Contents

1 Setup and data

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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 3A

# 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 = 5, nrow = 0))
names(res_full) = c("dist_bin", "n", "share", "type", "condition")

for(ab_tp in ab_tp_list) {
  
  # all variants
  df_sel = res_df %>% 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 %>% 
    group_by(peak_id) %>% 
    mutate(min_dist = min(dist2motif)) %>%
    filter(dist2motif == min_dist) %>%
    select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
    mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
    group_by(dist_bin) %>%
    summarize(n = n(), share = n / mean(N_tot), type = "real", condition = ab_tp)
  
  
  df_non_sign = df_sel %>% group_by(peak_id) %>% mutate(AI_peak = any(signif_strongAI)) %>% filter(!AI_peak) %>% ungroup()
  
  background_sum = lapply(1:100, 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 %>% 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 %>% 
      group_by(peak_id) %>% 
      mutate(min_dist = min(dist2motif)) %>%
      filter(dist2motif == min_dist) %>%
      select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
      mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
      group_by(dist_bin) %>%
      summarize(n = n(), share = n / mean(N_tot), share_full = n / N_peak_bg)
    
    
    bg_sum$type = "background"
    bg_sum
    
  }) %>% bind_rows()
  
  background_sum %<>% group_by(dist_bin) %>% summarize(n = mean(n), share = mean(share), type = "background", condition = ab_tp)
  
  res = rbind.data.frame(sign_sum, background_sum)
  
  res_full = rbind.data.frame(res_full, res)

}
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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)
  
p = ggplot(res_full %>% filter(type == "real"), aes(x = tf_labels, y = n, fill = dist_bin)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c(brewer.pal(n = 9, name = "Greys")[2:7], cbPalette[2]), name = "Variant to motif\ndistance") +
  theme_bw() +
  xlab("") +
  ylab("Share of peaks \n(only peaks with motifs considered)") +
  annotate(geom = "text", label = shares_in_motif, y = 10, x = 1:6) +
  theme(axis.text.x=element_text(size=11, angle = 45, hjust = 1, colour = TFcols),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12),
        panel.grid.major = element_line(colour = "lightgrey"),
        panel.grid.minor = element_line(colour = "lightgrey"))
   Warning: Vectorized input to `element_text()` is not officially supported.
   ℹ Results may be unexpected or may change in future versions of ggplot2.
p

outf = file.path(outdir_fig_main, paste0("Fig3A_dist2motif.pdf"))
ggsave(outf, p, width = 5, height = 5)

3 Figure 3B

fimo_alleles %<>% mutate(condition_label = ab_tp_labels[condition], condition_label = factor(condition_label, levels = ab_tp_labels)) 


df_sum = fimo_alleles %>% 
  select(snp_id, condition, in_peak, signif_strongAI, condition_label) %>% unique() %>%
  group_by(in_peak) %>% 
  mutate(N = n(), in_peak = factor(in_peak, levels = c(TRUE, FALSE))) %>% 
  group_by(condition_label, in_peak, signif_strongAI) %>% 
  summarize(n = n(), share = n / mean(N))
   `summarise()` has grouped output by 'condition_label', 'in_peak'. You can
   override using the `.groups` argument.
p = ggplot(df_sum, aes(x = in_peak, y = share, fill = signif_strongAI)) + geom_bar(stat = "identity") + 
  theme_bw() +
  scale_fill_manual(values = c("lightgrey", cbPalette[2]), name = "AI variant") +
  xlab("Motif in peak") +
  ylab("Share of variants in motifs") +
  theme(axis.text.x=element_text(size=12, angle = 45, hjust = 1),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12))

p

outf = file.path(outdir_fig_main, paste0("Fig3B_motifs_in_peaks.pdf"))
ggsave(outf, p, width = 3, height = 4)

4 Figure 3C

fimo_alleles %<>% mutate(condition_label = ab_tp_labels[condition], condition_label = factor(condition_label, levels = ab_tp_labels)) 


# Distance of motif to peak summit
df_filt = fimo_alleles %>% filter(in_peak) %>% select(snp_id, condition, signif_strongAI, dist2summit) %>% unique()

p = ggplot(df_filt, aes(x = dist2summit, color = signif_strongAI)) + 
  geom_density(size = 1) + 
  theme_bw() +
  scale_color_manual(values = c("grey", cbPalette[2]), name = "AI variant") +
  xlab("Distance to peak summit") +
  ylab("Density") +
  scale_y_continuous(breaks = seq(0, 0.01, by = 0.002), labels = seq(0, 0.01, by = 0.002)) +
  theme(axis.text=element_text(size=10),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12),
        legend.position = c(0.7, 0.8))
   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.
p

outf = file.path(outdir_fig_main, paste0("Fig3C_dist2summit.pdf"))
ggsave(outf, p, width = 3, height = 4)

5 Figure 3D

fimo_alleles$motif_presence = "both alleles"
fimo_alleles$motif_presence[!is.na(fimo_alleles$score.ref) & is.na(fimo_alleles$score.alt)] = "only REF"
fimo_alleles$motif_presence[is.na(fimo_alleles$score.ref) & !is.na(fimo_alleles$score.alt)] = "only ALT"


df_filt = fimo_alleles %>% 
  filter(signif_strongAI & in_peak) %>% 
  group_by(snp_id, condition) %>% 
  mutate(n = n()) %>% filter(n == 1 & motif_presence != "both alleles") # remove cases when motif was shifted and motifs in both alleles
                                                                           
p = ggplot(df_filt %>% filter(signif_strongAI & in_peak), aes(x = motif_presence, y = AI)) + 
  geom_violin(fill = "lightgrey") + geom_boxplot(width = 0.4, outlier.size = 0.2, fill = "darkblue", alpha = 0.5) +
  geom_hline(yintercept = 0.5, color = "darkred", size = 1, linetype = "dashed") +
  theme_bw() +
  ylab("Allele Imbalance") +
  xlab("Motif presence") +
  theme(axis.text.x=element_text(size=12, angle = 45, hjust = 1), axis.text.y=element_text(size=12),
        axis.title.x=element_text(size=14), axis.title.y=element_text(size=14),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12))



p

outf = file.path(outdir_fig_main, paste0("Fig3D_motifs_in_alleles.pdf"))
ggsave(outf, p, width = 2, height = 4)

6 Figure 3E

# Correlation between AI and delta_score

score_thres = 0
df_shared = fimo_alleles %>% filter(in_peak & !is.na(score.ref) & !is.na(score.alt) & 
                                      signif_strongAI) %>%
  mutate(delta_score = as.numeric(score.ref) - as.numeric(score.alt),
         type = ifelse((AI > 0.6 & delta_score > 0) | (AI < 0.4 & delta_score < 0), "concordant", "discordant"))

df_shared %>% filter(abs(delta_score) > score_thres) %>% 
  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(signif_strongAI & 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 = 0.6, 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
   `geom_smooth()` using formula = 'y ~ x'

outf = file.path(outdir_fig_main, paste0("Fig3E_score_ai_concordance.pdf"))
ggsave(outf, p, width = 4, height = 4)
   `geom_smooth()` using formula = 'y ~ x'

7 Figure 3F - Examples of AIs by position

df = lapply(ab_tp_list, function(ab_tp) {df = prepare_snps_in_motif_2alleles_4plotting(ab_tp, cht, remove_indels = T) %>%
                                              mutate(ab = TFs[ab_tp])}) %>% 
                                         bind_rows()

tf2pos = c(3, 3, 9, 5)
names(tf2pos) = unique(TFs)

for(tf in names(tf2pos)) {
  
  pos = tf2pos[tf]
  
  tmp = df %>% filter(ab == tf & var_pos == pos) %>%
  group_by(allele) %>% mutate(mean_allele_pref = mean(share_affinity)) %>%
  arrange(mean_allele_pref) %>%
  ungroup() %>%
  mutate(allele = factor(allele, levels = unique(allele)))

  letter_cols = letter_colors
  cols = letter_cols[as.character(unique(tmp$allele))]
  
  p = ggplot(tmp, aes(x = interaction(snp_id, condition), y = share_affinity, fill = allele)) + geom_bar(stat = "identity") +
    geom_hline(yintercept = 0.5, linetype = "dashed", color = "darkgrey") +
    scale_fill_manual(name = "Allele", values = cols) +
    scale_color_manual(name = "Allele", values = cols) +
    xlab("SNPs in position") +
    ylab("Allele Imbalance") +
    ggtitle(paste(tf, ", position", pos)) +
    theme_bw() +
    theme(axis.text.y = element_text(size=14), axis.text.x = element_blank(), 
          axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
          plot.title = element_text(size= 16, hjust = 0.5))

  print(p)
  outf = file.path(outdir_fig_main, paste0("Fig3F_AI_per_pos_", tf, "_pos", pos,  ".pdf"))
  ggsave(outf, p,  width = 3, height = 2)

  
}

8 Figure 3G - Known PWMs

outf_base = file.path(outdir_fig_main, "/motif_logos/")

lapply(unique(TFs), function(tf) {print(tf)
                                  # forward strand
                                  p1 = get_motif_pfm_logo(tf, x_axis = T)
                                  print(p1)
                                  outf1 = file.path(outf_base, paste0("Fig_3G_known_pwm_fw_", tf,  "_icm.pdf"))
                                  ggsave(outf1, p1,  width = 4, height = 2)
                                  # reverse strand
                                  p2 = get_motif_pfm_logo(tf, x_axis = T, rev_comp = T)
                                  outf2 = file.path(outf_base, paste0("Fig_3G_known_pwm_rv_", tf,  "_icm.pdf"))
                                  ggsave(outf2, p2,  width = 4, height = 2)
                                  print(p2)})
   [1] "Twi"
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [1] "CTCF"
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [1] "Mef2"
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [1] "Bin"
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [[1]]

   
   [[2]]

   
   [[3]]

   
   [[4]]

9 Figure 3G - AI-PCMs and AI-ICMs

outf_base = file.path(outdir_fig_main, "/motif_logos/")

for(TF in names(TF2cond)) {
  
  cond = TF2cond[[TF]]
  df = lapply(cond, function(ab_tp) {df = prepare_snps_in_motif_2alleles_4plotting(ab_tp, cht, remove_indels = T)}) %>% bind_rows()
  print(paste0("N SNPs: ", nrow(df)/2))
  mat = make_ppm(df) + 0.01
  #p = convert_type(p, "PPM")
  p = view_logo(mat, colour.scheme = letter_colors, sort.positions = T) +
    theme_classic() +
    scale_x_continuous(breaks = 1:ncol(mat), labels = 1:ncol(mat), name = "Position") +
    ylab("# SNPs") +
    theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12),
          axis.title.y = element_text(size=12), axis.title.x = element_text(size=12),
          legend.position="none",
          plot.title = element_text(color="black", face="bold", size=16, hjust=0.5))
  print(p)
  outf1 = file.path(outf_base, paste0("Fig_3G_AI_pcm_", TF,  ".pdf"))
  ggsave(outf1, p,  width = 4, height = 2)

  mat = convert_type(mat, "PPM")  
  p = view_motifs(mat, use.type = "ICM", colour.scheme = letter_colors, sort.positions = T) +
    theme_classic() +
    scale_x_continuous(breaks = 1:ncol(mat), labels = 1:ncol(mat), name = "Position") +
    ylab("Bits") +
    theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12),
          axis.title.y = element_text(size=12), axis.title.x = element_text(size=12),
          legend.position="none",
          plot.title = element_text(color="black", face="bold", size=16, hjust=0.5))
  print(p)
  outf2 = file.path(outf_base, paste0("Fig_3G_AI_icm_", TF,  ".pdf"))
  ggsave(outf2, p,  width = 4, height = 2)
  
}
   [1] "N SNPs: 48"
   `summarise()` has grouped output by 'var_pos'. You can override using the
   `.groups` argument.
   Scale for x is already present. Adding another scale for x, which will replace
   the existing scale.
   motifs converted to class 'universalmotif'
   Scale for x is already present. Adding another scale for x, which will replace
   the existing scale.

   [1] "N SNPs: 111"
   `summarise()` has grouped output by 'var_pos'. You can override using the
   `.groups` argument.
   Scale for x is already present. Adding another scale for x, which will replace
   the existing scale.

   motifs converted to class 'universalmotif'
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [1] "N SNPs: 92"
   `summarise()` has grouped output by 'var_pos'. You can override using the
   `.groups` argument.
   Scale for x is already present. Adding another scale for x, which will replace
   the existing scale.

   motifs converted to class 'universalmotif'
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

   [1] "N SNPs: 97"
   `summarise()` has grouped output by 'var_pos'. You can override using the
   `.groups` argument.
   Scale for x is already present. Adding another scale for x, which will replace
   the existing scale.

   motifs converted to class 'universalmotif'
   Scale for x is already present.
   Adding another scale for x, which will replace the existing scale.

---
title: "Figure_3"
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 3A

```{r}

# 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 = 5, nrow = 0))
names(res_full) = c("dist_bin", "n", "share", "type", "condition")

for(ab_tp in ab_tp_list) {
  
  # all variants
  df_sel = res_df %>% 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 %>% 
    group_by(peak_id) %>% 
    mutate(min_dist = min(dist2motif)) %>%
    filter(dist2motif == min_dist) %>%
    select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
    mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
    group_by(dist_bin) %>%
    summarize(n = n(), share = n / mean(N_tot), type = "real", condition = ab_tp)
  
  
  df_non_sign = df_sel %>% group_by(peak_id) %>% mutate(AI_peak = any(signif_strongAI)) %>% filter(!AI_peak) %>% ungroup()
  
  background_sum = lapply(1:100, 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 %>% 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 %>% 
      group_by(peak_id) %>% 
      mutate(min_dist = min(dist2motif)) %>%
      filter(dist2motif == min_dist) %>%
      select(peak_id, dist2motif) %>% unique() %>% ungroup() %>%
      mutate(N_tot = n(), dist_bin = cut(dist2motif, breaks = dist_breaks, labels = dist_labels)) %>%
      group_by(dist_bin) %>%
      summarize(n = n(), share = n / mean(N_tot), share_full = n / N_peak_bg)
    
    
    bg_sum$type = "background"
    bg_sum
    
  }) %>% bind_rows()
  
  background_sum %<>% group_by(dist_bin) %>% summarize(n = mean(n), share = mean(share), 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)
  
p = ggplot(res_full %>% filter(type == "real"), aes(x = tf_labels, y = n, fill = dist_bin)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c(brewer.pal(n = 9, name = "Greys")[2:7], cbPalette[2]), name = "Variant to motif\ndistance") +
  theme_bw() +
  xlab("") +
  ylab("Share of peaks \n(only peaks with motifs considered)") +
  annotate(geom = "text", label = shares_in_motif, y = 10, x = 1:6) +
  theme(axis.text.x=element_text(size=11, angle = 45, hjust = 1, colour = TFcols),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12),
        panel.grid.major = element_line(colour = "lightgrey"),
        panel.grid.minor = element_line(colour = "lightgrey"))

p
outf = file.path(outdir_fig_main, paste0("Fig3A_dist2motif.pdf"))
ggsave(outf, p, width = 5, height = 5)

```


# Figure 3B

```{r}
fimo_alleles %<>% mutate(condition_label = ab_tp_labels[condition], condition_label = factor(condition_label, levels = ab_tp_labels)) 


df_sum = fimo_alleles %>% 
  select(snp_id, condition, in_peak, signif_strongAI, condition_label) %>% unique() %>%
  group_by(in_peak) %>% 
  mutate(N = n(), in_peak = factor(in_peak, levels = c(TRUE, FALSE))) %>% 
  group_by(condition_label, in_peak, signif_strongAI) %>% 
  summarize(n = n(), share = n / mean(N))

p = ggplot(df_sum, aes(x = in_peak, y = share, fill = signif_strongAI)) + geom_bar(stat = "identity") + 
  theme_bw() +
  scale_fill_manual(values = c("lightgrey", cbPalette[2]), name = "AI variant") +
  xlab("Motif in peak") +
  ylab("Share of variants in motifs") +
  theme(axis.text.x=element_text(size=12, angle = 45, hjust = 1),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12))

p
outf = file.path(outdir_fig_main, paste0("Fig3B_motifs_in_peaks.pdf"))
ggsave(outf, p, width = 3, height = 4)

```


# Figure 3C


```{r, fig.width=4, fig.height=3}
fimo_alleles %<>% mutate(condition_label = ab_tp_labels[condition], condition_label = factor(condition_label, levels = ab_tp_labels)) 


# Distance of motif to peak summit
df_filt = fimo_alleles %>% filter(in_peak) %>% select(snp_id, condition, signif_strongAI, dist2summit) %>% unique()

p = ggplot(df_filt, aes(x = dist2summit, color = signif_strongAI)) + 
  geom_density(size = 1) + 
  theme_bw() +
  scale_color_manual(values = c("grey", cbPalette[2]), name = "AI variant") +
  xlab("Distance to peak summit") +
  ylab("Density") +
  scale_y_continuous(breaks = seq(0, 0.01, by = 0.002), labels = seq(0, 0.01, by = 0.002)) +
  theme(axis.text=element_text(size=10),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12),
        legend.position = c(0.7, 0.8))


p
outf = file.path(outdir_fig_main, paste0("Fig3C_dist2summit.pdf"))
ggsave(outf, p, width = 3, height = 4)
```


# Figure 3D

```{r}

fimo_alleles$motif_presence = "both alleles"
fimo_alleles$motif_presence[!is.na(fimo_alleles$score.ref) & is.na(fimo_alleles$score.alt)] = "only REF"
fimo_alleles$motif_presence[is.na(fimo_alleles$score.ref) & !is.na(fimo_alleles$score.alt)] = "only ALT"


df_filt = fimo_alleles %>% 
  filter(signif_strongAI & in_peak) %>% 
  group_by(snp_id, condition) %>% 
  mutate(n = n()) %>% filter(n == 1 & motif_presence != "both alleles") # remove cases when motif was shifted and motifs in both alleles
                                                                           
p = ggplot(df_filt %>% filter(signif_strongAI & in_peak), aes(x = motif_presence, y = AI)) + 
  geom_violin(fill = "lightgrey") + geom_boxplot(width = 0.4, outlier.size = 0.2, fill = "darkblue", alpha = 0.5) +
  geom_hline(yintercept = 0.5, color = "darkred", size = 1, linetype = "dashed") +
  theme_bw() +
  ylab("Allele Imbalance") +
  xlab("Motif presence") +
  theme(axis.text.x=element_text(size=12, angle = 45, hjust = 1), axis.text.y=element_text(size=12),
        axis.title.x=element_text(size=14), axis.title.y=element_text(size=14),
        axis.title=element_text(size=12),
        legend.text = element_text(size=12))



p
outf = file.path(outdir_fig_main, paste0("Fig3D_motifs_in_alleles.pdf"))
ggsave(outf, p, width = 2, height = 4)


```


# Figure 3E

```{r}

# Correlation between AI and delta_score

score_thres = 0
df_shared = fimo_alleles %>% filter(in_peak & !is.na(score.ref) & !is.na(score.alt) & 
                                      signif_strongAI) %>%
  mutate(delta_score = as.numeric(score.ref) - as.numeric(score.alt),
         type = ifelse((AI > 0.6 & delta_score > 0) | (AI < 0.4 & delta_score < 0), "concordant", "discordant"))

df_shared %>% filter(abs(delta_score) > score_thres) %>% 
  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(signif_strongAI & 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 = 0.6, 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_main, paste0("Fig3E_score_ai_concordance.pdf"))
ggsave(outf, p, width = 4, height = 4)

```

# Figure 3F - Examples of AIs by position

```{r}
df = lapply(ab_tp_list, function(ab_tp) {df = prepare_snps_in_motif_2alleles_4plotting(ab_tp, cht, remove_indels = T) %>%
                                              mutate(ab = TFs[ab_tp])}) %>% 
                                         bind_rows()

tf2pos = c(3, 3, 9, 5)
names(tf2pos) = unique(TFs)

for(tf in names(tf2pos)) {
  
  pos = tf2pos[tf]
  
  tmp = df %>% filter(ab == tf & var_pos == pos) %>%
  group_by(allele) %>% mutate(mean_allele_pref = mean(share_affinity)) %>%
  arrange(mean_allele_pref) %>%
  ungroup() %>%
  mutate(allele = factor(allele, levels = unique(allele)))

  letter_cols = letter_colors
  cols = letter_cols[as.character(unique(tmp$allele))]
  
  p = ggplot(tmp, aes(x = interaction(snp_id, condition), y = share_affinity, fill = allele)) + geom_bar(stat = "identity") +
    geom_hline(yintercept = 0.5, linetype = "dashed", color = "darkgrey") +
    scale_fill_manual(name = "Allele", values = cols) +
    scale_color_manual(name = "Allele", values = cols) +
    xlab("SNPs in position") +
    ylab("Allele Imbalance") +
    ggtitle(paste(tf, ", position", pos)) +
    theme_bw() +
    theme(axis.text.y = element_text(size=14), axis.text.x = element_blank(), 
          axis.title.x = element_text(size=14), axis.title.y = element_text(size=14),
          plot.title = element_text(size= 16, hjust = 0.5))

  print(p)
  outf = file.path(outdir_fig_main, paste0("Fig3F_AI_per_pos_", tf, "_pos", pos,  ".pdf"))
  ggsave(outf, p,  width = 3, height = 2)

  
}

```



# Figure 3G - Known PWMs

```{r}

outf_base = file.path(outdir_fig_main, "/motif_logos/")

lapply(unique(TFs), function(tf) {print(tf)
                                  # forward strand
                                  p1 = get_motif_pfm_logo(tf, x_axis = T)
                                  print(p1)
                                  outf1 = file.path(outf_base, paste0("Fig_3G_known_pwm_fw_", tf,  "_icm.pdf"))
                                  ggsave(outf1, p1,  width = 4, height = 2)
                                  # reverse strand
                                  p2 = get_motif_pfm_logo(tf, x_axis = T, rev_comp = T)
                                  outf2 = file.path(outf_base, paste0("Fig_3G_known_pwm_rv_", tf,  "_icm.pdf"))
                                  ggsave(outf2, p2,  width = 4, height = 2)
                                  print(p2)})

```



# Figure 3G - AI-PCMs and AI-ICMs

```{r}

outf_base = file.path(outdir_fig_main, "/motif_logos/")

for(TF in names(TF2cond)) {
  
  cond = TF2cond[[TF]]
  df = lapply(cond, function(ab_tp) {df = prepare_snps_in_motif_2alleles_4plotting(ab_tp, cht, remove_indels = T)}) %>% bind_rows()
  print(paste0("N SNPs: ", nrow(df)/2))
  mat = make_ppm(df) + 0.01
  #p = convert_type(p, "PPM")
  p = view_logo(mat, colour.scheme = letter_colors, sort.positions = T) +
    theme_classic() +
    scale_x_continuous(breaks = 1:ncol(mat), labels = 1:ncol(mat), name = "Position") +
    ylab("# SNPs") +
    theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12),
          axis.title.y = element_text(size=12), axis.title.x = element_text(size=12),
          legend.position="none",
          plot.title = element_text(color="black", face="bold", size=16, hjust=0.5))
  print(p)
  outf1 = file.path(outf_base, paste0("Fig_3G_AI_pcm_", TF,  ".pdf"))
  ggsave(outf1, p,  width = 4, height = 2)

  mat = convert_type(mat, "PPM")  
  p = view_motifs(mat, use.type = "ICM", colour.scheme = letter_colors, sort.positions = T) +
    theme_classic() +
    scale_x_continuous(breaks = 1:ncol(mat), labels = 1:ncol(mat), name = "Position") +
    ylab("Bits") +
    theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12),
          axis.title.y = element_text(size=12), axis.title.x = element_text(size=12),
          legend.position="none",
          plot.title = element_text(color="black", face="bold", size=16, hjust=0.5))
  print(p)
  outf2 = file.path(outf_base, paste0("Fig_3G_AI_icm_", TF,  ".pdf"))
  ggsave(outf2, p,  width = 4, height = 2)
  
}

```
