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library(tidyverse)
library(data.table)
library(ggplot2)
source("code/plots.R")

Here, we explore the DNase-seq and ATAC-seq accessibility profiles round TF motif matches.

CTCF

CTCF profiles in GM12878

We scanned CTCF motif matches along the genome, then extended 100bp window on both sides of motif matches.

We downloaded DNase-seq, ATAC-seq and CTCF ChIP-seq data in GM12878 from ENCODE.

About the processed files:

  • CTCF_MA0139.2_1e-5.candidate.sites.rds: a data frame of candidate binding sites matching CTCF motif (MA0139.2) (result from FIMO, p-value < 1e-5).

  • CTCF.GM12878.sites.chip.labels.rds: a data frame with the CTCF motif matches (result from FIMO, as in *.candidate.sites.rds), as well as normalized ChIP-seq counts (“chip” column) and ChIP-seq peak labels (“chip_label”) in GM12878.

  • CTCF.GM12878.DNase.counts.mat.rds: DNase-seq count matrix, where the rows are the motif matches (in the same order as *.candidate.sites.rds), and the columns are the DNase-seq counts at each position in the window. The first half of the columns are counts on the forward strand, and the second half are counts on the reverse strand. We could simple combine the counts on both strands as shown below. After combining the strands, we will have counts for the 100 bp on the left flanking window, counts in motif region, and 100bp on the right flanking window.

  • CTCF.GM12878.ATAC.counts.mat.rds: ATAC-seq count matrix, similar to *.DNase.counts.mat.rds.

sites <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF_MA0139.2_1e-5.candidate.sites.rds')
sites_chip_labels <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.GM12878.sites.chip.labels.rds')

dnase_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.GM12878.DNase.counts.mat.rds')
# combine counts on both strands
dnase_count_matrix <- dnase_count_matrix[,1:(ncol(dnase_count_matrix)/2)] + dnase_count_matrix[,(ncol(dnase_count_matrix)/2+1):ncol(dnase_count_matrix)]

atac_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.GM12878.ATAC.counts.mat.rds')
# combine counts on both strands
atac_count_matrix <- atac_count_matrix[,1:(ncol(atac_count_matrix)/2)] + atac_count_matrix[,(ncol(atac_count_matrix)/2+1):ncol(atac_count_matrix)]

Plot aggregate profiles of DNase-seq and ATAC-seq counts around motif sites.

pos_idx <- which(sites_chip_labels$chip_label == 1)
neg_idx <- which(sites_chip_labels$chip_label == 0)

dnase_pos_profile <- colMeans(dnase_count_matrix[pos_idx, ], na.rm = TRUE)
dnase_neg_profile <- colMeans(dnase_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(dnase_pos_profile, dnase_neg_profile, 
                      title = "Aggregate DNase-seq profiles around CTCF motifs in GM12878")

Version Author Date
2c0e42e kevinlkx 2025-06-03 
atac_pos_profile <- colMeans(atac_count_matrix[pos_idx, ], na.rm = TRUE)
atac_neg_profile <- colMeans(atac_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(atac_pos_profile, atac_neg_profile, 
                      title = "Aggregate ATAC-seq profiles around CTCF motifs in GM12878")

Version Author Date
2c0e42e kevinlkx 2025-06-03

Heatmap of 2000 positive sites and 2000 negative sites

We randomly sample 2000 positive sites and 2000 negative sites, and plot the PWM scores, DNase-seq, ATAC-seq and ChIP-seq results.

set.seed(1)
sites_idx <- c(sample(pos_idx, 2000), sample(neg_idx, 2000))

pwm = sites_chip_labels$pwm.score[sites_idx]
chip = sites_chip_labels$chip[sites_idx]
chip_label = sites_chip_labels$chip_label[sites_idx]
dnase_data = dnase_count_matrix[sites_idx,]
atac_data = atac_count_matrix[sites_idx,]

rank = order(chip_label, chip)

data.l <- list(DNaase = dnase_data,
               ATAC = atac_data)
chip.df <- data.frame(chip = chip, chip_label = chip_label)

plot_data_matrix_heatmap(pwm, data.l, chip.df, rank, 
                         data_name = c("DNase-seq", "ATAC-seq"), 
                         chip_name = c("ChIP-seq counts", "ChIP-seq peaks"), 
                         title = "CTCF sites in GM12878",
                         zMax_data = c(1, 3), 
                         zMax_chip = c(200, 1))

Version Author Date
2c0e42e kevinlkx 2025-06-03

CTCF profiles in K562

We scanned CTCF motif matches along the genome, then extended 100bp window on both sides of motif matches.

We downloaded DNase-seq, ATAC-seq and CTCF ChIP-seq data in K562 from ENCODE.

# sites <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF_MA0139.2_1e-5.candidate.sites.rds')

sites_chip_labels <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.K562.sites.chip.labels.rds')

dnase_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.K562.DNase.counts.mat.rds')
# combine counts on both strands
dnase_count_matrix <- dnase_count_matrix[,1:(ncol(dnase_count_matrix)/2)] + dnase_count_matrix[,(ncol(dnase_count_matrix)/2+1):ncol(dnase_count_matrix)]

atac_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/CTCF.K562.ATAC.counts.mat.rds')
# combine counts on both strands
atac_count_matrix <- atac_count_matrix[,1:(ncol(atac_count_matrix)/2)] + atac_count_matrix[,(ncol(atac_count_matrix)/2+1):ncol(atac_count_matrix)]

Plot aggregate profiles of DNase-seq and ATAC-seq counts around motif sites.

pos_idx <- which(sites_chip_labels$chip_label == 1)
neg_idx <- which(sites_chip_labels$chip_label == 0)

dnase_pos_profile <- colMeans(dnase_count_matrix[pos_idx, ], na.rm = TRUE)
dnase_neg_profile <- colMeans(dnase_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(dnase_pos_profile, dnase_neg_profile, 
                      title = "Aggregate DNase-seq profiles around CTCF motifs in K562")

Version Author Date
2c0e42e kevinlkx 2025-06-03 
atac_pos_profile <- colMeans(atac_count_matrix[pos_idx, ], na.rm = TRUE)
atac_neg_profile <- colMeans(atac_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(atac_pos_profile, atac_neg_profile, 
                      title = "Aggregate ATAC-seq profiles around CTCF motifs in K562")

Version Author Date
2c0e42e kevinlkx 2025-06-03

Heatmap of 2000 positive sites and 2000 negative sites

We randomly sample 2000 positive sites and 2000 negative sites, and plot the PWM scores, DNase-seq, ATAC-seq and ChIP-seq results.

set.seed(1)
sites_idx <- c(sample(pos_idx, 2000), sample(neg_idx, 2000))

pwm = sites_chip_labels$pwm.score[sites_idx]
chip = sites_chip_labels$chip[sites_idx]
chip_label = sites_chip_labels$chip_label[sites_idx]
dnase_data = dnase_count_matrix[sites_idx,]
atac_data = atac_count_matrix[sites_idx,]

rank = order(chip_label, chip)

data.l <- list(DNaase = dnase_data,
               ATAC = atac_data)
chip.df <- data.frame(chip = chip, chip_label = chip_label)

plot_data_matrix_heatmap(pwm, data.l, chip.df, rank, 
                         data_name = c("DNase-seq", "ATAC-seq"), 
                         chip_name = c("ChIP-seq counts", "ChIP-seq peaks"), 
                         title = "CTCF sites in K562",
                         zMax_data = c(1, 3), 
                         zMax_chip = c(200, 1))

Version Author Date
2c0e42e kevinlkx 2025-06-03

REST

REST profiles in GM12878

We scanned REST motif matches along the genome, then extended 100bp window on both sides of motif matches.

We downloaded DNase-seq, ATAC-seq and REST ChIP-seq data in GM12878 from ENCODE.

# sites <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST_MA0138.3_1e-5.candidate.sites.rds')

sites_chip_labels <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.GM12878.sites.chip.labels.rds')

dnase_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.GM12878.DNase.counts.mat.rds')
# combine counts on both strands
dnase_count_matrix <- dnase_count_matrix[,1:(ncol(dnase_count_matrix)/2)] + dnase_count_matrix[,(ncol(dnase_count_matrix)/2+1):ncol(dnase_count_matrix)]

atac_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.GM12878.ATAC.counts.mat.rds')
# combine counts on both strands
atac_count_matrix <- atac_count_matrix[,1:(ncol(atac_count_matrix)/2)] + atac_count_matrix[,(ncol(atac_count_matrix)/2+1):ncol(atac_count_matrix)]

Plot aggregate profiles of DNase-seq and ATAC-seq counts around motif sites.

pos_idx <- which(sites_chip_labels$chip_label == 1)
neg_idx <- which(sites_chip_labels$chip_label == 0)

dnase_pos_profile <- colMeans(dnase_count_matrix[pos_idx, ], na.rm = TRUE)
dnase_neg_profile <- colMeans(dnase_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(dnase_pos_profile, dnase_neg_profile, 
                      title = "Aggregate DNase-seq profiles around REST motifs in GM12878")

Version Author Date
2c0e42e kevinlkx 2025-06-03 
atac_pos_profile <- colMeans(atac_count_matrix[pos_idx, ], na.rm = TRUE)
atac_neg_profile <- colMeans(atac_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(atac_pos_profile, atac_neg_profile, 
                      title = "Aggregate ATAC-seq profiles around REST motifs in GM12878")

Version Author Date
2c0e42e kevinlkx 2025-06-03

Heatmap of 2000 positive sites and 2000 negative sites

We randomly sample 2000 positive sites and 2000 negative sites, and plot the PWM scores, DNase-seq, ATAC-seq and ChIP-seq results.

set.seed(1)
sites_idx <- c(sample(pos_idx, 2000), sample(neg_idx, 2000))

pwm = sites_chip_labels$pwm.score[sites_idx]
chip = sites_chip_labels$chip[sites_idx]
chip_label = sites_chip_labels$chip_label[sites_idx]
dnase_data = dnase_count_matrix[sites_idx,]
atac_data = atac_count_matrix[sites_idx,]

rank = order(chip_label, chip)

data.l <- list(DNaase = dnase_data,
               ATAC = atac_data)
chip.df <- data.frame(chip = chip, chip_label = chip_label)

plot_data_matrix_heatmap(pwm, data.l, chip.df, rank, 
                         data_name = c("DNase-seq", "ATAC-seq"), 
                         chip_name = c("ChIP-seq counts", "ChIP-seq peaks"), 
                         title = "REST sites in GM12878",
                         zMax_data = c(1, 3), 
                         zMax_chip = c(200, 1))

Version Author Date
2c0e42e kevinlkx 2025-06-03

REST profiles in K562

We scanned REST motif matches along the genome, then extended 100bp window on both sides of motif matches.

We downloaded DNase-seq, ATAC-seq and REST ChIP-seq data in K562 from ENCODE.

# sites <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST_MA0138.3_1e-5.candidate.sites.rds')

sites_chip_labels <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.K562.sites.chip.labels.rds')

dnase_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.K562.DNase.counts.mat.rds')
# combine counts on both strands
dnase_count_matrix <- dnase_count_matrix[,1:(ncol(dnase_count_matrix)/2)] + dnase_count_matrix[,(ncol(dnase_count_matrix)/2+1):ncol(dnase_count_matrix)]

atac_count_matrix <- readRDS('/project2/xinhe/kevinluo/footprint_clustering/processed_data/hg38/REST.K562.ATAC.counts.mat.rds')
# combine counts on both strands
atac_count_matrix <- atac_count_matrix[,1:(ncol(atac_count_matrix)/2)] + atac_count_matrix[,(ncol(atac_count_matrix)/2+1):ncol(atac_count_matrix)]

Plot aggregate profiles of DNase-seq and ATAC-seq counts around motif sites.

pos_idx <- which(sites_chip_labels$chip_label == 1)
neg_idx <- which(sites_chip_labels$chip_label == 0)

dnase_pos_profile <- colMeans(dnase_count_matrix[pos_idx, ], na.rm = TRUE)
dnase_neg_profile <- colMeans(dnase_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(dnase_pos_profile, dnase_neg_profile, 
                      title = "Aggregate DNase-seq profiles around REST motifs in K562")

Version Author Date
2c0e42e kevinlkx 2025-06-03 
atac_pos_profile <- colMeans(atac_count_matrix[pos_idx, ], na.rm = TRUE)
atac_neg_profile <- colMeans(atac_count_matrix[neg_idx, ], na.rm = TRUE)
plot_pos_neg_profiles(atac_pos_profile, atac_neg_profile, 
                      title = "Aggregate ATAC-seq profiles around REST motifs in K562")

Version Author Date
2c0e42e kevinlkx 2025-06-03

Heatmap of 2000 positive sites and 2000 negative sites

We randomly sample 2000 positive sites and 2000 negative sites, and plot the PWM scores, DNase-seq, ATAC-seq and ChIP-seq results.

set.seed(1)
sites_idx <- c(sample(pos_idx, 2000), sample(neg_idx, 2000))

pwm = sites_chip_labels$pwm.score[sites_idx]
chip = sites_chip_labels$chip[sites_idx]
chip_label = sites_chip_labels$chip_label[sites_idx]
dnase_data = dnase_count_matrix[sites_idx,]
atac_data = atac_count_matrix[sites_idx,]

rank = order(chip_label, chip)

data.l <- list(DNaase = dnase_data,
               ATAC = atac_data)
chip.df <- data.frame(chip = chip, chip_label = chip_label)

plot_data_matrix_heatmap(pwm, data.l, chip.df, rank, 
                         data_name = c("DNase-seq", "ATAC-seq"), 
                         chip_name = c("ChIP-seq counts", "ChIP-seq peaks"), 
                         title = "REST sites in K562",
                         zMax_data = c(1, 3), 
                         zMax_chip = c(200, 1))

Version Author Date
2c0e42e kevinlkx 2025-06-03 

sessionInfo()
#> R version 4.2.0 (2022-04-22)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#> 
#> Matrix products: default
#> BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
#>  [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
#>  [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
#> [10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] data.table_1.14.2 forcats_0.5.1     stringr_1.5.1     dplyr_1.1.4      
#>  [5] purrr_1.0.2       readr_2.1.2       tidyr_1.3.1       tibble_3.2.1     
#>  [9] ggplot2_3.5.1     tidyverse_1.3.1   workflowr_1.7.0  
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1 xfun_0.30        bslib_0.3.1      haven_2.5.0     
#>  [5] colorspace_2.0-3 vctrs_0.6.5      generics_0.1.2   htmltools_0.5.2 
#>  [9] yaml_2.3.5       rlang_1.1.4      jquerylib_0.1.4  later_1.3.0     
#> [13] pillar_1.10.1    withr_2.5.0      glue_1.6.2       DBI_1.1.2       
#> [17] dbplyr_2.1.1     readxl_1.4.0     modelr_0.1.8     lifecycle_1.0.4 
#> [21] cellranger_1.1.0 munsell_0.5.0    gtable_0.3.0     rvest_1.0.2     
#> [25] evaluate_0.15    knitr_1.39       tzdb_0.3.0       callr_3.7.3     
#> [29] fastmap_1.1.0    httpuv_1.6.5     ps_1.7.0         highr_0.9       
#> [33] broom_0.8.0      Rcpp_1.0.12      promises_1.2.0.1 backports_1.4.1 
#> [37] scales_1.3.0     jsonlite_1.8.0   fs_1.5.2         hms_1.1.1       
#> [41] digest_0.6.29    stringi_1.7.6    processx_3.8.0   getPass_0.2-2   
#> [45] rprojroot_2.0.3  grid_4.2.0       cli_3.6.3        tools_4.2.0     
#> [49] magrittr_2.0.3   sass_0.4.1       crayon_1.5.1     whisker_0.4     
#> [53] pkgconfig_2.0.3  ellipsis_0.3.2   xml2_1.3.3       reprex_2.0.1    
#> [57] lubridate_1.8.0  assertthat_0.2.1 rmarkdown_2.14   httr_1.4.3      
#> [61] rstudioapi_0.13  R6_2.5.1         git2r_0.30.1     compiler_4.2.0