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 S5G
cond_combinations = combn(ab_tp_list, 2)
# top AI variants per peak
res_top = 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 = T) %>%
mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "top")
}) %>% bind_rows()
sum_top = res_top %>%
select(cond1, cond2, peak_id.x, peak_id.y, AI.x, AI.y, bin_dist) %>%
unique() %>%
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)) %>%
group_by(cond1, cond2) %>%
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.
# ssupplement
p = ggplot(sum_top, aes(x = cond2, y = cond1, fill = concordance)) +
geom_tile(color = "white") +
# scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 0.5,
# limit = c(0,1), space = "Lab",
# name="Pearson\nCorrelation") +
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 = 12, hjust = 1),
axis.text.y = element_text(size = 12),
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_suppl, paste0("FigS5G_ai_correlations_cond_comb.pdf"))
ggsave(outf, p, width = 6, height = 5)
Figure S5H - Number of coaffected peaks
res_all_comb = 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, fimo, filter_top = F) %>%
mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "full")
}) %>% bind_rows()
sum_all_comb = res_all_comb %>%
dplyr::select(cond1, cond2, peak_id.x, peak_id.y, padjust.x, padjust.y, AI.x, AI.y, AI_abs.x, AI_abs.y) %>%
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_tot_peaks = length(unique(peak_comb)),
n_sign_peaks = length(unique(peak_comb[(padjust.x < 0.01 & AI_abs.x > 0.1) | (padjust.y < 0.01 & AI_abs.y > 0.1)])),
n_comm_peaks = length(unique(peak_comb[(padjust.x < 0.01 & AI_abs.x > 0.1) & (padjust.y < 0.01 & AI_abs.y > 0.1)]))
) %>%
dplyr::mutate(comm_proportion = (n_comm_peaks / n_sign_peaks)*100,
labs = paste0(round(n_comm_peaks / n_sign_peaks, 3)*100, " %", "\n", n_comm_peaks, " / ", n_sign_peaks, "\n", n_tot_peaks)
) %>%
ungroup %>%
arrange(desc(cond1))
`summarise()` has grouped output by 'cond1'. You can override using the
`.groups` argument.
p = ggplot(sum_all_comb, aes(x = cond2, y = cond1, fill = comm_proportion)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 14,
limit = c(0,28), space = "Lab",
name="% Common\nSignificant") +
geom_text(aes(x = cond2, y = cond1, label = labs), 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))
Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.
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("FigS5H_proportion_of_coaffected_peaks.pdf"))
ggsave(outf, p, width = 6, height = 5)
---
title: "Figure S5"
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 S5G

```{r}

cond_combinations = combn(ab_tp_list, 2)

# top AI variants per peak
res_top = 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 = T) %>%
                                                       mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "top")
                                                     }) %>% bind_rows()


sum_top = res_top %>% 
  select(cond1, cond2, peak_id.x, peak_id.y, AI.x, AI.y, bin_dist) %>% 
  unique() %>%
  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)) %>%
  group_by(cond1, cond2) %>% 
  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))



# ssupplement
p = ggplot(sum_top, aes(x = cond2, y = cond1, fill = concordance)) + 
  geom_tile(color = "white") +
#  scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 0.5, 
#                       limit = c(0,1), space = "Lab", 
#                       name="Pearson\nCorrelation") +
  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 = 12, hjust = 1),
        axis.text.y = element_text(size = 12),
        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_suppl, paste0("FigS5G_ai_correlations_cond_comb.pdf"))
ggsave(outf, p,  width = 6, height = 5)


```




# Figure S5H - Number of coaffected peaks

```{r }
res_all_comb = 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, fimo, filter_top = F) %>%
                                                            mutate(cond1 = ab_tp_labels[ab_tp1], cond2 = ab_tp_labels[ab_tp2], type = "full")
                                                          }) %>% bind_rows()


sum_all_comb = res_all_comb %>% 
  dplyr::select(cond1, cond2, peak_id.x, peak_id.y, padjust.x, padjust.y, AI.x, AI.y, AI_abs.x, AI_abs.y) %>% 
  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_tot_peaks = length(unique(peak_comb)), 
         n_sign_peaks = length(unique(peak_comb[(padjust.x < 0.01 & AI_abs.x > 0.1) | (padjust.y < 0.01 & AI_abs.y > 0.1)])),
         n_comm_peaks = length(unique(peak_comb[(padjust.x < 0.01 & AI_abs.x > 0.1) & (padjust.y < 0.01 & AI_abs.y > 0.1)]))
         ) %>%
  dplyr::mutate(comm_proportion = (n_comm_peaks / n_sign_peaks)*100,
         labs = paste0(round(n_comm_peaks / n_sign_peaks, 3)*100, " %", "\n", n_comm_peaks, " / ", n_sign_peaks, "\n",  n_tot_peaks)
         ) %>%
  ungroup %>%
  arrange(desc(cond1))


p = ggplot(sum_all_comb, aes(x = cond2, y = cond1, fill = comm_proportion)) + 
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "cornsilk", high = "coral3", mid = "bisque", midpoint = 14, 
                       limit = c(0,28), space = "Lab", 
                       name="% Common\nSignificant") +
  geom_text(aes(x = cond2, y = cond1, label = labs), 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_suppl, paste0("FigS5H_proportion_of_coaffected_peaks.pdf"))
ggsave(outf, p,  width = 6, height = 5)

```
