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 S7A

variants_overlapping_motif = unique(gsub("_", ":", fimo_alleles$snp_id))

twi_24h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_twi.24_annot.txt")
twi_24h_best_basenji_in_peak = take_best_Basenji_in_peak(twi_24h)

bin_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.68_annot.txt")
bin_68h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_68h)

ctcf_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_ctcf.68_annot.txt")
ctcf_68h_best_basenji_in_peak = take_best_Basenji_in_peak(ctcf_68h)

mef2_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.68_annot.txt")
mef2_68h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_68h)

bin_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.1012_annot.txt")
bin_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_1012h)

mef2_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.1012_annot.txt")
mef2_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_1012h)

all_samples = rbind(twi_24h, bin_68h, ctcf_68h, mef2_68h, bin_1012h, mef2_1012h)
all_samples$correct_predict = factor(ifelse(all_samples$AI>0.5 & all_samples$Basenji_AI>0.5, "correct", ifelse(all_samples$AI<0.5 & all_samples$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak = rbind(twi_24h_best_basenji_in_peak, bin_68h_best_basenji_in_peak, ctcf_68h_best_basenji_in_peak, mef2_68h_best_basenji_in_peak, bin_1012h_best_basenji_in_peak, mef2_1012h_best_basenji_in_peak)
all_samples_best_basenji_in_peak$correct_predict = factor(ifelse(all_samples_best_basenji_in_peak$AI>0.5 & all_samples_best_basenji_in_peak$Basenji_AI>0.5, "correct", ifelse(all_samples_best_basenji_in_peak$AI<0.5 & all_samples_best_basenji_in_peak$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)

all_samples_best_basenji_in_peak$best_variant = 1

all_samples_2nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, all_samples_best_basenji_in_peak)
all_samples_2nd_best_basenji_in_peak$best_variant = 2

all_samples_3nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak))
all_samples_3nd_best_basenji_in_peak$best_variant = 3

all_samples_4nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak))
all_samples_4nd_best_basenji_in_peak$best_variant = 4

all_samples_5nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak, all_samples_4nd_best_basenji_in_peak))
all_samples_5nd_best_basenji_in_peak$best_variant = 5

all_samples$motif = ifelse((all_samples$variant_ID %in% variants_overlapping_motif), TRUE, FALSE)


all_samples_sub_on_motif = subset(all_samples, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_best_basenji_in_peak_sub_on_motif = subset(all_samples_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_2nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_3nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_4nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_5nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")

all_samples_sub_outside_motif = subset(all_samples, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_best_basenji_in_peak_sub_outside_motif = subset(all_samples_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_2nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_3nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_4nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_5nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")


success_proportion_on_motif = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub_on_motif), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub_on_motif), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub_on_motif), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub_on_motif), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub_on_motif), 
  "all", nrow(subset(all_samples_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub_on_motif)), nrow=2)))

colnames(success_proportion_on_motif) = c("best_Basenji_AI", "proportion_correct")
success_proportion_on_motif$proportion_correct = as.numeric(success_proportion_on_motif$proportion_correct)





success_proportion_outside_motif = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub_outside_motif), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub_outside_motif), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub_outside_motif), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub_outside_motif), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub_outside_motif), 
  "all", nrow(subset(all_samples_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub_outside_motif)), nrow=2)))

colnames(success_proportion_outside_motif) = c("best_Basenji_AI", "proportion_correct")
success_proportion_outside_motif$proportion_correct = as.numeric(success_proportion_outside_motif$proportion_correct)




background_success_proportion_df = data.frame(row.names = c("1st", "2nd", "3rd", "4th", "5th", "all"))

for (i in seq(1, 1000)) {
  
  all_samples_variant = subset(all_samples)
  background_variant_shuff = all_samples_variant %>% 
    select(variant_ID, peak_ID, significant, correct_predict) %>%
    group_by(peak_ID) %>%
    mutate(rank=sample(row_number())) %>%
    ungroup()
  background_best_variant_shuff = subset(background_variant_shuff, rank==1 & significant==TRUE)
  background_2nd_best_variant_shuff = subset(background_variant_shuff, rank==2 & significant==TRUE)
  background_3nd_best_variant_shuff = subset(background_variant_shuff, rank==3 & significant==TRUE)
  background_4nd_best_variant_shuff = subset(background_variant_shuff, rank==4 & significant==TRUE)
  background_5nd_best_variant_shuff = subset(background_variant_shuff, rank==5 & significant==TRUE)

  background_success_proportion = as.data.frame(t(matrix(c("1st", nrow(subset(background_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_best_variant_shuff), 
  "2nd", nrow(subset(background_2nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_2nd_best_variant_shuff), 
  "3rd", nrow(subset(background_3nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_3nd_best_variant_shuff), 
  "4th", nrow(subset(background_4nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_4nd_best_variant_shuff), 
  "5th", nrow(subset(background_5nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_5nd_best_variant_shuff), 
  "all", nrow(subset(background_variant_shuff, (correct_predict=="correct"))) / nrow(background_variant_shuff)), nrow=2)))

  colnames(background_success_proportion) = c("best_Basenji_AI", "proportion_correct")
  background_success_proportion$proportion_correct = as.numeric(background_success_proportion$proportion_correct)

  background_success_proportion_df = cbind(background_success_proportion_df, background_success_proportion$proportion_correct)
}


background_success_proportion_summary = data.frame(background_mean = rowMeans(background_success_proportion_df),
                                                   background_std = apply(background_success_proportion_df, 1, sd, na.rm = TRUE))
background_success_proportion_summary$best_Basenji_AI = rownames(background_success_proportion_summary)




success_proportion_on_motif = success_proportion_on_motif[c(1,2,3,4,5), ]
success_proportion_outside_motif = success_proportion_outside_motif[c(1,2,3,4,5), ]
background_success_proportion_summary = background_success_proportion_summary[c(1,2,3,4,5), ]



p = ggplot() +
    geom_point(data = success_proportion_on_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), size=3, colour="#FFA736") +
    geom_line(data = success_proportion_on_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), colour="#FFA736") +
    geom_point(data = success_proportion_outside_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), size=3, colour="grey15") +
    geom_line(data = success_proportion_outside_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), colour="grey15") +
   geom_line(data=background_success_proportion_summary, aes(x=best_Basenji_AI, y = background_mean, group=1), color = "grey60", linewidth = 1) + 
  geom_ribbon(data=background_success_proportion_summary, aes(x=best_Basenji_AI, y = background_mean, ymin = background_mean - background_std * 2, ymax = background_mean + background_std * 2, group=1), fill = "grey70", alpha = .2) +
    ylim(0.47, 1) +
    geom_hline(yintercept = 0.5, colour = "#C92B27", linetype="dashed") +
    geom_text(data = success_proportion_on_motif, aes(x = best_Basenji_AI, y = proportion_correct, label=round(proportion_correct, 3)), colour="grey15", fontface = 2, size = 4, vjust=-2) +
    geom_text(data = success_proportion_outside_motif, aes(x = best_Basenji_AI, y = proportion_correct, label=round(proportion_correct, 3)), colour="grey15", fontface = 2, size = 4, vjust=-2) +
    xlab("Best variant order (Basenji AI)") +
    ylab("Proportion of correct predictions (AI direction)") +
    theme_bw() + 
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")

p

outf = file.path(outdir_fig_suppl, paste0("FigS7A_Variants_priority_motifs_overlap.pdf"))
ggsave(outf, p, width = 6, height = 4)

3 Figure S7B

give.n <- function(x){
  return(c(y = 0.025, label = length(x))) 
  # experiment with the multiplier to find the perfect position
}


all_samples$overlaps_motif = factor(all_samples$overlaps_motif, levels=c("overlaps_motif", "no_overlap"))
all_samples_sign_peak = subset(all_samples, peak_ID %in% unique(subset(all_samples, significant==TRUE)$peak_ID) )

p = ggplot(all_samples_sign_peak, aes(x=overlaps_motif, y=Basenji_abs_AI, fill=overlaps_motif)) + 
    geom_violin(width=1.15) + 
    geom_boxplot(width=0.02, outlier.shape = NA, fill="white", alpha=0.75) +
    ylim(0,0.010) +
    xlab("Variant overlaps cognate motif") +
    ylab("Basenji absolute pAI") +
    stat_summary(fun.data = give.n, geom = "text", fun.y = 0.25) +
    scale_fill_manual(values = c("#FFA736", "grey70")) +
    theme_bw() + 
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")
   Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
   ℹ Please use the `fun` argument instead.
   This warning is displayed once every 8 hours.
   Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
   generated.
p
   Warning: Removed 7496 rows containing non-finite values (`stat_ydensity()`).
   Warning: Removed 7496 rows containing non-finite values (`stat_boxplot()`).
   Warning: Removed 7496 rows containing non-finite values (`stat_summary()`).
   Warning: `position_dodge()` requires non-overlapping x intervals
   Warning: Removed 2 rows containing missing values (`geom_text()`).

outf = file.path(outdir_fig_suppl, paste0("FigS7B_Distribution_of_abs_AI_motif_overlap.pdf"))
ggsave(outf, p, width = 6, height = 4)
   Warning: Removed 7496 rows containing non-finite values (`stat_ydensity()`).
   Warning: Removed 7496 rows containing non-finite values (`stat_boxplot()`).
   Warning: Removed 7496 rows containing non-finite values (`stat_summary()`).
   Warning: `position_dodge()` requires non-overlapping x intervals
   Warning: Removed 2 rows containing missing values (`geom_text()`).

Wilcoxon pvalue: 0

4 Figure S7C

all_samples_best_basenji_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)
all_samples_2nd_best_basenji_sub = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE)
all_samples_3nd_best_basenji_sub = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE)
all_samples_4nd_best_basenji_sub = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE)
all_samples_5nd_best_basenji_sub = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE)

all_samples_rank_variants = rbind(all_samples_best_basenji_sub, all_samples_2nd_best_basenji_sub, all_samples_3nd_best_basenji_sub, all_samples_4nd_best_basenji_sub, all_samples_5nd_best_basenji_sub)

percentages = data.frame(table(all_samples_rank_variants[, c("best_variant", "overlaps_motif")])) %>% 
        pivot_wider(names_from =overlaps_motif, values_from = Freq)
percentages$tot = percentages$no_overlap + percentages$overlaps_motif
percentages$no_overlap = percentages$no_overlap / percentages$tot
percentages$overlaps_motif = percentages$overlaps_motif / percentages$tot
percentages = percentages %>%
    select(best_variant, no_overlap, overlaps_motif) %>%
    pivot_longer(cols = c(no_overlap, overlaps_motif))
colnames(percentages) = c("best_variant", "overlaps_motif", "ratio")

p = ggplot(percentages, aes(x=best_variant, y=ratio, fill=overlaps_motif)) + 
    geom_col(position="fill") +
    xlab("Best variant order (Basenji AI)") +
    ylab("Proportion of variants overlapping cognate motif") +
    scale_y_continuous(labels = scales::percent) +
    scale_fill_manual(values = c("grey70", "#FFA736")) +
    theme_bw() + 
    geom_text(aes(label = paste0(round(ratio*100, 1), "%")), position = position_fill(vjust = 0.5), size=5) +
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")

p

outf = file.path(outdir_fig_suppl, paste0("FigS7C_Proportion_of_ranks_motifs_overlap.pdf"))
ggsave(outf, p, width = 6, height = 6)

5 Figure S7D

saturation_scores_predictions = read.table("/g/furlong/project/103_Basenji/Mattia/analysis/saturation_scores/Basenji_DataTable_predictions.txt", header=TRUE)
saturation_scores_predictions$Basenji_predict = gsub("0", "no_prediction", gsub("1", "full_prediction", gsub("partially", "partial_prediction", saturation_scores_predictions$Basenji_predict)))
saturation_scores_predictions$Basenji_predict = factor(saturation_scores_predictions$Basenji_predict, levels=c("full_prediction", "partial_prediction", "no_prediction"))

saturation_scores_predictions$motif_on_variant = ifelse(saturation_scores_predictions$variant_in_self_motif == 1, "self_motif", ifelse(saturation_scores_predictions$variant_in_other_motif == 1, "cofactor_motif", ifelse(saturation_scores_predictions$Basenji_predict == "no_prediction", "no_prediction", "no_motif")))
saturation_scores_predictions$motif_on_variant = factor(saturation_scores_predictions$motif_on_variant, levels=c("self_motif", "cofactor_motif","no_motif", "no_prediction"))
saturation_scores_predictions$condition = factor(saturation_scores_predictions$condition, levels=c("twi.24", "ctcf.68", "mef2.68", "mef2.1012", "bin.68", "bin.1012"))
saturation_scores_predictions$motifs_predictions = ifelse(saturation_scores_predictions$self_motif == 1 & saturation_scores_predictions$cofactor_motif == 1, "self_and_cofactor", ifelse(saturation_scores_predictions$self_motif == 1, "self_motif",  ifelse(saturation_scores_predictions$cofactor_motif == 1, "cofactor_motif", ifelse(saturation_scores_predictions$Basenji_predict == "no_prediction", "no_prediction" ,"no_motif"))))
saturation_scores_predictions$motifs_predictions = factor(saturation_scores_predictions$motifs_predictions, levels=c("self_and_cofactor", "self_motif", "cofactor_motif", "no_motif", "no_prediction"))

p = plot_counts_barplot(saturation_scores_predictions, "condition", "motifs_predictions") +
  scale_fill_manual(values = c("#339024", "#FF2341", "#FFA736", "grey70", "grey15")) +
  geom_text(aes(label=counts),  position = position_stack(vjust = 0.5), colour="white") +
  labs(fill="predicted_motifs")
   Scale for fill is already present.
   Adding another scale for fill, which will replace the existing scale.
p

outf = file.path(outdir_fig_suppl, paste0("FigS7D_basenji_predictions_by_predicted_motif.pdf"))
ggsave(outf, p, width = 3, height = 3)

6 Figure S7E

7 Figure S7F

twi_24h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_twi.24_annot.txt")
twi_24h_best_basenji_in_peak = take_best_Basenji_in_peak(twi_24h)

bin_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.68_annot.txt")
bin_68h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_68h)

ctcf_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_ctcf.68_annot.txt")
ctcf_68h_best_basenji_in_peak = take_best_Basenji_in_peak(ctcf_68h)

mef2_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.68_annot.txt")
mef2_68h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_68h)

bin_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.1012_annot.txt")
bin_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_1012h)

mef2_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.1012_annot.txt")
mef2_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_1012h)

all_samples = rbind(twi_24h, bin_68h, ctcf_68h, mef2_68h, bin_1012h, mef2_1012h)
all_samples$correct_predict = factor(ifelse(all_samples$AI>0.5 & all_samples$Basenji_AI>0.5, "correct", ifelse(all_samples$AI<0.5 & all_samples$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak = rbind(twi_24h_best_basenji_in_peak, bin_68h_best_basenji_in_peak, ctcf_68h_best_basenji_in_peak, mef2_68h_best_basenji_in_peak, bin_1012h_best_basenji_in_peak, mef2_1012h_best_basenji_in_peak)
all_samples_best_basenji_in_peak$correct_predict = factor(ifelse(all_samples_best_basenji_in_peak$AI>0.5 & all_samples_best_basenji_in_peak$Basenji_AI>0.5, "correct", ifelse(all_samples_best_basenji_in_peak$AI<0.5 & all_samples_best_basenji_in_peak$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)


all_samples_best_basenji_in_peak$best_variant = 1
all_samples_2nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, all_samples_best_basenji_in_peak)
all_samples_2nd_best_basenji_in_peak$best_variant = 2
all_samples_3nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak))
all_samples_3nd_best_basenji_in_peak$best_variant = 3
all_samples_4nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak))
all_samples_4nd_best_basenji_in_peak$best_variant = 4
all_samples_5nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak, all_samples_4nd_best_basenji_in_peak))
all_samples_5nd_best_basenji_in_peak$best_variant = 5

all_samples_sub = subset(all_samples, significant==TRUE)
all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)
all_samples_2nd_best_basenji_in_peak_sub = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE)
all_samples_3nd_best_basenji_in_peak_sub = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE)
all_samples_4nd_best_basenji_in_peak_sub = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE)
all_samples_5nd_best_basenji_in_peak_sub = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE)


success_proportion = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub), 
  "all", nrow(subset(all_samples_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub)), nrow=2)))

colnames(success_proportion) = c("best_Basenji_AI", "proportion_correct")
success_proportion$proportion_correct = as.numeric(success_proportion$proportion_correct)

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE & (Basenji_abs_AI > 0.1))

p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "TSS", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_TSS.pdf"))
ggsave(outf, p, width = 3, height = 3)
p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "overlaps_peak", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_peak_overlap.pdf"))
ggsave(outf, p, width = 3, height = 3)
p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "overlaps_motif", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_motif_overlap.pdf"))
ggsave(outf, p, width = 3, height = 3)
---
title: "Figure S7"
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 S7A

```{r }
variants_overlapping_motif = unique(gsub("_", ":", fimo_alleles$snp_id))

twi_24h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_twi.24_annot.txt")
twi_24h_best_basenji_in_peak = take_best_Basenji_in_peak(twi_24h)

bin_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.68_annot.txt")
bin_68h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_68h)

ctcf_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_ctcf.68_annot.txt")
ctcf_68h_best_basenji_in_peak = take_best_Basenji_in_peak(ctcf_68h)

mef2_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.68_annot.txt")
mef2_68h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_68h)

bin_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.1012_annot.txt")
bin_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_1012h)

mef2_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.1012_annot.txt")
mef2_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_1012h)

all_samples = rbind(twi_24h, bin_68h, ctcf_68h, mef2_68h, bin_1012h, mef2_1012h)
all_samples$correct_predict = factor(ifelse(all_samples$AI>0.5 & all_samples$Basenji_AI>0.5, "correct", ifelse(all_samples$AI<0.5 & all_samples$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak = rbind(twi_24h_best_basenji_in_peak, bin_68h_best_basenji_in_peak, ctcf_68h_best_basenji_in_peak, mef2_68h_best_basenji_in_peak, bin_1012h_best_basenji_in_peak, mef2_1012h_best_basenji_in_peak)
all_samples_best_basenji_in_peak$correct_predict = factor(ifelse(all_samples_best_basenji_in_peak$AI>0.5 & all_samples_best_basenji_in_peak$Basenji_AI>0.5, "correct", ifelse(all_samples_best_basenji_in_peak$AI<0.5 & all_samples_best_basenji_in_peak$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)

all_samples_best_basenji_in_peak$best_variant = 1

all_samples_2nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, all_samples_best_basenji_in_peak)
all_samples_2nd_best_basenji_in_peak$best_variant = 2

all_samples_3nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak))
all_samples_3nd_best_basenji_in_peak$best_variant = 3

all_samples_4nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak))
all_samples_4nd_best_basenji_in_peak$best_variant = 4

all_samples_5nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak, all_samples_4nd_best_basenji_in_peak))
all_samples_5nd_best_basenji_in_peak$best_variant = 5

all_samples$motif = ifelse((all_samples$variant_ID %in% variants_overlapping_motif), TRUE, FALSE)


all_samples_sub_on_motif = subset(all_samples, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_best_basenji_in_peak_sub_on_motif = subset(all_samples_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_2nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_3nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_4nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")
all_samples_5nd_best_basenji_in_peak_sub_on_motif = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="overlaps_motif")

all_samples_sub_outside_motif = subset(all_samples, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_best_basenji_in_peak_sub_outside_motif = subset(all_samples_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_2nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_3nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_4nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")
all_samples_5nd_best_basenji_in_peak_sub_outside_motif = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE & overlaps_motif=="no_overlap")


success_proportion_on_motif = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub_on_motif), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub_on_motif), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub_on_motif), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub_on_motif), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub_on_motif), 
  "all", nrow(subset(all_samples_sub_on_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub_on_motif)), nrow=2)))

colnames(success_proportion_on_motif) = c("best_Basenji_AI", "proportion_correct")
success_proportion_on_motif$proportion_correct = as.numeric(success_proportion_on_motif$proportion_correct)





success_proportion_outside_motif = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub_outside_motif), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub_outside_motif), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub_outside_motif), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub_outside_motif), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub_outside_motif), 
  "all", nrow(subset(all_samples_sub_outside_motif, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub_outside_motif)), nrow=2)))

colnames(success_proportion_outside_motif) = c("best_Basenji_AI", "proportion_correct")
success_proportion_outside_motif$proportion_correct = as.numeric(success_proportion_outside_motif$proportion_correct)




background_success_proportion_df = data.frame(row.names = c("1st", "2nd", "3rd", "4th", "5th", "all"))

for (i in seq(1, 1000)) {
  
  all_samples_variant = subset(all_samples)
  background_variant_shuff = all_samples_variant %>% 
    select(variant_ID, peak_ID, significant, correct_predict) %>%
    group_by(peak_ID) %>%
    mutate(rank=sample(row_number())) %>%
    ungroup()
  background_best_variant_shuff = subset(background_variant_shuff, rank==1 & significant==TRUE)
  background_2nd_best_variant_shuff = subset(background_variant_shuff, rank==2 & significant==TRUE)
  background_3nd_best_variant_shuff = subset(background_variant_shuff, rank==3 & significant==TRUE)
  background_4nd_best_variant_shuff = subset(background_variant_shuff, rank==4 & significant==TRUE)
  background_5nd_best_variant_shuff = subset(background_variant_shuff, rank==5 & significant==TRUE)

  background_success_proportion = as.data.frame(t(matrix(c("1st", nrow(subset(background_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_best_variant_shuff), 
  "2nd", nrow(subset(background_2nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_2nd_best_variant_shuff), 
  "3rd", nrow(subset(background_3nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_3nd_best_variant_shuff), 
  "4th", nrow(subset(background_4nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_4nd_best_variant_shuff), 
  "5th", nrow(subset(background_5nd_best_variant_shuff, (correct_predict=="correct"))) / nrow(background_5nd_best_variant_shuff), 
  "all", nrow(subset(background_variant_shuff, (correct_predict=="correct"))) / nrow(background_variant_shuff)), nrow=2)))

  colnames(background_success_proportion) = c("best_Basenji_AI", "proportion_correct")
  background_success_proportion$proportion_correct = as.numeric(background_success_proportion$proportion_correct)

  background_success_proportion_df = cbind(background_success_proportion_df, background_success_proportion$proportion_correct)
}


background_success_proportion_summary = data.frame(background_mean = rowMeans(background_success_proportion_df),
                                                   background_std = apply(background_success_proportion_df, 1, sd, na.rm = TRUE))
background_success_proportion_summary$best_Basenji_AI = rownames(background_success_proportion_summary)




success_proportion_on_motif = success_proportion_on_motif[c(1,2,3,4,5), ]
success_proportion_outside_motif = success_proportion_outside_motif[c(1,2,3,4,5), ]
background_success_proportion_summary = background_success_proportion_summary[c(1,2,3,4,5), ]



p = ggplot() +
    geom_point(data = success_proportion_on_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), size=3, colour="#FFA736") +
    geom_line(data = success_proportion_on_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), colour="#FFA736") +
    geom_point(data = success_proportion_outside_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), size=3, colour="grey15") +
    geom_line(data = success_proportion_outside_motif, aes(x=best_Basenji_AI, y=proportion_correct, group=1), colour="grey15") +
   geom_line(data=background_success_proportion_summary, aes(x=best_Basenji_AI, y = background_mean, group=1), color = "grey60", linewidth = 1) + 
  geom_ribbon(data=background_success_proportion_summary, aes(x=best_Basenji_AI, y = background_mean, ymin = background_mean - background_std * 2, ymax = background_mean + background_std * 2, group=1), fill = "grey70", alpha = .2) +
    ylim(0.47, 1) +
    geom_hline(yintercept = 0.5, colour = "#C92B27", linetype="dashed") +
    geom_text(data = success_proportion_on_motif, aes(x = best_Basenji_AI, y = proportion_correct, label=round(proportion_correct, 3)), colour="grey15", fontface = 2, size = 4, vjust=-2) +
    geom_text(data = success_proportion_outside_motif, aes(x = best_Basenji_AI, y = proportion_correct, label=round(proportion_correct, 3)), colour="grey15", fontface = 2, size = 4, vjust=-2) +
    xlab("Best variant order (Basenji AI)") +
    ylab("Proportion of correct predictions (AI direction)") +
    theme_bw() + 
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")

p
outf = file.path(outdir_fig_suppl, paste0("FigS7A_Variants_priority_motifs_overlap.pdf"))
ggsave(outf, p, width = 6, height = 4)
```


# Figure S7B


```{r }
give.n <- function(x){
  return(c(y = 0.025, label = length(x))) 
  # experiment with the multiplier to find the perfect position
}


all_samples$overlaps_motif = factor(all_samples$overlaps_motif, levels=c("overlaps_motif", "no_overlap"))
all_samples_sign_peak = subset(all_samples, peak_ID %in% unique(subset(all_samples, significant==TRUE)$peak_ID) )

p = ggplot(all_samples_sign_peak, aes(x=overlaps_motif, y=Basenji_abs_AI, fill=overlaps_motif)) + 
    geom_violin(width=1.15) + 
    geom_boxplot(width=0.02, outlier.shape = NA, fill="white", alpha=0.75) +
    ylim(0,0.010) +
    xlab("Variant overlaps cognate motif") +
    ylab("Basenji absolute pAI") +
    stat_summary(fun.data = give.n, geom = "text", fun.y = 0.25) +
    scale_fill_manual(values = c("#FFA736", "grey70")) +
    theme_bw() + 
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")


p
outf = file.path(outdir_fig_suppl, paste0("FigS7B_Distribution_of_abs_AI_motif_overlap.pdf"))
ggsave(outf, p, width = 6, height = 4)
```

Wilcoxon pvalue: `r wilcox.test(subset(all_samples, overlaps_motif=="overlaps_motif")$Basenji_abs_AI, subset(all_samples, overlaps_motif=="no_overlap")$Basenji_abs_AI)$p.value`   


# Figure S7C

```{r }
all_samples_best_basenji_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)
all_samples_2nd_best_basenji_sub = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE)
all_samples_3nd_best_basenji_sub = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE)
all_samples_4nd_best_basenji_sub = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE)
all_samples_5nd_best_basenji_sub = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE)

all_samples_rank_variants = rbind(all_samples_best_basenji_sub, all_samples_2nd_best_basenji_sub, all_samples_3nd_best_basenji_sub, all_samples_4nd_best_basenji_sub, all_samples_5nd_best_basenji_sub)

percentages = data.frame(table(all_samples_rank_variants[, c("best_variant", "overlaps_motif")])) %>% 
        pivot_wider(names_from =overlaps_motif, values_from = Freq)
percentages$tot = percentages$no_overlap + percentages$overlaps_motif
percentages$no_overlap = percentages$no_overlap / percentages$tot
percentages$overlaps_motif = percentages$overlaps_motif / percentages$tot
percentages = percentages %>%
    select(best_variant, no_overlap, overlaps_motif) %>%
    pivot_longer(cols = c(no_overlap, overlaps_motif))
colnames(percentages) = c("best_variant", "overlaps_motif", "ratio")

p = ggplot(percentages, aes(x=best_variant, y=ratio, fill=overlaps_motif)) + 
    geom_col(position="fill") +
    xlab("Best variant order (Basenji AI)") +
    ylab("Proportion of variants overlapping cognate motif") +
    scale_y_continuous(labels = scales::percent) +
    scale_fill_manual(values = c("grey70", "#FFA736")) +
    theme_bw() + 
    geom_text(aes(label = paste0(round(ratio*100, 1), "%")), position = position_fill(vjust = 0.5), size=5) +
    theme(panel.grid = element_line(colour = "grey80", linewidth = 1), axis.text = element_text(size = 12)) +
    theme(axis.title = element_text(size = 12), plot.title = element_text(size=12)) +
    theme(panel.grid.minor = element_line(linewidth = 0.25), panel.grid.major = element_line(linewidth = 0.5)) +
    theme(legend.position = "none")

p

outf = file.path(outdir_fig_suppl, paste0("FigS7C_Proportion_of_ranks_motifs_overlap.pdf"))
ggsave(outf, p, width = 6, height = 6)
```




# Figure S7D

```{r }
saturation_scores_predictions = read.table("/g/furlong/project/103_Basenji/Mattia/analysis/saturation_scores/Basenji_DataTable_predictions.txt", header=TRUE)
saturation_scores_predictions$Basenji_predict = gsub("0", "no_prediction", gsub("1", "full_prediction", gsub("partially", "partial_prediction", saturation_scores_predictions$Basenji_predict)))
saturation_scores_predictions$Basenji_predict = factor(saturation_scores_predictions$Basenji_predict, levels=c("full_prediction", "partial_prediction", "no_prediction"))

saturation_scores_predictions$motif_on_variant = ifelse(saturation_scores_predictions$variant_in_self_motif == 1, "self_motif", ifelse(saturation_scores_predictions$variant_in_other_motif == 1, "cofactor_motif", ifelse(saturation_scores_predictions$Basenji_predict == "no_prediction", "no_prediction", "no_motif")))
saturation_scores_predictions$motif_on_variant = factor(saturation_scores_predictions$motif_on_variant, levels=c("self_motif", "cofactor_motif","no_motif", "no_prediction"))
saturation_scores_predictions$condition = factor(saturation_scores_predictions$condition, levels=c("twi.24", "ctcf.68", "mef2.68", "mef2.1012", "bin.68", "bin.1012"))
saturation_scores_predictions$motifs_predictions = ifelse(saturation_scores_predictions$self_motif == 1 & saturation_scores_predictions$cofactor_motif == 1, "self_and_cofactor", ifelse(saturation_scores_predictions$self_motif == 1, "self_motif",  ifelse(saturation_scores_predictions$cofactor_motif == 1, "cofactor_motif", ifelse(saturation_scores_predictions$Basenji_predict == "no_prediction", "no_prediction" ,"no_motif"))))
saturation_scores_predictions$motifs_predictions = factor(saturation_scores_predictions$motifs_predictions, levels=c("self_and_cofactor", "self_motif", "cofactor_motif", "no_motif", "no_prediction"))

p = plot_counts_barplot(saturation_scores_predictions, "condition", "motifs_predictions") +
  scale_fill_manual(values = c("#339024", "#FF2341", "#FFA736", "grey70", "grey15")) +
  geom_text(aes(label=counts),  position = position_stack(vjust = 0.5), colour="white") +
  labs(fill="predicted_motifs")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7D_basenji_predictions_by_predicted_motif.pdf"))
ggsave(outf, p, width = 3, height = 3)
```

# Figure S7E

```{r plot_motif_in_variant, comment=NA, echo=FALSE, message=FALSE, fig.height = 6, fig.width = 12}
p = plot_counts_barplot(saturation_scores_predictions, "correct_predict", "motif_on_variant") +
  scale_fill_manual(values = c("#FF2341", "#FFA736", "grey70", "grey15")) +
  geom_text(aes(label=counts),  position = position_stack(vjust = 0.5), colour="white") +
  labs(fill="motif on variant") +
  ggtitle("Predictions and motif on variant")

p

outf = file.path(outdir_fig_suppl, paste0("FigS7E_basenji_predictions_by_predicted_motif_correct.pdf"))
ggsave(outf, p, width = 3, height = 3)
```







# Figure S7F

```{r }
twi_24h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_twi.24_annot.txt")
twi_24h_best_basenji_in_peak = take_best_Basenji_in_peak(twi_24h)

bin_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.68_annot.txt")
bin_68h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_68h)

ctcf_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_ctcf.68_annot.txt")
ctcf_68h_best_basenji_in_peak = take_best_Basenji_in_peak(ctcf_68h)

mef2_68h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.68_annot.txt")
mef2_68h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_68h)

bin_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_bin.1012_annot.txt")
bin_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(bin_1012h)

mef2_1012h = read_input("/g/furlong/project/103_Basenji/Mattia/analysis/correlations/Basenji_scores_and_allelic_imbalance_mef2.1012_annot.txt")
mef2_1012h_best_basenji_in_peak = take_best_Basenji_in_peak(mef2_1012h)

all_samples = rbind(twi_24h, bin_68h, ctcf_68h, mef2_68h, bin_1012h, mef2_1012h)
all_samples$correct_predict = factor(ifelse(all_samples$AI>0.5 & all_samples$Basenji_AI>0.5, "correct", ifelse(all_samples$AI<0.5 & all_samples$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak = rbind(twi_24h_best_basenji_in_peak, bin_68h_best_basenji_in_peak, ctcf_68h_best_basenji_in_peak, mef2_68h_best_basenji_in_peak, bin_1012h_best_basenji_in_peak, mef2_1012h_best_basenji_in_peak)
all_samples_best_basenji_in_peak$correct_predict = factor(ifelse(all_samples_best_basenji_in_peak$AI>0.5 & all_samples_best_basenji_in_peak$Basenji_AI>0.5, "correct", ifelse(all_samples_best_basenji_in_peak$AI<0.5 & all_samples_best_basenji_in_peak$Basenji_AI<0.5, "correct", "incorrect")), levels=c("incorrect", "correct"))

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)


all_samples_best_basenji_in_peak$best_variant = 1
all_samples_2nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, all_samples_best_basenji_in_peak)
all_samples_2nd_best_basenji_in_peak$best_variant = 2
all_samples_3nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak))
all_samples_3nd_best_basenji_in_peak$best_variant = 3
all_samples_4nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak))
all_samples_4nd_best_basenji_in_peak$best_variant = 4
all_samples_5nd_best_basenji_in_peak = take_next_best_Basenji_in_peak(all_samples, rbind(all_samples_best_basenji_in_peak, all_samples_2nd_best_basenji_in_peak, all_samples_3nd_best_basenji_in_peak, all_samples_4nd_best_basenji_in_peak))
all_samples_5nd_best_basenji_in_peak$best_variant = 5

all_samples_sub = subset(all_samples, significant==TRUE)
all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE)
all_samples_2nd_best_basenji_in_peak_sub = subset(all_samples_2nd_best_basenji_in_peak, significant==TRUE)
all_samples_3nd_best_basenji_in_peak_sub = subset(all_samples_3nd_best_basenji_in_peak, significant==TRUE)
all_samples_4nd_best_basenji_in_peak_sub = subset(all_samples_4nd_best_basenji_in_peak, significant==TRUE)
all_samples_5nd_best_basenji_in_peak_sub = subset(all_samples_5nd_best_basenji_in_peak, significant==TRUE)


success_proportion = as.data.frame(t(matrix(c("1st", nrow(subset(all_samples_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_best_basenji_in_peak_sub), 
  "2nd", nrow(subset(all_samples_2nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_2nd_best_basenji_in_peak_sub), 
  "3rd", nrow(subset(all_samples_3nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_3nd_best_basenji_in_peak_sub), 
  "4th", nrow(subset(all_samples_4nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_4nd_best_basenji_in_peak_sub), 
  "5th", nrow(subset(all_samples_5nd_best_basenji_in_peak_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_5nd_best_basenji_in_peak_sub), 
  "all", nrow(subset(all_samples_sub, (Basenji_AI > 0.5 & AI > 0.5) | (Basenji_AI < 0.5 & AI < 0.5))) / nrow(all_samples_sub)), nrow=2)))

colnames(success_proportion) = c("best_Basenji_AI", "proportion_correct")
success_proportion$proportion_correct = as.numeric(success_proportion$proportion_correct)

all_samples_best_basenji_in_peak_sub = subset(all_samples_best_basenji_in_peak, significant==TRUE & (Basenji_abs_AI > 0.1))

p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "TSS", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_TSS.pdf"))
ggsave(outf, p, width = 3, height = 3)
```


```{r }
p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "overlaps_peak", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_peak_overlap.pdf"))
ggsave(outf, p, width = 3, height = 3)
```


```{r }
p = plot_counts_barplot(all_samples_best_basenji_in_peak_sub, "overlaps_motif", "correct_predict")
p

outf = file.path(outdir_fig_suppl, paste0("FigS7F_basenji_predictions_by_motif_overlap.pdf"))
ggsave(outf, p, width = 3, height = 3)
```











