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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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 S4A
# get variants distance to motifs (excluding peaks without motifs)
res_df = lapply(ab_tp_list, function(ab_tp) get_variant_distance2TFmotif(ab_tp, cht, fimo, same_peak = T) %>%
mutate(condition = ab_tp)) %>% bind_rows()
dist_breaks = c(-1, 0, 20, 40, 60, 80, 100, 3000)
dist_labels = c("in motif", "1-20 bp", "21-40 bp", "41-60 bp", "61-80 bp", "81-100 bp", ">100 bp")
res_full = data.frame(matrix(ncol = 6, nrow = 0))
names(res_full) = c("dist_bin", "n", "share", "sd", "type", "condition")
background_sum_all_cond = data.frame(matrix(ncol = 7, nrow = 0))
names(background_sum_all_cond) = c("dist_bin", "n", "share", "share_full", "type", "condition", "i")
for(ab_tp in ab_tp_list) {
# all variants
df_sel = res_df %>% dplyr::filter(condition == ab_tp)
N_var = length(unique(df_sel$snp_id)) # number of variants in peaks with motifs
N_peak = length(unique(df_sel$peak_id)) # number of peaks with motifs
# significant variants
df_sign = df_sel %>% filter(signif_strongAI)
N_var_sign = length(unique(df_sign$snp_id)) # number of significant variants in peaks with motifs
N_peak_sign = length(unique(df_sign$peak_id)) # number of AI peaks with motifs
sign_sum = df_sign %>%
dplyr::group_by(peak_id) %>%
dplyr::mutate(min_dist = min(dist2motif)) %>%
dplyr::filter(dist2motif == min_dist) %>%
dplyr::select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
dplyr::mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
dplyr::group_by(dist_bin) %>%
dplyr::summarize(n = n(), share = n / mean(N_tot), sd = sd(n), type = "real", condition = ab_tp)
df_non_sign = df_sel %>% dplyr::group_by(peak_id) %>% dplyr::mutate(AI_peak = any(signif_strongAI)) %>% dplyr::filter(!AI_peak) %>% ungroup()
background_sum_all = lapply(1:10000, function(i) {
#print(i)
# 1. select same number of peaks as in AI peaks
peak_ids = sample(unique(df_non_sign$peak_id), N_peak_sign)
df_bg = df_non_sign %>% dplyr::filter(peak_id %in% peak_ids)
# 2. select same number of variants as for AI peaks
variant_ids = sample(unique(df_bg$snp_id), N_var_sign)
df_bg %<>% filter(snp_id %in% variant_ids)
N_peak_bg = length(unique(df_bg$peak_id))
bg_sum = df_bg %>%
dplyr::group_by(peak_id) %>%
dplyr::mutate(min_dist = min(dist2motif)) %>%
dplyr::filter(dist2motif == min_dist) %>%
dplyr::select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
dplyr::mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
dplyr::group_by(dist_bin) %>%
dplyr::summarize(n = n(), share = n / mean(N_tot), share_full = n / N_peak_bg) %>%
dplyr::mutate(condition = ab_tp, i = i)
bg_sum$type = "background"
bg_sum
}) %>% bind_rows()
background_sum_all_cond = rbind(background_sum_all_cond, background_sum_all)
background_sum = background_sum_all %>%
dplyr::group_by(dist_bin) %>%
dplyr::summarize(n = mean(n),
share = mean(share),
sd = sd(share_full),
type = "background",
condition = ab_tp)
res = rbind.data.frame(sign_sum, background_sum)
res_full = rbind.data.frame(res_full, res)
}
res_full$dist_bin = factor(res_full$dist_bin, levels = rev(dist_labels))
res_full$tf_labels = ab_tp_labels[res_full$condition]
res_full$tf_labels = factor(res_full$tf_labels, levels = ab_tp_labels)
shares_in_motif = res_full %>%
arrange(tf_labels) %>%
filter(dist_bin == "in motif", type == "real") %>%
select(share) %>% unlist() %>% unique() %>% round(2)
res_full$dist_bin = factor(res_full$dist_bin, levels = dist_labels)
res_full$type = factor(res_full$type, levels = unique(res_full$type))
p = ggplot(res_full, aes(x = dist_bin, y = share, color = type, group = type)) +
facet_wrap(~tf_labels, ncol = 3) +
geom_ribbon(data=subset(res_full, type=="background"), aes(x=dist_bin, y = share, ymin = share - sd * 2, ymax = share + sd * 2, group = type), fill = "grey70", colour=NA, alpha = .4) +
geom_point(size = 2) +
geom_line(linewidth = 1) +
scale_color_manual(values = c("grey", "darkblue"), name = "", labels = c("AI peaks with motifs", "background")) +
theme_bw() +
ylab("Share of peaks") +
xlab("Distance from TF motif") +
theme(axis.text.y = element_text(size=14), axis.text.x = element_text(size=14, angle = 45, hjust = 1),
axis.title.x = element_text(size=16), axis.title.y = element_text(size=16),
legend.text=element_text(size=16), legend.title=element_text(size=16))
p

outf = file.path(outdir_fig_suppl, paste0("FigS4A_dist2motif_vs_background.pdf"))
ggsave(outf, p, width = 12, height = 6)
Empirical p-values for the test: real share is larger than background share
In motif
1-20 bp
21-40 bp
41-60 bp
61-80 bp
81-100 bp
>100 bp
Figure S4B - Fisher’s tests: enrichments of significant variants in motiifs in / outside peaks
out_df = data.frame(matrix(ncol = 10, nrow = 0))
names(out_df) = c("condition", "motif", "N_sign", "N_ns", "N_sign_motif", "N_ns_motif", "N_motif", "group", "odd_ratio", "pvalue")
i = 1
for(ab_tp in ab_tp_list) {
tf = TFs[ab_tp]
print(ab_tp)
cht_sel = cht %>% filter(condition == ab_tp)
# all signif variants - all, in and outside peaks
N1 = cht_sel %>% filter(signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N1_a = cht_sel %>% filter(signif_strongAI & dist2summit < 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N1_b = cht_sel %>% filter(signif_strongAI & dist2summit >= 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
# all n.s. variants - in and outside peaks
N2 = cht_sel %>% filter(!signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N2_a = cht_sel %>% filter(!signif_strongAI & dist2summit < 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N2_b = cht_sel %>% filter(!signif_strongAI & dist2summit >= 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
# variants in the motifs of selected TFs
fimo_selected = parse_motifs_in_two_alleles(ab_tp, cht_sel, radius = 15, path2_base1 = "/all_variants_alleles/FIMO/",
path2_base2 = "combined_motifs/fimo.tsv",
peak_radius = 250, subset_motif = F, subset_cht = T)
# 1. Enrichment in cognate motifs
print("1. Enrichment in cognate motifs")
#fimo_sel = fimo_selected %>% filter(motif_alt_id == tf & snp_id %in% cht_sel$snp_id)
fimo_sel = fimo_selected %>% filter(motif_alt_id == tf) # equivalent
cognate_motif_ids = unique(fimo_sel$snp_id)
# number of significant variants in motifs - all, in and outside peaks
N3 = fimo_sel %>% filter(signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N3_a = fimo_sel %>% filter(signif_strongAI & in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N3_b = fimo_sel %>% filter(signif_strongAI & !in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
# number of n.s. variants in motifs - all, in and outside peaks
N4 = fimo_sel %>% filter(!signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N4_a = fimo_sel %>% filter(!signif_strongAI & in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
N4_b = fimo_sel %>% filter(!signif_strongAI & !in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
# mat = matrix(c(N3, N4, N1, N2), nrow = 2)
# ft = fisher.test(mat)
# out_df[i, ] = c(ab_tp, tf, N1, N2, N3, N4, N3 + N4, "all variants", ft$estimate, ft$p.value)
# i = i + 1
# print("1a. All variants")
# print(ft)
mat = matrix(c(N3_a, N4_a, N1_a, N2_a), nrow = 2)
ft = fisher.test(mat)
out_df[i, ] = c(ab_tp, tf, N1_a, N2_a, N3_a, N4_a, N3_a + N4_a, "in peaks", ft$estimate, ft$p.value)
i = i + 1
print("1b. Variants in peaks")
print(ft)
mat = matrix(c(N3_b, N4_b, N1_b, N2_b), nrow = 2)
ft = fisher.test(mat)
out_df[i, ] = c(ab_tp, tf, N1_b, N2_b, N3_b, N4_b, N3_b + N4_b, "outside peaks", ft$estimate, ft$p.value)
i = i + 1
print("1c. Variants outside peaks")
print(ft)
}
[1] "twi/24"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 2.4e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.7026 2.5660
sample estimates:
odds ratio
2.0982
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.0076
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.0463 1.3476
sample estimates:
odds ratio
1.1895
[1] "ctcf/68"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value <2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.3256 3.3831
sample estimates:
odds ratio
2.8113
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.92
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.87304 1.12213
sample estimates:
odds ratio
0.9914
[1] "mef2/68"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value <2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.2275 3.3489
sample estimates:
odds ratio
2.7412
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.78
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.86807 1.19931
sample estimates:
odds ratio
1.0234
[1] "mef2/1012"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value <2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.2876 3.3341
sample estimates:
odds ratio
2.7702
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.18
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.95347 1.25785
sample estimates:
odds ratio
1.0975
[1] "bin/68"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 4.7e-12
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.1427 3.6830
sample estimates:
odds ratio
2.8278
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.95
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.75705 1.24591
sample estimates:
odds ratio
0.97841
[1] "bin/1012"
[1] "1. Enrichment in cognate motifs"
[1] "1b. Variants in peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 2.1e-13
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.407 4.254
sample estimates:
odds ratio
3.2233
[1] "1c. Variants outside peaks"
Fisher's Exact Test for Count Data
data: mat
p-value = 0.044
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.99598 1.66680
sample estimates:
odds ratio
1.2986
out_df$label = rep(ab_tp_labels, each = 2)
out_df$label = factor(out_df$label, levels = ab_tp_labels)
out_df$pvalue = as.numeric(out_df$pvalue)
out_df$odd_ratio = as.numeric(out_df$odd_ratio)
p = ggplot(out_df, aes(x = label, y = odd_ratio)) +
facet_wrap(~group) +
geom_hline(yintercept = 1, color = "darkred") +
geom_bar(aes(fill = -log10(pvalue)), color = "darkblue", stat = "identity", position = "dodge", width = 0.5) +
#scale_fill_manual(name = "", values = c("darkblue", "darkgrey"), labels = c("AI peaks", "non-AI peaks")) +
#geom_text(aes(label = round(r1, 2), x = label, y = odds_ratio + 0.07), data = df, size = 6) +
ylab("Enrichment in cognate TF motifs \nFisher's Test Odds Ratio") +
theme_bw() +
theme(axis.text.x = element_text(size = 14, angle = 45, hjust = 1, colour = TFcols),
axis.text.y = element_text(size = 12),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 16),
legend.text = element_text(size=14),
legend.title = element_text(size=14))
Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

outf = file.path(outdir_fig_suppl, paste0("FigS4B_motifs_in_peaks_fisher.pdf"))
ggsave(outf, p, width = 10, height = 6)
Figure S4C - Motif prediction of AI
# Correlation between AI and delta_score
score_thres = 1
df_shared = fimo_alleles %>% filter(in_peak & !is.na(score.ref) & !is.na(score.alt)) %>%
mutate(delta_score = as.numeric(score.ref) - as.numeric(score.alt),
type = ifelse((AI > 0.5 & delta_score > 0) | (AI < 0.5 & delta_score < 0), "concordant", "discordant"))
df_shared %>%
group_by(condition, is_indel) %>%
summarize(min(abs(delta_score)), max(dist2summit), cor(delta_score, AI), share_concordant = sum(type == "concordant") / n(), n())
`summarise()` has grouped output by 'condition'. You can override using the
`.groups` argument.
df_sum = df_shared %>% filter(abs(delta_score) > score_thres & dist2summit < 250) %>%
summarize(min(abs(delta_score)), max(dist2summit), cor = cor(delta_score, AI), share_concordant = sum(type == "concordant") / n(), n())
cor = round(df_sum$cor, 2)
n_conc = round(df_sum$share_concordant, 2) * 100
n_disc = (1 - round(df_sum$share_concordant, 2)) * 100
p = ggplot(df_shared %>% filter(abs(delta_score) > score_thres ), aes(x = delta_score, y = AI, color = type)) +
geom_point(size = 1, color = cbPalette[2]) +
geom_smooth(method = "lm", se = F, color = "darkblue", size = 0.5) +
geom_vline(xintercept = -score_thres, color = "grey", size = 0.7) +
geom_vline(xintercept = score_thres, color = "grey", size = 0.7) +
geom_hline(yintercept = 0.5, color = "grey", size = 0.7) +
#geom_hline(yintercept = 0.4, color = "grey", size = 0.7) +
theme_bw() +
annotate(geom = "text", x = -3.5, y = 0.95, label = paste("R=", cor)) +
annotate(geom = "text", x = -3.5, y = 0.88, label = paste("% concordant: ", n_conc), size = 4) +
xlab("Motif score change (REF-ALT)") +
ylab("Allele Imbalamce") +
#scale_color_manual(values = c(cbPalette[2], "darkgrey"), labels = c(paste0("concordant, ", n_conc, "%"), paste0("discordant, ", n_disc, "%")), name = "Variant type") +
theme(axis.text=element_text(size=12),
axis.title=element_text(size=14),
legend.text = element_text(size=12),
legend.title = element_text(size=12))
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
`geom_smooth()` using formula = 'y ~ x'

outf = file.path(outdir_fig_suppl, paste0("FigS4C_motifs_AI_prediction.pdf"))
ggsave(outf, p, width = 4, height = 4)
`geom_smooth()` using formula = 'y ~ x'
---
title: "Figure_S4"
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 S4A

```{r, fig.width=6, fig.height=3}

# get variants distance to motifs (excluding peaks without motifs)
res_df = lapply(ab_tp_list, function(ab_tp) get_variant_distance2TFmotif(ab_tp, cht, fimo, same_peak = T) %>% 
                  mutate(condition = ab_tp)) %>% bind_rows()

dist_breaks = c(-1, 0, 20, 40, 60, 80, 100, 3000)
dist_labels = c("in motif", "1-20 bp", "21-40 bp", "41-60 bp", "61-80 bp", "81-100 bp", ">100 bp")

res_full = data.frame(matrix(ncol = 6, nrow = 0))
names(res_full) = c("dist_bin", "n", "share", "sd", "type", "condition")
background_sum_all_cond = data.frame(matrix(ncol = 7, nrow = 0))
names(background_sum_all_cond) = c("dist_bin", "n", "share", "share_full",  "type", "condition", "i")

for(ab_tp in ab_tp_list) {
  
  # all variants
  df_sel = res_df %>% dplyr::filter(condition == ab_tp)
  N_var = length(unique(df_sel$snp_id)) # number of variants in peaks with motifs
  N_peak = length(unique(df_sel$peak_id)) # number of peaks with motifs
  
  # significant variants
  df_sign = df_sel %>% filter(signif_strongAI)
  N_var_sign = length(unique(df_sign$snp_id)) # number of significant variants in peaks with motifs
  N_peak_sign = length(unique(df_sign$peak_id)) # number of AI peaks with motifs

  sign_sum = df_sign %>% 
    dplyr::group_by(peak_id) %>% 
    dplyr::mutate(min_dist = min(dist2motif)) %>%
    dplyr::filter(dist2motif == min_dist) %>%
    dplyr::select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
    dplyr::mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
    dplyr::group_by(dist_bin) %>%
    dplyr::summarize(n = n(), share = n / mean(N_tot), sd = sd(n), type = "real", condition = ab_tp)
  
  
  df_non_sign = df_sel %>% dplyr::group_by(peak_id) %>% dplyr::mutate(AI_peak = any(signif_strongAI)) %>% dplyr::filter(!AI_peak) %>% ungroup()
  
  background_sum_all = lapply(1:10000, function(i) {
    
    #print(i)
    
    # 1. select same number of peaks as in AI peaks
    peak_ids = sample(unique(df_non_sign$peak_id), N_peak_sign)
    df_bg = df_non_sign %>% dplyr::filter(peak_id %in% peak_ids)
    
    # 2. select same number of variants as for AI peaks
    variant_ids = sample(unique(df_bg$snp_id), N_var_sign)
    df_bg %<>% filter(snp_id %in% variant_ids)
    
    N_peak_bg = length(unique(df_bg$peak_id)) 
    
    bg_sum = df_bg %>% 
      dplyr::group_by(peak_id) %>% 
      dplyr::mutate(min_dist = min(dist2motif)) %>%
      dplyr::filter(dist2motif == min_dist) %>%
      dplyr::select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
      dplyr::mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
      dplyr::group_by(dist_bin) %>%
      dplyr::summarize(n = n(), share = n / mean(N_tot), share_full = n / N_peak_bg) %>%
      dplyr::mutate(condition = ab_tp, i = i)
    
    
    bg_sum$type = "background"
    bg_sum
    
  }) %>% bind_rows()
  
  background_sum_all_cond = rbind(background_sum_all_cond, background_sum_all)
  
  background_sum = background_sum_all %>% 
    dplyr::group_by(dist_bin) %>% 
    dplyr::summarize(n = mean(n), 
                     share = mean(share), 
                     sd = sd(share_full),
                     type = "background", 
                     condition = ab_tp)
  
  
  res = rbind.data.frame(sign_sum, background_sum)
  
  res_full = rbind.data.frame(res_full, res)

}


res_full$dist_bin = factor(res_full$dist_bin, levels = rev(dist_labels))
res_full$tf_labels = ab_tp_labels[res_full$condition]  
res_full$tf_labels = factor(res_full$tf_labels, levels = ab_tp_labels)

shares_in_motif = res_full %>% 
  arrange(tf_labels) %>% 
  filter(dist_bin == "in motif", type == "real") %>% 
  select(share) %>% unlist() %>% unique() %>% round(2)


res_full$dist_bin = factor(res_full$dist_bin, levels = dist_labels)
res_full$type = factor(res_full$type, levels = unique(res_full$type))

p = ggplot(res_full, aes(x = dist_bin, y = share, color = type, group = type)) +
  facet_wrap(~tf_labels, ncol = 3) +
  geom_ribbon(data=subset(res_full, type=="background"), aes(x=dist_bin, y = share, ymin = share - sd * 2, ymax = share + sd * 2, group = type), fill = "grey70", colour=NA, alpha = .4) +
  geom_point(size = 2) + 
  geom_line(linewidth = 1) +
  scale_color_manual(values = c("grey", "darkblue"), name = "", labels = c("AI peaks with motifs", "background")) +
  theme_bw() +
  ylab("Share of peaks") +
  xlab("Distance from TF motif") +
  theme(axis.text.y = element_text(size=14), axis.text.x = element_text(size=14, angle = 45, hjust = 1),
        axis.title.x = element_text(size=16), axis.title.y = element_text(size=16),
        legend.text=element_text(size=16), legend.title=element_text(size=16))


p
outf = file.path(outdir_fig_suppl, paste0("FigS4A_dist2motif_vs_background.pdf"))
ggsave(outf, p, width = 12, height = 6)

```

**Empirical p-values for the test: real share is larger than background share**

<br>

**In motif**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="in motif")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="in motif" & condition=="bin/1012")$share) + 1)` |


<br>

**1-20 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="1-20 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="1-20 bp" & condition=="bin/1012")$share) + 1)` |

<br>

**21-40 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="21-40 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="21-40 bp" & condition=="bin/1012")$share) + 1)` |


<br>

**41-60 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="41-60 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="41-60 bp" & condition=="bin/1012")$share) + 1)` |


<br>

**61-80 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="61-80 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="61-80 bp" & condition=="bin/1012")$share) + 1)` |


<br>

**81-100 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin=="81-100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin=="81-100 bp" & condition=="bin/1012")$share) + 1)` |


<br>

**>100 bp**

| Condition | p-value |
| :----------- | :-----------: |
| Twi 2-4h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="twi/24")$share >= subset(res_full, type=="real" & condition=="twi/24" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="twi/24")$share) + 1)` |
| CTCF 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="ctcf/68")$share >= subset(res_full, type=="real" & condition=="ctcf/68" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="ctcf/68")$share) + 1)` |
| Mef2 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="mef2/68")$share >= subset(res_full, type=="real" & condition=="mef2/68" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="mef2/68")$share) + 1)` |
| Mef2 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="mef2/1012")$share >= subset(res_full, type=="real" & condition=="mef2/1012" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="mef2/1012")$share) + 1)` |
| Bin 6-8h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="bin/68")$share >= subset(res_full, type=="real" & condition=="bin/68" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="bin/68")$share) + 1)` |
| Bin 10-12h | `r (sum(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="bin/1012")$share >= subset(res_full, type=="real" & condition=="bin/1012" & dist_bin==">100 bp")$share) + 1) / (length(subset(background_sum_all_cond, dist_bin==">100 bp" & condition=="bin/1012")$share) + 1)` |




# Figure S4B - Fisher's tests: enrichments of significant variants in motiifs in / outside peaks

```{r}
out_df = data.frame(matrix(ncol = 10, nrow = 0))
names(out_df) = c("condition", "motif", "N_sign", "N_ns", "N_sign_motif", "N_ns_motif", "N_motif", "group", "odd_ratio", "pvalue")
i = 1

for(ab_tp in ab_tp_list) {
  
  tf = TFs[ab_tp]
  print(ab_tp)
  cht_sel = cht %>% filter(condition == ab_tp)
  
  # all signif variants - all, in and outside peaks
  N1 = cht_sel %>% filter(signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N1_a = cht_sel %>% filter(signif_strongAI & dist2summit < 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N1_b = cht_sel %>% filter(signif_strongAI & dist2summit >= 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  
  # all n.s. variants - in and outside peaks
  N2 = cht_sel %>% filter(!signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N2_a = cht_sel %>% filter(!signif_strongAI & dist2summit < 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N2_b = cht_sel %>% filter(!signif_strongAI & dist2summit >= 250) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  
  
  # variants in the motifs of selected TFs
  fimo_selected = parse_motifs_in_two_alleles(ab_tp, cht_sel, radius = 15, path2_base1 = "/all_variants_alleles/FIMO/",
                                              path2_base2 = "combined_motifs/fimo.tsv", 
                                              peak_radius = 250, subset_motif = F, subset_cht = T) 
  
  
  # 1. Enrichment in cognate motifs
  print("1. Enrichment in cognate motifs")
  #fimo_sel = fimo_selected %>% filter(motif_alt_id == tf & snp_id %in% cht_sel$snp_id)
  fimo_sel = fimo_selected %>% filter(motif_alt_id == tf)  # equivalent
  cognate_motif_ids = unique(fimo_sel$snp_id)
  
  # number of significant variants in motifs - all, in and outside peaks
  N3 = fimo_sel %>% filter(signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N3_a = fimo_sel %>% filter(signif_strongAI & in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N3_b = fimo_sel %>% filter(signif_strongAI & !in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  
  # number of n.s. variants in motifs - all, in and outside peaks
  N4 = fimo_sel %>% filter(!signif_strongAI) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N4_a = fimo_sel %>% filter(!signif_strongAI & in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  N4_b = fimo_sel %>% filter(!signif_strongAI & !in_peak) %>% select(snp_id) %>% unique() %>% tally() %>% unlist()
  
  # mat = matrix(c(N3, N4, N1, N2), nrow = 2)
  # ft = fisher.test(mat)
  # out_df[i, ] = c(ab_tp, tf, N1, N2, N3, N4, N3 + N4, "all variants", ft$estimate, ft$p.value)
  # i = i + 1
  # print("1a. All variants")
  # print(ft)
  
  mat = matrix(c(N3_a, N4_a, N1_a, N2_a), nrow = 2)
  ft = fisher.test(mat)
  out_df[i, ] = c(ab_tp, tf, N1_a, N2_a, N3_a, N4_a, N3_a + N4_a, "in peaks", ft$estimate, ft$p.value)
  i = i + 1
  print("1b. Variants in peaks")
  print(ft)
  
  mat = matrix(c(N3_b, N4_b, N1_b, N2_b), nrow = 2)
  ft = fisher.test(mat)
  out_df[i, ] = c(ab_tp, tf, N1_b, N2_b, N3_b, N4_b, N3_b + N4_b, "outside peaks", ft$estimate, ft$p.value)
  i = i + 1
  print("1c. Variants outside peaks")
  print(ft)

}


out_df$label = rep(ab_tp_labels, each = 2)
out_df$label = factor(out_df$label, levels = ab_tp_labels)
out_df$pvalue = as.numeric(out_df$pvalue)
out_df$odd_ratio = as.numeric(out_df$odd_ratio)



p = ggplot(out_df, aes(x = label, y = odd_ratio)) +
  facet_wrap(~group) +
  geom_hline(yintercept = 1, color = "darkred") +
  geom_bar(aes(fill = -log10(pvalue)), color = "darkblue", stat = "identity", position = "dodge", width = 0.5) +
  #scale_fill_manual(name = "", values = c("darkblue", "darkgrey"), labels = c("AI peaks", "non-AI peaks")) +
  #geom_text(aes(label = round(r1, 2), x = label, y = odds_ratio + 0.07), data = df, size = 6) +
  ylab("Enrichment in cognate TF motifs \nFisher's Test Odds Ratio") +
  theme_bw() +
  theme(axis.text.x = element_text(size = 14, angle = 45, hjust = 1, colour = TFcols),
        axis.text.y = element_text(size = 12), 
        axis.title.x = element_blank(),
        axis.title.y = element_text(size = 16),
        legend.text = element_text(size=14),
        legend.title = element_text(size=14))


p

outf = file.path(outdir_fig_suppl, paste0("FigS4B_motifs_in_peaks_fisher.pdf"))
ggsave(outf, p, width = 10, height = 6)
```


# Figure S4C - Motif prediction of AI

```{r}

# Correlation between AI and delta_score

score_thres = 1
df_shared = fimo_alleles %>% filter(in_peak & !is.na(score.ref) & !is.na(score.alt)) %>%
  mutate(delta_score = as.numeric(score.ref) - as.numeric(score.alt),
         type = ifelse((AI > 0.5 & delta_score > 0) | (AI < 0.5 & delta_score < 0), "concordant", "discordant"))

df_shared %>% 
  group_by(condition, is_indel) %>% 
  summarize(min(abs(delta_score)), max(dist2summit), cor(delta_score, AI), share_concordant = sum(type == "concordant") / n(), n())

df_sum = df_shared %>% filter(abs(delta_score) > score_thres  & dist2summit < 250) %>% 
  summarize(min(abs(delta_score)), max(dist2summit), cor = cor(delta_score, AI), share_concordant = sum(type == "concordant") / n(), n())

cor = round(df_sum$cor, 2)
n_conc = round(df_sum$share_concordant, 2) * 100
n_disc = (1 - round(df_sum$share_concordant, 2)) * 100


p = ggplot(df_shared %>% filter(abs(delta_score) > score_thres ), aes(x = delta_score, y = AI, color = type)) + 
  geom_point(size = 1, color = cbPalette[2]) + 
  geom_smooth(method = "lm", se = F, color = "darkblue", size = 0.5) +
  geom_vline(xintercept = -score_thres, color = "grey", size = 0.7) +
  geom_vline(xintercept = score_thres, color = "grey", size = 0.7) +
  geom_hline(yintercept = 0.5, color = "grey", size = 0.7) +
  #geom_hline(yintercept = 0.4, color = "grey", size = 0.7) +
  theme_bw() +
  annotate(geom = "text", x = -3.5, y = 0.95, label = paste("R=", cor)) +
  annotate(geom = "text", x = -3.5, y = 0.88, label = paste("% concordant: ", n_conc), size = 4) +
  xlab("Motif score change (REF-ALT)") +
  ylab("Allele Imbalamce") +
  #scale_color_manual(values = c(cbPalette[2], "darkgrey"), labels = c(paste0("concordant, ", n_conc, "%"), paste0("discordant, ", n_disc, "%")), name = "Variant type") +
  theme(axis.text=element_text(size=12),
        axis.title=element_text(size=14),
        legend.text = element_text(size=12),
        legend.title = element_text(size=12))


p
outf = file.path(outdir_fig_suppl, paste0("FigS4C_motifs_AI_prediction.pdf"))
ggsave(outf, p, width = 4, height = 4)

```



# Figure S4D - ProBound scores on AI and non-AI variants

```{r}
ProBound_CTCF = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_CTCF_REF_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_CTCF$TF = "CTCF"
ProBound_Bin = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Bin_REF_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_Bin$TF = "Bin"
ProBound_Mef2 = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Mef2_REF_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_Mef2$TF = "Mef2"
ProBound_Twi = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Twi_REF_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_Twi$TF = "Twi"

ProBound_results_REF = rbind(ProBound_CTCF, ProBound_Bin, ProBound_Mef2, ProBound_Twi)
colnames(ProBound_results_REF) = c("variant_chr", "variant_pos", "REF", "ALT", "ProBound_bed_REF", "ProBound_seq_REF", "ProBound_score_REF", "TF")
ProBound_results_REF = ProBound_results_REF %>%
        group_by(TF) %>%
        mutate(ProBound_score_scaled_REF = ProBound_score_REF / max(ProBound_score_REF))


ProBound_ALT_CTCF = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_CTCF_ALT_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_ALT_CTCF$TF = "CTCF"
ProBound_ALT_Bin = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Bin_ALT_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_ALT_Bin$TF = "Bin"
ProBound_ALT_Mef2 = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Mef2_ALT_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_ALT_Mef2$TF = "Mef2"
ProBound_ALT_Twi = read.table("/g/furlong/project/68_F1_cisreg_ichip/analysis/ProBound/ProBound_Twi_ALT_sequence_15bp_around_variants.txt", header=TRUE)
ProBound_ALT_Twi$TF = "Twi"

ProBound_results_ALT = rbind(ProBound_ALT_CTCF, ProBound_ALT_Bin, ProBound_ALT_Mef2, ProBound_ALT_Twi)
colnames(ProBound_results_ALT) = c("variant_chr", "variant_pos", "REF", "ALT", "ProBound_bed_ALT", "ProBound_seq_ALT", "ProBound_score_ALT", "TF")
ProBound_results_ALT = ProBound_results_ALT %>%
        group_by(TF) %>%
        mutate(ProBound_score_scaled_ALT = ProBound_score_ALT / max(ProBound_score_ALT))


ProBound_results = merge(ProBound_results_REF, ProBound_results_ALT, by = c("variant_chr", "variant_pos", "REF", "ALT", "TF"), all.x=TRUE)
ProBound_results$variant_ID = paste(ProBound_results$variant_chr, ProBound_results$variant_pos, ProBound_results$TF, sep="_")
ProBound_results$ProBound_score_max = pmax(ProBound_results$ProBound_score_ALT, ProBound_results$ProBound_score_REF)


cht$variant_ID = paste(cht$snp_id, cht$ab, sep="_")
cht_ProBound = merge(cht, ProBound_results, by="variant_ID")
cht_ProBound$signif_strongAI = factor(cht_ProBound$signif_strongAI, levels=c(TRUE, FALSE))


p = ggplot(cht_ProBound, aes(x=signif_strongAI, y=ProBound_score_max, fill=signif_strongAI)) + 
      facet_wrap(~condition) +
      geom_violin(fill = "darkblue", alpha = 0.3) +
      geom_boxplot(width = 0.4, outlier.size = 0.1, fill = "darkblue", alpha = 0.5) +
      scale_y_log10() + 
      stat_compare_means(method = "wilcox") +
      xlab("Significant allelic imbalance") +
      ylab("-log(10) ProBound sscaled core") +
      theme_bw() +
      theme(axis.text.y = element_text(size=10), axis.text.x = element_text(size=10), 
        axis.title.x = element_text(size=10), axis.title.y = element_text(size=10),
        strip.text.x = element_text(size = 10), strip.text.y = element_text(size = 10),
        legend.position="none")
outf = file.path(outdir_fig_suppl, paste0("FigS4D_ProBound_max_scores_on_AI_variants.pdf"))
ggsave(outf, p, width = 6, height = 4)
```
