Figure S3F - GO Enrichment for AI peaks
for (cond in c(ab_tp_list, list(ab_tp_list))) {
test_geneID = get_test_geneID(cht, cond, 1000)
background_geneID = get_background_geneID(cht, cond, 1000, test_geneID)
out = create_GOdata(test_geneID, background_geneID, "BP")
GOdata = out[[1]]
test = out[[2]]
n_nodes = length(attributes(GOdata)$graph@nodes)
# Classic Fisher test
test.stat <- new("classicCount", testStatistic = GOFisherTest, name = "Fisher test")
resultFisher <- getSigGroups(GOdata, test.stat)
# KS test
test.stat <- new("classicScore", testStatistic = GOKSTest, name = "KS tests")
resultKS <- getSigGroups(GOdata, test.stat)
# Weight algorithm
test.stat <- new("weightCount", testStatistic = GOFisherTest, name = "Fisher test", sigRatio = "ratio")
resultWeight <- getSigGroups(GOdata, test.stat)
allRes <- GenTable(GOdata, classic = resultFisher,
KS = resultKS, weight = resultWeight,
orderBy = "weight", ranksOf = "classic", topNodes = n_nodes)
allRes$Fold_Enrichment = allRes$Significant / allRes$Expected
allRes$FDR = allRes$weight
top_results = select_top_GO(allRes, 500, 10, 0.05, 10)
p = ggplot_GO_enrichment(top_results, nrow(as.data.frame(test)), length(as.data.frame(test)[as.data.frame(test)[,1] == 1, ]), cond)
print(p)
out_filename = paste0("FigS3F_GO_enrichment_AI_peaks_", gsub("/", "_", paste(cond, collapse = '_')) ,".pdf")
ggsave(file.path(outdir_fig_suppl, out_filename), p, width = 6, height = 6)
}
>> preparing features information... 2024-11-29 01:49:24 PM
>> identifying nearest features... 2024-11-29 01:49:24 PM
>> calculating distance from peak to TSS... 2024-11-29 01:49:24 PM
>> assigning genomic annotation... 2024-11-29 01:49:24 PM
>> adding gene annotation... 2024-11-29 01:49:29 PM
'select()' returned 1:1 mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:49:29 PM
>> done... 2024-11-29 01:49:29 PM
>> preparing features information... 2024-11-29 01:49:30 PM
>> identifying nearest features... 2024-11-29 01:49:30 PM
>> calculating distance from peak to TSS... 2024-11-29 01:49:30 PM
>> assigning genomic annotation... 2024-11-29 01:49:30 PM
>> adding gene annotation... 2024-11-29 01:49:31 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:49:31 PM
>> done... 2024-11-29 01:49:31 PM
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3583 GO terms found. )
Build GO DAG topology ..........
( 6174 GO terms and 13460 relations. )
Annotating nodes ...............
( 3270 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 3229 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 6174 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 3229 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 17: 4 nodes to be scored.
Level 16: 11 nodes to be scored.
Level 15: 20 nodes to be scored.
Level 14: 37 nodes to be scored.
Level 13: 74 nodes to be scored.
Level 12: 130 nodes to be scored.
Level 11: 226 nodes to be scored.
Level 10: 319 nodes to be scored.
Level 9: 452 nodes to be scored.
Level 8: 450 nodes to be scored.
Level 7: 494 nodes to be scored.
Level 6: 449 nodes to be scored.
Level 5: 312 nodes to be scored.
Level 4: 162 nodes to be scored.
Level 3: 72 nodes to be scored.
Level 2: 16 nodes to be scored.
Level 1: 1 nodes to be scored.
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.
Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
>> preparing features information... 2024-11-29 01:51:02 PM
>> identifying nearest features... 2024-11-29 01:51:02 PM
>> calculating distance from peak to TSS... 2024-11-29 01:51:02 PM
>> assigning genomic annotation... 2024-11-29 01:51:02 PM
>> adding gene annotation... 2024-11-29 01:51:03 PM
'select()' returned 1:1 mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:51:03 PM
>> done... 2024-11-29 01:51:03 PM
>> preparing features information... 2024-11-29 01:51:04 PM
>> identifying nearest features... 2024-11-29 01:51:04 PM
>> calculating distance from peak to TSS... 2024-11-29 01:51:04 PM
>> assigning genomic annotation... 2024-11-29 01:51:04 PM
>> adding gene annotation... 2024-11-29 01:51:05 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:51:05 PM
>> done... 2024-11-29 01:51:05 PM
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3072 GO terms found. )
Build GO DAG topology ..........
( 5653 GO terms and 12291 relations. )
Annotating nodes ...............
( 2339 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 2911 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 5653 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 2911 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 17: 1 nodes to be scored.
Level 16: 7 nodes to be scored.
Level 15: 17 nodes to be scored.
Level 14: 25 nodes to be scored.
Level 13: 58 nodes to be scored.
Level 12: 102 nodes to be scored.
Level 11: 193 nodes to be scored.
Level 10: 272 nodes to be scored.
Level 9: 384 nodes to be scored.
Level 8: 408 nodes to be scored.
Level 7: 467 nodes to be scored.
Level 6: 425 nodes to be scored.
Level 5: 297 nodes to be scored.
Level 4: 164 nodes to be scored.
Level 3: 74 nodes to be scored.
Level 2: 16 nodes to be scored.
Level 1: 1 nodes to be scored.

>> preparing features information... 2024-11-29 01:52:18 PM
>> identifying nearest features... 2024-11-29 01:52:18 PM
>> calculating distance from peak to TSS... 2024-11-29 01:52:19 PM
>> assigning genomic annotation... 2024-11-29 01:52:19 PM
>> adding gene annotation... 2024-11-29 01:52:20 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:52:20 PM
>> done... 2024-11-29 01:52:20 PM
>> preparing features information... 2024-11-29 01:52:20 PM
>> identifying nearest features... 2024-11-29 01:52:20 PM
>> calculating distance from peak to TSS... 2024-11-29 01:52:21 PM
>> assigning genomic annotation... 2024-11-29 01:52:21 PM
>> adding gene annotation... 2024-11-29 01:52:22 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:52:22 PM
>> done... 2024-11-29 01:52:22 PM
'select()' returned 1:many mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3547 GO terms found. )
Build GO DAG topology ..........
( 6132 GO terms and 13320 relations. )
Annotating nodes ...............
( 3147 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 3047 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 6132 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 3047 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 17: 2 nodes to be scored.
Level 16: 11 nodes to be scored.
Level 15: 20 nodes to be scored.
Level 14: 44 nodes to be scored.
Level 13: 60 nodes to be scored.
Level 12: 108 nodes to be scored.
Level 11: 202 nodes to be scored.
Level 10: 290 nodes to be scored.
Level 9: 417 nodes to be scored.
Level 8: 431 nodes to be scored.
Level 7: 480 nodes to be scored.
Level 6: 422 nodes to be scored.
Level 5: 306 nodes to be scored.
Level 4: 165 nodes to be scored.
Level 3: 71 nodes to be scored.
Level 2: 17 nodes to be scored.
Level 1: 1 nodes to be scored.

>> preparing features information... 2024-11-29 01:53:49 PM
>> identifying nearest features... 2024-11-29 01:53:49 PM
>> calculating distance from peak to TSS... 2024-11-29 01:53:49 PM
>> assigning genomic annotation... 2024-11-29 01:53:49 PM
>> adding gene annotation... 2024-11-29 01:53:50 PM
'select()' returned 1:1 mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:53:50 PM
>> done... 2024-11-29 01:53:50 PM
>> preparing features information... 2024-11-29 01:53:51 PM
>> identifying nearest features... 2024-11-29 01:53:51 PM
>> calculating distance from peak to TSS... 2024-11-29 01:53:52 PM
>> assigning genomic annotation... 2024-11-29 01:53:52 PM
>> adding gene annotation... 2024-11-29 01:53:53 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:53:53 PM
>> done... 2024-11-29 01:53:53 PM
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3712 GO terms found. )
Build GO DAG topology ..........
( 6316 GO terms and 13779 relations. )
Annotating nodes ...............
( 3568 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 3142 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 6316 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 3142 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 18: 2 nodes to be scored.
Level 17: 4 nodes to be scored.
Level 16: 9 nodes to be scored.
Level 15: 20 nodes to be scored.
Level 14: 40 nodes to be scored.
Level 13: 58 nodes to be scored.
Level 12: 108 nodes to be scored.
Level 11: 202 nodes to be scored.
Level 10: 291 nodes to be scored.
Level 9: 418 nodes to be scored.
Level 8: 466 nodes to be scored.
Level 7: 523 nodes to be scored.
Level 6: 445 nodes to be scored.
Level 5: 303 nodes to be scored.
Level 4: 164 nodes to be scored.
Level 3: 72 nodes to be scored.
Level 2: 16 nodes to be scored.
Level 1: 1 nodes to be scored.

>> preparing features information... 2024-11-29 01:55:21 PM
>> identifying nearest features... 2024-11-29 01:55:21 PM
>> calculating distance from peak to TSS... 2024-11-29 01:55:21 PM
>> assigning genomic annotation... 2024-11-29 01:55:21 PM
>> adding gene annotation... 2024-11-29 01:55:22 PM
'select()' returned 1:1 mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:55:22 PM
>> done... 2024-11-29 01:55:22 PM
>> preparing features information... 2024-11-29 01:55:22 PM
>> identifying nearest features... 2024-11-29 01:55:22 PM
>> calculating distance from peak to TSS... 2024-11-29 01:55:23 PM
>> assigning genomic annotation... 2024-11-29 01:55:23 PM
>> adding gene annotation... 2024-11-29 01:55:24 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:55:24 PM
>> done... 2024-11-29 01:55:24 PM
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3159 GO terms found. )
Build GO DAG topology ..........
( 5697 GO terms and 12408 relations. )
Annotating nodes ...............
( 2450 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 2594 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 5697 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 2594 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 18: 1 nodes to be scored.
Level 17: 2 nodes to be scored.
Level 16: 5 nodes to be scored.
Level 15: 17 nodes to be scored.
Level 14: 35 nodes to be scored.
Level 13: 55 nodes to be scored.
Level 12: 87 nodes to be scored.
Level 11: 153 nodes to be scored.
Level 10: 240 nodes to be scored.
Level 9: 341 nodes to be scored.
Level 8: 369 nodes to be scored.
Level 7: 409 nodes to be scored.
Level 6: 374 nodes to be scored.
Level 5: 275 nodes to be scored.
Level 4: 150 nodes to be scored.
Level 3: 66 nodes to be scored.
Level 2: 14 nodes to be scored.
Level 1: 1 nodes to be scored.

>> preparing features information... 2024-11-29 01:56:38 PM
>> identifying nearest features... 2024-11-29 01:56:38 PM
>> calculating distance from peak to TSS... 2024-11-29 01:56:38 PM
>> assigning genomic annotation... 2024-11-29 01:56:38 PM
>> adding gene annotation... 2024-11-29 01:56:39 PM
'select()' returned 1:1 mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:56:39 PM
>> done... 2024-11-29 01:56:39 PM
>> preparing features information... 2024-11-29 01:56:39 PM
>> identifying nearest features... 2024-11-29 01:56:39 PM
>> calculating distance from peak to TSS... 2024-11-29 01:56:40 PM
>> assigning genomic annotation... 2024-11-29 01:56:40 PM
>> adding gene annotation... 2024-11-29 01:56:40 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:56:41 PM
>> done... 2024-11-29 01:56:41 PM
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 3202 GO terms found. )
Build GO DAG topology ..........
( 5756 GO terms and 12564 relations. )
Annotating nodes ...............
( 2358 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 2484 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 5756 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 2484 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 18: 1 nodes to be scored.
Level 17: 3 nodes to be scored.
Level 16: 7 nodes to be scored.
Level 15: 14 nodes to be scored.
Level 14: 24 nodes to be scored.
Level 13: 42 nodes to be scored.
Level 12: 71 nodes to be scored.
Level 11: 131 nodes to be scored.
Level 10: 223 nodes to be scored.
Level 9: 327 nodes to be scored.
Level 8: 365 nodes to be scored.
Level 7: 408 nodes to be scored.
Level 6: 370 nodes to be scored.
Level 5: 267 nodes to be scored.
Level 4: 144 nodes to be scored.
Level 3: 70 nodes to be scored.
Level 2: 16 nodes to be scored.
Level 1: 1 nodes to be scored.

>> preparing features information... 2024-11-29 01:57:55 PM
>> identifying nearest features... 2024-11-29 01:57:55 PM
>> calculating distance from peak to TSS... 2024-11-29 01:57:55 PM
>> assigning genomic annotation... 2024-11-29 01:57:55 PM
>> adding gene annotation... 2024-11-29 01:57:56 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:57:56 PM
>> done... 2024-11-29 01:57:56 PM
>> preparing features information... 2024-11-29 01:58:01 PM
>> identifying nearest features... 2024-11-29 01:58:01 PM
>> calculating distance from peak to TSS... 2024-11-29 01:58:02 PM
>> assigning genomic annotation... 2024-11-29 01:58:02 PM
>> adding gene annotation... 2024-11-29 01:58:04 PM
'select()' returned 1:many mapping between keys and columns
>> assigning chromosome lengths 2024-11-29 01:58:05 PM
>> done... 2024-11-29 01:58:05 PM
'select()' returned 1:many mapping between keys and columns
'select()' returned 1:many mapping between keys and columns
Building most specific GOs .....
( 4188 GO terms found. )
Build GO DAG topology ..........
( 6851 GO terms and 14972 relations. )
Annotating nodes ...............
( 5165 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 4850 nontrivial nodes
parameters:
test statistic: Fisher test
-- Classic Algorithm --
the algorithm is scoring 6851 nontrivial nodes
parameters:
test statistic: KS tests
score order: increasing
-- Weight Algorithm --
The algorithm is scoring 4850 nontrivial nodes
parameters:
test statistic: Fisher test : ratio
Level 18: 2 nodes to be scored.
Level 17: 6 nodes to be scored.
Level 16: 15 nodes to be scored.
Level 15: 34 nodes to be scored.
Level 14: 73 nodes to be scored.
Level 13: 121 nodes to be scored.
Level 12: 221 nodes to be scored.
Level 11: 388 nodes to be scored.
Level 10: 552 nodes to be scored.
Level 9: 700 nodes to be scored.
Level 8: 714 nodes to be scored.
Level 7: 733 nodes to be scored.
Level 6: 598 nodes to be scored.
Level 5: 387 nodes to be scored.
Level 4: 204 nodes to be scored.
Level 3: 84 nodes to be scored.
Level 2: 17 nodes to be scored.
Level 1: 1 nodes to be scored.


---
title: "Figure_S3"
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 S3B: Indels length

```{r}
cht_sel = cht %>%
  filter(signif_strongAI) %>%
  group_by(peak_id) %>%
  #mutate(min_pval = min(P.VALUE)) %>%
  #filter(P.VALUE == min_pval) %>%
  mutate(max_indel = max(indel_length), min_indel = min(indel_length)) %>%
  filter(indel_length == max_indel) %>%
#  mutate(max_AI = max(AI_abs)) %>%
#  filter(AI_abs == max_AI) %>%
  select(condition, peak_id, indel_length, AI_abs) %>%
  unique() %>%
  mutate(bin_indel = cut(indel_length, breaks = c(0, 1, 10, Inf), labels = c("SNP", "2-10", ">10"), include.lowest = T))


cht_sel$label = factor(ab_tp_labels[cht_sel$condition], levels = ab_tp_labels)


p = ggplot(cht_sel, aes(x = bin_indel, y = AI_abs, fill = bin_indel)) + 
  geom_boxplot(width = 0.6, outlier.size = 0.1) +
  scale_fill_manual(values = cbPalette, name = "Indel length") +
  scale_y_continuous(trans = "log2", limits = c(0.1, 0.7)) +
  stat_compare_means(comparisons = list(c("SNP", ">10"), c("SNP", "2-10"))) +
  stat_compare_means() +
  xlab("") +
  ylab("Allele imbalance") +
  theme_bw() +
  facet_grid(~label) +
  stat_summary(fun.data = function(x) c(y = median(x) + 0.1, label = length(x)), geom = "text", size = 4) +
  theme(axis.text.x = element_blank(), 
        #axis.text.x = element_text(size=16, angle = 45, hjust = 1, color = TFcols), 
        axis.text.y = element_text(size=16), 
        axis.title.x = element_text(size=18), axis.title.y = element_text(size=16),
        strip.text.x = element_text(size = 14), strip.text.y = element_text(size = 14),
        legend.text=element_text(size=16), legend.title=element_text(size=16))


cht_sel %>% 
  group_by(condition) %>%
  mutate(N = n()) %>%
  group_by(condition, bin_indel) %>%
  dplyr::summarize(n = n(), share = n / mean(N), res_share = 1 - share)

p

outf = file.path(outdir_fig_suppl, paste0("FigS3B_indels_AI.pdf"))
ggsave(outf, p, width = 18, height = 5)


```


# Figure S3C: Indels length

```{r}

cht = lapply(ab_tp_list, function(ab_tp) load_cht_results(ab_tp)) %>% bind_rows()
cht_gr = cht %>% mutate(chr = TEST.SNP.CHROM, start = TEST.SNP.POS, end = TEST.SNP.POS) %>% GRanges()

# F1 ATAC-seq results

# coordinates of ATAC peaks
atac_regions_path = "/g/furlong/project/68_F1_cisreg_ichip/data/F1_paper_multiom/ATAC_feature_location_dm6.bed"
atac_regions = import(atac_regions_path, format = "bed")

# imbalance info for ATAC peaks
atac_ai_path = "/g/furlong/project/68_F1_cisreg_ichip/data/F1_paper_multiom/ATAC_all_peaks_atac_X.txt"
atac_ai = read.delim(atac_ai_path)

# additional annotations for allele imbalance
atac_ai_sum = atac_ai %>% 
  mutate(AI = padj < 0.01 & abs(0.5 - meanprop) > 0.1) %>%
  group_by(feature, time) %>% 
  summarize(n_AI = sum(AI), 
            AI_peak = any(AI),
            mean_AI = abs(0.5 - mean(meanprop)), 
            max_AI = max(abs(0.5 - meanprop)),
            n_het_lines =n())

# combine AII info with peak coordinates
atac_f1_df = merge(data.frame(atac_regions), atac_ai_sum, by.x = "name", by.y = "feature") %>% filter(!seqnames %in% c("chrX", "chrY"))
atac_ai_gr = GRanges(atac_f1_df)


# Do the analysis per TF and ATAC time-point


df = data.frame()

for (ab_tp in ab_tp_list) {
  
  tp = gsub("-", "", timepoints[ab_tp])
  
  # get TF peaks
  AI_peaks = get_peaks_from_cht(ab_tp, cht)
  names(mcols(AI_peaks)) = paste0(names(mcols(AI_peaks)), ".tf")
  
  # get ATAC peaks for selected time-point
  AI_atac = atac_ai_gr[atac_ai_gr$time == tp]
  names(mcols(AI_atac)) = paste0(names(mcols(AI_atac)), ".atac")
  
  ov = findOverlaps(AI_peaks, AI_atac)
  
  
  res = cbind.data.frame(mcols(AI_peaks[queryHits(ov)]), mcols(AI_atac[subjectHits(ov)]))
  
  print(ab_tp)
  wt = wilcox.test(mean_AI.atac ~ AI_peak.tf, res, alternative = "less")
  print(wt)
  wt = wilcox.test(max_AI.atac ~ AI_peak.tf, res, alternative = "less")
  print(wt)
  
  df = rbind.data.frame(df, res)
  
}

df %<>% 
  mutate(label = factor(ab_tp_labels[condition.tf], levels = ab_tp_labels))

df %>% group_by(condition.tf, AI_peak.tf) %>%
  summarize(mean(mean_AI.atac, mean(max_AI.atac)))

p = ggplot(df, aes(x = AI_peak.tf,  y = max_AI.atac)) +
  geom_violin(fill = "darkblue", alpha = 0.3) +
  geom_boxplot(width = 0.4, outlier.size = 0.1, fill = "darkblue", alpha = 0.7) +
  facet_wrap(~ label, ncol = 3) +
  theme_bw() +
  stat_compare_means() +
  xlab("AI TF peaks (F1)") +
  ylab("Allele imbalance of ATAC peaks (F1)") +
  theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), 
        axis.title.x = element_text(size=16), axis.title.y = element_text(size=16),
        strip.text.x = element_text(size = 14), strip.text.y = element_text(size = 14))

print(p)
ggsave(file.path(outdir_fig_suppl, "FigS3C_AI_ATAC_by_TF.pdf"), p, width = 9, height = 6)

```




# Figure S3D

```{r}

# only quantified peaks for CHT
#peaks = lapply(ab_tp_list, function(ab_tp) get_peaks_from_cht(ab_tp, cht, as_granges = T))
#names(peaks) = ab_tp_list

peaks = get_consensus_peaksets_with_AI(ab_tp_list, cht, filter = F)

dhs = load_dhs() %>% GRanges()
dhs$DHScond = cut(dhs$num_conditions, breaks = c(1, 2, 17, 19), include.lowest = T)
dhs$DHS_modERN = cut(dhs$num_modERN,breaks = c(0, 9, 251), include.lowest = T)


res = lapply(c("distal", "proximal"), function(TSS_type) {

  dhs_gr = dhs %>% as.data.frame() %>% filter(TSS == TSS_type) %>% GRanges()
  
  # DHS conditions
  df = lapply(peaks, function(x) {ov = findOverlaps(x, dhs_gr);
                                  cbind.data.frame(as.data.frame(x[queryHits(ov)]), as.data.frame(dhs_gr[subjectHits(ov)])) }) %>%
            bind_rows()
  
  fts1 = lapply(ab_tp_list, function(ab_tp) {fisher_test_two_groups(df, ab_tp,
                                                                    group1 = "isAI", group1_val = c(TRUE, FALSE), 
                                                                    group2 = "DHScond", group2_val = c("[1,2]", "(17,19]"))}) %>%
            bind_rows() %>% mutate(comp = "DHS conditions\n(1-2 vs. 18-19)", labels = ab_tp_labels)
  
  # modERN TFs
  fts2 = lapply(ab_tp_list, function(ab_tp) {fisher_test_two_groups(df, ab_tp,
                                                                    group1 = "isAI", group1_val = c(TRUE, FALSE), 
                                                                    group2 = "DHS_modERN", group2_val = c("(9,251]", "[0,9]"))}) %>%
            bind_rows() %>% mutate(comp = "modERN TFs\n(31-251 vs. 0-5)", labels = ab_tp_labels)
  
  
  res = rbind.data.frame(fts1, fts2) %>% mutate(TSS_type = paste("TSS", TSS_type)) 
  
  
}) %>% bind_rows()


# ModERN TFs

df = res %>% filter(comp2 == "DHS_modERN_(9,251]_vs_[0,9]") 
df$label = ab_tp_labels
df$label = factor(df$label, levels = ab_tp_labels)

p = ggplot(df, aes(x = label, y = odds_ratio)) +
  facet_wrap(~TSS_type) +
  geom_hline(yintercept = 1, color = "darkred") +
  geom_bar(aes(fill = -log10(pval)), 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 + 0.05), data = df, size = 6) +
  ylab("Depletioin in ubiquitously bound regions (>10 modERN TFs) \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 = 14),
        legend.text = element_text(size=14),
        legend.title = element_text(size=14))


p

outf = file.path(outdir_fig_suppl, paste0("FigS3D_modERN_fisher.pdf"))
ggsave(outf, p, width = 10, height = 6)

```



# Figure S3E


```{r}
df = res %>% filter(comp2 == "DHScond_[1,2]_vs_(17,19]") 
df$label = ab_tp_labels
df$label = factor(df$label, levels = ab_tp_labels)



p = ggplot(df, aes(x = label, y = odds_ratio)) +
  facet_wrap(~TSS_type) +
  geom_hline(yintercept = 1, color = "darkred") +
  geom_bar(aes(fill = -log10(pval)), 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 condition-specific DHS \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("FigS3E_nonubiqDHS_fisher.pdf"))
ggsave(outf, p, width = 10, height = 6)



```



# Figure S3F - GO Enrichment for AI peaks

```{r}
for (cond in c(ab_tp_list, list(ab_tp_list))) {
  
    test_geneID = get_test_geneID(cht, cond, 1000)
    background_geneID = get_background_geneID(cht, cond, 1000, test_geneID)

    out = create_GOdata(test_geneID, background_geneID, "BP")
    GOdata = out[[1]]
    test = out[[2]]
    n_nodes = length(attributes(GOdata)$graph@nodes)

    # Classic Fisher test
    test.stat <- new("classicCount", testStatistic = GOFisherTest, name = "Fisher test")
    resultFisher <- getSigGroups(GOdata, test.stat)

    # KS test
    test.stat <- new("classicScore", testStatistic = GOKSTest, name = "KS tests")
    resultKS <- getSigGroups(GOdata, test.stat)

    # Weight algorithm
    test.stat <- new("weightCount", testStatistic = GOFisherTest, name = "Fisher test", sigRatio = "ratio")
    resultWeight <- getSigGroups(GOdata, test.stat)
    
    allRes <- GenTable(GOdata, classic = resultFisher, 
           KS = resultKS, weight = resultWeight,
           orderBy = "weight", ranksOf = "classic", topNodes = n_nodes)
    allRes$Fold_Enrichment = allRes$Significant / allRes$Expected
    allRes$FDR = allRes$weight
    
    top_results = select_top_GO(allRes, 500, 10, 0.05, 10) 

    p = ggplot_GO_enrichment(top_results, nrow(as.data.frame(test)), length(as.data.frame(test)[as.data.frame(test)[,1] == 1, ]), cond)
    print(p)
    out_filename = paste0("FigS3F_GO_enrichment_AI_peaks_", gsub("/", "_", paste(cond, collapse = '_')) ,".pdf")
    ggsave(file.path(outdir_fig_suppl, out_filename), p, width = 6, height = 6)
}
```


# Figure S3G - PhyloP enrichment 


```{r}

phyloP = import.bw(config$data$genome$dm6$phyloP124)
peaks_cht = unique(data.frame("chr"=cht$TEST.SNP.CHROM, "start"=cht$REGION.START, "end"=cht$REGION.END, "seqinfo"=cht$peak_id, "signif"=cht$signif_strongAI, "cond"=cht$cond))
peaks_cht_GRange = GRanges(peaks_cht)
ov = findOverlaps(peaks_cht_GRange, phyloP)
cht_peaks_phylo =  cbind(as.data.frame(peaks_cht_GRange)[as.data.frame(ov)$queryHits, ], as.data.frame(phyloP)[as.data.frame(ov)$subjectHits, ])
colnames(cht_peaks_phylo) = c("peak_chr", "peaks_start", "peak_end", "peaks_width", "peak_strand", "seqinfo",  "signif", "cond", "seqnames", "start",  "end", "width", "strand", "score")

cht_peaks_phylo_mean = cht_peaks_phylo %>%
  dplyr::mutate(weighted_phylop = score * width) %>%
  dplyr::select(seqinfo, score, weighted_phylop, peaks_width, signif, cond) %>%
  dplyr::group_by(seqinfo, signif, cond) %>%
  dplyr::summarize(phylop = sum(weighted_phylop) / mean(peaks_width))
              

cht_peaks_phylo_mean$signif = factor(gsub(TRUE, "AI peak", gsub(FALSE, "no AI", cht_peaks_phylo_mean$signif)), levels=c("AI peak", "no AI"))
cht_peaks_phylo_mean$cond = factor(cht_peaks_phylo_mean$cond, levels=c("twi/24", "ctcf/68", "mef2/68", "mef2/1012", "bin/68",  "bin/1012"))

Summary_data = cht_peaks_phylo_mean %>%  group_by(signif) %>% summarise(n=n()) 

p = ggplot(cht_peaks_phylo_mean, aes(x=signif, y=phylop, fill=signif)) +
    geom_violin() +
    geom_boxplot(width=0.1, fill="white", alpha=0.5) +
    scale_fill_manual(values = c("orange2", "grey40")) +
    stat_compare_means(comparisons = list(c("AI peak", "no AI")), label.y=5) +
    geom_text(data=Summary_data ,aes(x = signif, y = -0.6, label=n),color="grey25", fontface = 2, size = 3.5) +
    ylim(-1,6) +
    xlab("AI Peaks vs non AI peaks") +
    ylab("PhyloP average score on peak") +
    theme_bw() + 
    theme(legend.position="none") +
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 9)) +
    theme(axis.title = element_text(size = 9), plot.title = element_text(size=9)) +
    theme(strip.text = element_text(size=9)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) 
#     facet_wrap(~cond)+

print(p)
ggsave(file.path(outdir_fig_suppl, "FigS3G_phyloP_conservation_sign_peaks.pdf"), p, width = 6, height = 6)
```






