Contents

1 Setup and data

source("../utils/utils.R")
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   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() 

2 Figure # 4A

cond_combinations = combn(ab_tp_list, 2)

# all AI variants
res_full = lapply(1:ncol(cond_combinations), function(i) {ab_tp1 = cond_combinations[1, i]
                                                          ab_tp2 = cond_combinations[2, i]
                                                          get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = F) %>%
                                                            mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "full")
                                                          }) %>% bind_rows()

# summary with correlations
sum_full = res_full %>% 
  dplyr::select(cond1, cond2, peak_id.x, peak_id.y, AI.x, AI.y, bin_dist) %>% 
  unique() %>%
  dplyr::mutate(peak_comb = paste(peak_id.x, peak_id.y, sep = "_"),
         cond1 = factor(cond1, levels = rev(ab_tp_labels)),
         cond2 = factor(cond2, levels = ab_tp_labels)) %>%
  dplyr::group_by(cond1, cond2) %>% 
  dplyr::summarize(n_variants = n(), 
            n_peaks = length(unique(peak_comb)), 
            cor = round(cor.test(AI.x, AI.y, method = "pearson")$estimate, 2),
            concordance = round((sum(ifelse((AI.x>0.5 & AI.y>0.5) | (AI.x<0.5 & AI.y<0.5), TRUE, FALSE)) / n_variants * 100), 1),
            #lab = paste(cor, paste(n_variants, n_peaks, sep = " / "), sep = "\n"),
            lab = paste(paste0(concordance, "%"), paste(n_variants, n_peaks, sep = " / "), sep = "\n"),
            cor.p = cor.test(AI.x, AI.y, method = "pearson")$p.value) %>%
  arrange(desc(cond1))
   `summarise()` has grouped output by 'cond1'. You can override using the
   `.groups` argument.
#p = ggplot(sum_full, aes(x = cond2, y = cond1, fill = cor)) + 
p = ggplot(sum_full, aes(x = cond2, y = cond1, fill = concordance)) + 
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 95, 
                       limit = c(90,100), space = "Lab", 
                       name="Proportion\nConcordant (%)") +
  geom_text(aes(x = cond2, y = cond1, label = lab), color = "black", size = 4) +
  theme_minimal()+ 
  xlab("") +
  ylab("") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 14, hjust = 1, color = TFcols[2:6]),
        axis.text.y = element_text(size = 14, color = rev(TFcols[1:5])),
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        legend.justification = c(1, 0),
        legend.position = c(0.55, 0.1),
        legend.direction = "horizontal") +
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                               title.position = "top", title.hjust = 0.5))
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print(p)

outf = file.path(outdir_fig_main, paste0("Fig4A_ai_correlations_cond_comb.pdf"))
ggsave(outf, p,  width = 6, height = 5)

3 Figure 4B-C

# 
 l1 = list()
 l2 = list()

cht_ns = cht %>% filter(!signif) 

for(i in 1:ncol(cond_combinations)) {
  
ab_tp1 = cond_combinations[1, i]
ab_tp2 = cond_combinations[2, i]
  
res_all = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht, fimo, filter_top = F)
res_ns = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_ns, fimo, filter_top = F)
res_sign = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = F)
p1 = plot_AI_pairwise_correlations(res_sign, res_all, y1 = 0.70, y2 = 0.3) # main
#l1[[i]] = p1

outf = file.path(outdir_fig_main, 
                 paste("Fig4B_ai_correlations", gsub("\\/", "", ab_tp1), gsub("\\/", "", ab_tp2), "pdf", sep = "."))
ggsave(outf, p1,  width = 4.5, height = 4.5)

res_sign = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = T)
p2 = plot_AI_pairwise_correlations(res_sign, res_all, y1 = 0.70, y2 = 0.22) # suppl
#l2[[i]] = p2
outf = file.path(outdir_fig_suppl, 
                 paste("FigS5_ai_correlations", gsub("\\/", "", ab_tp1), gsub("\\/", "", ab_tp2), "pdf", sep = "."))
ggsave(outf, p1,  width = 4.5, height = 4.5)



print(p1)

}
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#inx = c(1, 2, 4, 6, 8, 10, 11, 14, 15)

#p = do.call("grid.arrange", c(l1[inx], ncol= 3))

4 Figure 4D

vid = "chr2R_12528759"


ll_mef2_68 = get_counts_per_line("mef2/68") 
   [1] "399_399_1"
   [1] "399_399_2"
   [1] "vgn_28_1"
   [1] "vgn_28_2"
   [1] "vgn_307_1"
   [1] "vgn_307_2"
   [1] "vgn_399_1"
   [1] "vgn_399_2"
   [1] "vgn_57_1"
   [1] "vgn_57_2"
   [1] "vgn_639_1"
   [1] "vgn_639_2"
   [1] "vgn_712_1"
   [1] "vgn_712_2"
   [1] "vgn_714_1"
   [1] "vgn_714_2"
   [1] "vgn_852_1"
   [1] "vgn_852_2"
   [1] "vgn_vgn_1"
   [1] "vgn_vgn_2"
ll_ctcf = get_counts_per_line("ctcf/68") 
   [1] "399_399_1"
   [1] "399_399_2"
   [1] "vgn_28_1"
   [1] "vgn_28_2"
   [1] "vgn_307_1"
   [1] "vgn_307_2"
   [1] "vgn_399_1"
   [1] "vgn_399_2"
   [1] "vgn_57_1"
   [1] "vgn_57_2"
   [1] "vgn_639_1"
   [1] "vgn_639_2"
   [1] "vgn_712_1"
   [1] "vgn_712_2"
   [1] "vgn_714_1"
   [1] "vgn_714_2"
   [1] "vgn_852_1"
   [1] "vgn_852_2"
   [1] "vgn_vgn_1"
   [1] "vgn_vgn_2"
l1 = plot_ai_and_read_depth_for_variant(cht %>% filter(condition == "mef2/68"), ll_mef2_68, vid, "chr2R_12529068_mef2/68") 
l2 = plot_ai_and_read_depth_for_variant(cht %>% filter(condition == "ctcf/68"), ll_ctcf, vid, "chr2R_12528976_ctcf/68")

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_mef2_totcount.pdf"))
print(l1[[1]])

ggsave(outf, l1[[1]],  width = 3, height = 2)

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_mef2_as.pdf"))
print(l1[[2]])

ggsave(outf, l1[[2]],  width = 4, height = 3)

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_ctcf_totcount.pdf"))
print(l2[[1]])

ggsave(outf, l2[[1]],  width = 3, height = 2)


outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_ctcf_as.pdf"))
print(l2[[2]])

ggsave(outf, l2[[2]],  width = 4, height = 3)
---
title: "Figure 4"
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 # 4A

```{r fig4a}
cond_combinations = combn(ab_tp_list, 2)

# all AI variants
res_full = lapply(1:ncol(cond_combinations), function(i) {ab_tp1 = cond_combinations[1, i]
                                                          ab_tp2 = cond_combinations[2, i]
                                                          get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = F) %>%
                                                            mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "full")
                                                          }) %>% bind_rows()

# summary with correlations
sum_full = res_full %>% 
  dplyr::select(cond1, cond2, peak_id.x, peak_id.y, AI.x, AI.y, bin_dist) %>% 
  unique() %>%
  dplyr::mutate(peak_comb = paste(peak_id.x, peak_id.y, sep = "_"),
         cond1 = factor(cond1, levels = rev(ab_tp_labels)),
         cond2 = factor(cond2, levels = ab_tp_labels)) %>%
  dplyr::group_by(cond1, cond2) %>% 
  dplyr::summarize(n_variants = n(), 
            n_peaks = length(unique(peak_comb)), 
            cor = round(cor.test(AI.x, AI.y, method = "pearson")$estimate, 2),
            concordance = round((sum(ifelse((AI.x>0.5 & AI.y>0.5) | (AI.x<0.5 & AI.y<0.5), TRUE, FALSE)) / n_variants * 100), 1),
            #lab = paste(cor, paste(n_variants, n_peaks, sep = " / "), sep = "\n"),
            lab = paste(paste0(concordance, "%"), paste(n_variants, n_peaks, sep = " / "), sep = "\n"),
            cor.p = cor.test(AI.x, AI.y, method = "pearson")$p.value) %>%
  arrange(desc(cond1))



#p = ggplot(sum_full, aes(x = cond2, y = cond1, fill = cor)) + 
p = ggplot(sum_full, aes(x = cond2, y = cond1, fill = concordance)) + 
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 95, 
                       limit = c(90,100), space = "Lab", 
                       name="Proportion\nConcordant (%)") +
  geom_text(aes(x = cond2, y = cond1, label = lab), color = "black", size = 4) +
  theme_minimal()+ 
  xlab("") +
  ylab("") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 14, hjust = 1, color = TFcols[2:6]),
        axis.text.y = element_text(size = 14, color = rev(TFcols[1:5])),
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        legend.justification = c(1, 0),
        legend.position = c(0.55, 0.1),
        legend.direction = "horizontal") +
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                               title.position = "top", title.hjust = 0.5))


print(p)
outf = file.path(outdir_fig_main, paste0("Fig4A_ai_correlations_cond_comb.pdf"))
ggsave(outf, p,  width = 6, height = 5)

```



# Figure 4B-C

```{r}
# 
 l1 = list()
 l2 = list()

cht_ns = cht %>% filter(!signif) 

for(i in 1:ncol(cond_combinations)) {
  
ab_tp1 = cond_combinations[1, i]
ab_tp2 = cond_combinations[2, i]
  
res_all = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht, fimo, filter_top = F)
res_ns = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_ns, fimo, filter_top = F)
res_sign = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = F)
p1 = plot_AI_pairwise_correlations(res_sign, res_all, y1 = 0.70, y2 = 0.3) # main
#l1[[i]] = p1

outf = file.path(outdir_fig_main, 
                 paste("Fig4B_ai_correlations", gsub("\\/", "", ab_tp1), gsub("\\/", "", ab_tp2), "pdf", sep = "."))
ggsave(outf, p1,  width = 4.5, height = 4.5)

res_sign = get_coaffected_peaks_variants(ab_tp1, ab_tp2, cht_sign, fimo, filter_top = T)
p2 = plot_AI_pairwise_correlations(res_sign, res_all, y1 = 0.70, y2 = 0.22) # suppl
#l2[[i]] = p2
outf = file.path(outdir_fig_suppl, 
                 paste("FigS5_ai_correlations", gsub("\\/", "", ab_tp1), gsub("\\/", "", ab_tp2), "pdf", sep = "."))
ggsave(outf, p1,  width = 4.5, height = 4.5)



print(p1)

}


#inx = c(1, 2, 4, 6, 8, 10, 11, 14, 15)

#p = do.call("grid.arrange", c(l1[inx], ncol= 3))
```

# Figure 4D

```{r}
vid = "chr2R_12528759"


ll_mef2_68 = get_counts_per_line("mef2/68") 
ll_ctcf = get_counts_per_line("ctcf/68") 

l1 = plot_ai_and_read_depth_for_variant(cht %>% filter(condition == "mef2/68"), ll_mef2_68, vid, "chr2R_12529068_mef2/68") 
l2 = plot_ai_and_read_depth_for_variant(cht %>% filter(condition == "ctcf/68"), ll_ctcf, vid, "chr2R_12528976_ctcf/68")

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_mef2_totcount.pdf"))
print(l1[[1]])
ggsave(outf, l1[[1]],  width = 3, height = 2)

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_mef2_as.pdf"))
print(l1[[2]])
ggsave(outf, l1[[2]],  width = 4, height = 3)

outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_ctcf_totcount.pdf"))
print(l2[[1]])
ggsave(outf, l2[[1]],  width = 3, height = 2)


outf = file.path(outdir_fig_main, paste0("Fig4D_chr2R_12528759_ctcf_as.pdf"))
print(l2[[2]])
ggsave(outf, l2[[2]],  width = 4, height = 3)

```


