Contents
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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'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()
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.

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

```
