.df[idx_select, ]
cat("Number of sites:", nrow(sites.df), "\n")

Number of sites: 54859

counts_combined.m <- load_combine_counts(tf_name, pwm_name, dir_count_matrix)

Loading count matrices ...

counts_combined.m <- counts_combined.m[idx_select,]

## Clip extreme values
counts_combined.m[counts_combined.m > max_cuts] <- max_cuts

cat("Dimension of", dim(counts_combined.m), "\n")

Dimension of 54859 436

if(nrow(counts_combined.m) != nrow(sites.df)){
  stop("Sites not matched!")
}

plot footprint profiles of highest occupancy sites

order_selected <- order(sites.df$ChIP, 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-7-1.png:

Version	Author	Date
35c5feb	kevinlkx	2018-08-29

## save counts matrix
saveRDS(counts_selected.m, paste0(dir_matrix_examples, "/", pwm_name, "_", cell_type, "_dnase_counts_selected_sites.rds"))

REST in GM12878 cell type

load DNase footprint data

cell_type <- "GM12878"
tf_name <- "REST"
pwm_name <- "REST_MA0138.2_1e-5"

dir_count_matrix <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_tagcount_matrix/")
dir_sites_chip <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/ChIPSeq/")

filename_sites <- paste0(dir_sites_chip, "/", "chipseq_", cell_type, "_", pwm_name, "_flank", flank, "_exp1.totalcount")

sites.df <- read.table(filename_sites, header = T, comment.char = "!", stringsAsFactors = F)
sites.df <- sites.df[, c("chr", "start", "end", "site", "pwmScore", "strand", "pValue", "mapability", "ChIP_mean")]
colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value", "mapability", "ChIP")

idx_select <- select_sites(sites.df, thresh_mapability, thresh_PWMscore)

Select candidate sites 
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10

sites.df <- sites.df[idx_select, ]
cat("Number of sites:", nrow(sites.df), "\n")

Number of sites: 54533

counts_combined.m <- load_combine_counts(tf_name, pwm_name, dir_count_matrix)

Loading count matrices ...

counts_combined.m <- counts_combined.m[idx_select,]

## Clip extreme values
counts_combined.m[counts_combined.m > max_cuts] <- max_cuts

cat("Dimension of", dim(counts_combined.m), "\n")

Dimension of 54533 440

if(nrow(counts_combined.m) != nrow(sites.df)){
  stop("Sites not matched!")
}

plot footprint profiles of highest occupancy sites

order_selected <- order(sites.df$ChIP, 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-9-1.png:

Version	Author	Date
0e20593	kevinlkx	2018-08-29

## save counts matrix
saveRDS(counts_selected.m, paste0(dir_matrix_examples, "/", pwm_name, "_", cell_type, "_dnase_counts_selected_sites.rds"))

plot footprint 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-10-1.png:

Version	Author	Date
0e20593	kevinlkx	2018-08-29
35c5feb	kevinlkx	2018-08-29

REST in K562 cell type

load DNase footprint data

cell_type <- "K562"
tf_name <- "REST"
pwm_name <- "REST_MA0138.2_1e-5"


dir_count_matrix <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/DNaseSeq/DNase_tagcount_matrix/")
dir_sites_chip <- paste0(dir_data, "/DNase-seq_ENCODE/", cell_type, "/ChIPSeq/")

filename_sites <- paste0(dir_sites_chip, "/", "chipseq_", cell_type, "_", pwm_name, "_flank", flank, "_exp1.totalcount")

sites.df <- read.table(filename_sites, header = T, comment.char = "!", stringsAsFactors = F)
sites.df <- sites.df[, c("chr", "start", "end", "site", "pwmScore", "strand", "pValue", "mapability", "ChIP_mean")]
colnames(sites.df) = c("chr", "start", "end", "name", "pwm_score", "strand", "p_value", "mapability", "ChIP")

idx_select <- select_sites(sites.df, thresh_mapability, thresh_PWMscore)

Select candidate sites 
Select candidate sites with mapability >= 0.8 
Select candidate sites with PWM score >= 10

sites.df <- sites.df[idx_select, ]
cat("Number of sites:", nrow(sites.df), "\n")

Number of sites: 54533

counts_combined.m <- load_combine_counts(tf_name, pwm_name, dir_count_matrix)

Loading count matrices ...

counts_combined.m <- counts_combined.m[idx_select,]

## Clip extreme values
counts_combined.m[counts_combined.m > max_cuts] <- max_cuts

cat("Dimension of", dim(counts_combined.m), "\n")

Dimension of 54533 440

if(nrow(counts_combined.m) != nrow(sites.df)){
  stop("Sites not matched!")
}

plot footprint profiles of highest occupancy sites

order_selected <- order(sites.df$ChIP, 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, cell_type, "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, cell_type, "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)

Expand here to see past versions of unnamed-chunk-12-1.png:

Version	Author	Date
0e20593	kevinlkx	2018-08-29

## save counts matrix
saveRDS(counts_selected.m, paste0(dir_matrix_examples, "/", pwm_name, "_", cell_type, "_dnase_counts_selected_sites.rds"))

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

This reproducible R Markdown analysis was created with workflowr 1.1.1

DNase footprint profiles using ENCODE DNase-seq data

Kaixuan Luo

7/31/2018

functions

parameters

CTCF in GM12878 cell type

load DNase footprint data

plot footprint profiles of highest occupancy

plot footprint profiles of most accessible sites

CTCF in K562 cell type

load DNase footprint data

plot footprint profiles of highest occupancy sites

REST in GM12878 cell type

load DNase footprint data

plot footprint profiles of highest occupancy sites

plot footprint profiles of most accessible sites

REST in K562 cell type

load DNase footprint data

plot footprint profiles of highest occupancy sites

Session information