Contents

1 Setup and data

source("../utils/utils.R")
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   ChIPseeker v1.34.1  For help: https://guangchuangyu.github.io/software/ChIPseeker
   
   If you use ChIPseeker in published research, please cite:
   Qianwen Wang, Ming Li, Tianzhi Wu, Li Zhan, Lin Li, Meijun Chen, Wenqin Xie, Zijing Xie, Erqiang Hu, Shuangbin Xu, Guangchuang Yu. Exploring epigenomic datasets by ChIPseeker. Current Protocols 2022, 2(10): e585
   
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   Welcome to Bioconductor
   
       Vignettes contain introductory material; view with
       'browseVignettes()'. To cite Bioconductor, see
       'citation("Biobase")', and for packages 'citation("pkgname")'.
   
   
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config = load_config()

# load CHT results
cht_full = lapply(ab_tp_list, function(ab_tp) load_cht_results(ab_tp, remove_chr = F)) %>% bind_rows()
cht = cht_full %>% filter(!TEST.SNP.CHROM %in% c("chrX", "chrY", "chrM"))
cht_sign = cht %>% filter(signif_strongAI) 

# genes and promoters
genes = load_genes()
promoters = resize(genes, width = 1000, fix = "start")

# combined motif set (all TFs, peaks + alleles)
fimo = get_full_motif_sets(cht, ab_tp_list)
# only alleles
fimo_alleles  = lapply(ab_tp_list, function(ab_tp) parse_motifs_in_two_alleles(ab_tp, cht)) %>% bind_rows() 

2 Figure S2A. Correlation between biiological replicates over consensus peaks

cons_path = "/g/furlong/project/68_F1_cisreg_ichip/analysis/ChIPseq/Peaks/with_indels/merged_input/Consensus_peaksets"

ab_tp_list = c("twi/24", "mef2/68", "mef2/1012", "bin/68", "bin/1012", "ctcf/68")

cons_mult = lapply(ab_tp_list, function(ab_tp) {path = file.path(cons_path, ab_tp, "consensus_peaks_idr0.01_ind3_read_counts.txt");
                                                print(ab_tp);
                                                df = read.delim2(path, sep = "\t");
                                                df$ab_tp = ab_tp;
                                                df}) %>% bind_rows()
   [1] "twi/24"
   [1] "mef2/68"
   [1] "mef2/1012"
   [1] "bin/68"
   [1] "bin/1012"
   [1] "ctcf/68"
c(grep("_1.raw", colnames(cons_mult)),
  grep("_2.raw", colnames(cons_mult)))
    [1] 48 50 52 54 56 58 60 62 64 66 49 51 53 55 57 59 61 63 65 67
p = ggplot(cons_mult, aes(x = log(vgn_399_1.raw+1), y = log(vgn_399_2.raw+1))) + 
         geom_point(size = 0.1) + 
         facet_wrap(~ab_tp, ncol = 3, scales = "free") +
         geom_smooth(method = "lm") +
         theme_bw()

print(p)
   `geom_smooth()` using formula = 'y ~ x'

ggsave(file.path(outdir_fig_suppl, "FigS2A_correlation_between_replicates.pdf"), p, width = 9, height = 6)
   `geom_smooth()` using formula = 'y ~ x'

3 Figure S2B: Share of peaks in DHS

# paths to consensus peak sets for all TFs
ff = lapply(ab_tp_list, function(ab_tp) get_path_consensus_peakset(ab_tp))

# read peak sets
peaks = lapply(ff, function(f) load_consensus_peak_set(f)) %>% GRangesList()
names(peaks) = ab_tp_labels

# load DHS table
dhs = load_dhs() %>% GRanges()

# overlap with DHS
share_dhs = lapply(peaks, function(f) {ov = findOverlaps(f, dhs)
                           length(unique(queryHits(ov))) / length(f)}) %>% unlist()


df = data.frame(condition = names(share_dhs), share_dhs = share_dhs)
df$condition = factor(df$condition, levels = rev(df$condition))

# Share peaks in DHS

# 0.87-0.96% of peaks in DHS per TF
p = ggplot(df, aes(x = condition, y = share_dhs)) + 
  geom_bar(stat = "identity", width = 0.5, aes(fill = condition)) + 
  scale_y_continuous(breaks = seq(0, 1, by = 0.1)) +
  scale_fill_manual(name = "", values = rev(TFcols)) +
  coord_flip() +
  xlab("") +
  ylab("Share of peaks in DHS") +
  theme_bw() +
  theme(axis.text.y = element_text(size=19), axis.text.x = element_text(size=17), 
        axis.title.x = element_text(size=18), axis.title.y = element_text(size=18),
        legend.text=element_text(size=16), legend.position = "none")

p

ggsave(file.path(outdir_fig_suppl, "FigS2B_peaks_in_dhs.pdf"), p, width = 9, height = 6)

4 Figure S2C - distal vs proximal DHS

# add number of TFs per regiion
ff = lapply(ab_tp_list, function(ab_tp) get_path_consensus_peakset(ab_tp))

peaks = lapply(ff, function(f) load_consensus_peak_set(f, filter_chr = F)) %>% GRangesList()
names(peaks) = ab_tp_labels

combined_peakset = construct_combined_consensus_set(peaks)

peaks_stats = combined_peakset
peaks_stats$nTF = rowSums(combined_peakset[ , 6:ncol(combined_peakset)])

peaks_stats_gr = GRanges(peaks_stats)


ov = findOverlaps(peaks_stats_gr, promoters)

peaks_stats["TSS_proximal"] = 0
peaks_stats$TSS_proximal[queryHits(ov)] = 1

sum_df = peaks_stats %>% group_by(nTF) %>% mutate(N = n()) %>%
  group_by(nTF, N, TSS_proximal) %>% summarize(n = n()) %>% ungroup() %>%
  mutate(share = n / N)
   `summarise()` has grouped output by 'nTF', 'N'. You can override using the
   `.groups` argument.
p = ggplot(sum_df, aes(x = factor(nTF), y = share, fill = factor(TSS_proximal))) + 
           theme_bw() + 
           geom_bar(stat = "identity", width = 0.5) +
           scale_fill_manual(labels = c("TSS-distal", "TSS-proximal"), values = cbPalette, name = "") +
           xlab("# of TFs / time-points with the peak") +
           theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), 
                axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
                legend.text=element_text(size=14))

p

ggsave(file.path(outdir_fig_suppl, "FigS2C_TSS_dist_prox.pdf"), p, width = 8, height = 6)

5 Figure S2D - DHS accessibility across conditions

ov = findOverlaps(peaks_stats_gr, dhs)

peaks_stats["dhs_status"] = "no_dhs"
peaks_stats$dhs_status[queryHits(ov)] = dhs$ubiq_dhs[subjectHits(ov)]

sum_df = peaks_stats %>% filter(dhs_status!= "no_dhs" & !TSS_proximal) %>% 
  group_by(nTF) %>%
  mutate(N = n()) %>%
  group_by(nTF, N, dhs_status) %>% summarize(n = n()) %>%
  ungroup() %>%
  mutate(share = n / N)
   `summarise()` has grouped output by 'nTF', 'N'. You can override using the
   `.groups` argument.
p = ggplot(sum_df, aes(x = factor(nTF), y = share, fill = factor(dhs_status))) + 
           theme_bw() + 
           geom_bar(stat = "identity", width = 0.5) +
           scale_fill_manual(labels = c("False", "True"), values = cbPalette, name = "Ubiquitous DHS") +
           xlab("# of TFs / time-points with the peak \n(TSS-distal only)") +
           theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), 
                axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
                legend.text=element_text(size=14), legend.title=element_text(size=14))

p

ggsave(file.path(outdir_fig_suppl, "FigS2D_TSS_dist_ubiq_dhs.pdf"), p, width = 8, height = 6)
---
title: "Figure_S2"
output:
   BiocStyle::html_document:
      toc: true
      df_print: paged
      self_contained: true
      code_download: true
      highlight: tango
#bibliography: knn_ml_intro.bib
editor_options: 
  chunk_output_type: inline
---

```{r style, echo=FALSE, results="asis"}
library("knitr")
options(digits = 2, width = 80)
options(bitmapType = 'cairo')
golden_ratio <- (1 + sqrt(5)) / 2
opts_chunk$set(echo = TRUE, tidy = FALSE, include = TRUE, cache = FALSE,
               dev=c('png', 'pdf'), comment = '  ', dpi = 300)

options(stringsAsFactors = FALSE)
knitr::opts_chunk$set(cache=FALSE)
options(digits = 5)         
```

# Setup and data

```{r}
source("../utils/utils.R")
config = load_config()

# load CHT results
cht_full = lapply(ab_tp_list, function(ab_tp) load_cht_results(ab_tp, remove_chr = F)) %>% bind_rows()
cht = cht_full %>% filter(!TEST.SNP.CHROM %in% c("chrX", "chrY", "chrM"))
cht_sign = cht %>% filter(signif_strongAI) 

# genes and promoters
genes = load_genes()
promoters = resize(genes, width = 1000, fix = "start")

# combined motif set (all TFs, peaks + alleles)
fimo = get_full_motif_sets(cht, ab_tp_list)
# only alleles
fimo_alleles  = lapply(ab_tp_list, function(ab_tp) parse_motifs_in_two_alleles(ab_tp, cht)) %>% bind_rows() 

```


# Figure S2A. Correlation between biiological replicates over consensus peaks

```{r}
cons_path = "/g/furlong/project/68_F1_cisreg_ichip/analysis/ChIPseq/Peaks/with_indels/merged_input/Consensus_peaksets"

ab_tp_list = c("twi/24", "mef2/68", "mef2/1012", "bin/68", "bin/1012", "ctcf/68")

cons_mult = lapply(ab_tp_list, function(ab_tp) {path = file.path(cons_path, ab_tp, "consensus_peaks_idr0.01_ind3_read_counts.txt");
                                                print(ab_tp);
                                                df = read.delim2(path, sep = "\t");
                                                df$ab_tp = ab_tp;
                                                df}) %>% bind_rows()


c(grep("_1.raw", colnames(cons_mult)),
  grep("_2.raw", colnames(cons_mult)))

p = ggplot(cons_mult, aes(x = log(vgn_399_1.raw+1), y = log(vgn_399_2.raw+1))) + 
         geom_point(size = 0.1) + 
         facet_wrap(~ab_tp, ncol = 3, scales = "free") +
         geom_smooth(method = "lm") +
         theme_bw()

print(p)
ggsave(file.path(outdir_fig_suppl, "FigS2A_correlation_between_replicates.pdf"), p, width = 9, height = 6)

```

# Figure S2B: Share of peaks in DHS 
```{r}

# paths to consensus peak sets for all TFs
ff = lapply(ab_tp_list, function(ab_tp) get_path_consensus_peakset(ab_tp))

# read peak sets
peaks = lapply(ff, function(f) load_consensus_peak_set(f)) %>% GRangesList()
names(peaks) = ab_tp_labels

# load DHS table
dhs = load_dhs() %>% GRanges()

# overlap with DHS
share_dhs = lapply(peaks, function(f) {ov = findOverlaps(f, dhs)
                           length(unique(queryHits(ov))) / length(f)}) %>% unlist()


df = data.frame(condition = names(share_dhs), share_dhs = share_dhs)
df$condition = factor(df$condition, levels = rev(df$condition))

# Share peaks in DHS

# 0.87-0.96% of peaks in DHS per TF
p = ggplot(df, aes(x = condition, y = share_dhs)) + 
  geom_bar(stat = "identity", width = 0.5, aes(fill = condition)) + 
  scale_y_continuous(breaks = seq(0, 1, by = 0.1)) +
  scale_fill_manual(name = "", values = rev(TFcols)) +
  coord_flip() +
  xlab("") +
  ylab("Share of peaks in DHS") +
  theme_bw() +
  theme(axis.text.y = element_text(size=19), axis.text.x = element_text(size=17), 
        axis.title.x = element_text(size=18), axis.title.y = element_text(size=18),
        legend.text=element_text(size=16), legend.position = "none")

p
ggsave(file.path(outdir_fig_suppl, "FigS2B_peaks_in_dhs.pdf"), p, width = 9, height = 6)
```



# Figure S2C - distal vs proximal DHS


```{r}

# add number of TFs per regiion
ff = lapply(ab_tp_list, function(ab_tp) get_path_consensus_peakset(ab_tp))

peaks = lapply(ff, function(f) load_consensus_peak_set(f, filter_chr = F)) %>% GRangesList()
names(peaks) = ab_tp_labels

combined_peakset = construct_combined_consensus_set(peaks)

peaks_stats = combined_peakset
peaks_stats$nTF = rowSums(combined_peakset[ , 6:ncol(combined_peakset)])

peaks_stats_gr = GRanges(peaks_stats)


ov = findOverlaps(peaks_stats_gr, promoters)

peaks_stats["TSS_proximal"] = 0
peaks_stats$TSS_proximal[queryHits(ov)] = 1

sum_df = peaks_stats %>% group_by(nTF) %>% mutate(N = n()) %>%
  group_by(nTF, N, TSS_proximal) %>% summarize(n = n()) %>% ungroup() %>%
  mutate(share = n / N)

p = ggplot(sum_df, aes(x = factor(nTF), y = share, fill = factor(TSS_proximal))) + 
           theme_bw() + 
           geom_bar(stat = "identity", width = 0.5) +
           scale_fill_manual(labels = c("TSS-distal", "TSS-proximal"), values = cbPalette, name = "") +
           xlab("# of TFs / time-points with the peak") +
           theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), 
                axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
                legend.text=element_text(size=14))

p
ggsave(file.path(outdir_fig_suppl, "FigS2C_TSS_dist_prox.pdf"), p, width = 8, height = 6)

```




# Figure S2D - DHS accessibility across conditions



```{r}

ov = findOverlaps(peaks_stats_gr, dhs)

peaks_stats["dhs_status"] = "no_dhs"
peaks_stats$dhs_status[queryHits(ov)] = dhs$ubiq_dhs[subjectHits(ov)]

sum_df = peaks_stats %>% filter(dhs_status!= "no_dhs" & !TSS_proximal) %>% 
  group_by(nTF) %>%
  mutate(N = n()) %>%
  group_by(nTF, N, dhs_status) %>% summarize(n = n()) %>%
  ungroup() %>%
  mutate(share = n / N)

p = ggplot(sum_df, aes(x = factor(nTF), y = share, fill = factor(dhs_status))) + 
           theme_bw() + 
           geom_bar(stat = "identity", width = 0.5) +
           scale_fill_manual(labels = c("False", "True"), values = cbPalette, name = "Ubiquitous DHS") +
           xlab("# of TFs / time-points with the peak \n(TSS-distal only)") +
           theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), 
                axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
                legend.text=element_text(size=14), legend.title=element_text(size=14))

p

ggsave(file.path(outdir_fig_suppl, "FigS2D_TSS_dist_ubiq_dhs.pdf"), p, width = 8, height = 6)

```

