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Supplement_Revisions.Rmd
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---
title: "Supplement (Revisions)"
output:
github_document:
toc: true
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%",
eval = FALSE
)
```
```{r}
library(foreach)
library(GenomicRanges)
library(readr)
library(rtracklayer)
library(scales)
library(plyranges)
library(nlme)
library(lme4)
library(viridis)
library(ggridges)
library(RColorBrewer)
library(cowplot)
library(gridExtra)
library(doParallel)
library(data.table)
library(Signac)
library(plyr)
library(memes)
library(universalmotif)
library(HiCExperiment)
library(HiContacts)
library(InteractionSet)
library(plotgardener)
library(scico)
library(dplyr)
library(ggplot2)
library(ggpointdensity)
library(ggExtra)
library(tidyverse)
num_cores = 8
registerDoParallel(cores=num_cores)
```
# Set the theme
```{r}
my_theme <- theme_classic() +
theme(panel.grid.major = element_line(color = "#f0f0f0",
size = 0.5))+
theme(plot.title = element_text(color = "black", family = "Times New Roman", size = 18, face = "bold"),
axis.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
legend.text=element_text(color = "black", family = "Times New Roman", size =18,face = "bold"),
legend.title=element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"))
```
# Set the path
This is where we stored all those files in the protocol.
```{r}
path <- "/aryeelab/users/corri/data/replicate_FF_results/"
```
# S1 MNase HiChIP vs Intact Hi-C
### aggregated
```{r}
K562_CTCF_2_5kb_interactions_FitHiC_Q0_01 <- read_table("/aryeelab/users/corri/data/K562_CTCF_2.5kb.interactions_FitHiC_Q0.01.bed")
top_loops <- K562_CTCF_2_5kb_interactions_FitHiC_Q0_01 %>%
filter(chr1 == "chr1") %>%
arrange(`Q-Value_Bias`)
first <- top_loops %>%
makeGRangesFromDataFrame(seqnames = "chr1",
start.field = "s1",
end.field = "e1")
second <- top_loops %>%
makeGRangesFromDataFrame(seqnames = "chr2",
start.field = "s2",
end.field = "e2")
gi <- GInteractions(first,second)
```
```{r,fig.width=8,fig.height=8}
hic <- import("/aryeelab/users/corri/data/K562_MicroC_GSE237898_chr1.mcool",
format = 'mcool', focus = 'chr1:30500000-31000000',resolution = 4000)
aggr_centros <- HiContacts::aggregate(
hic, targets = gi,
BPPARAM = BiocParallel::SerialParam()
#flankingBins=20
)
#> Going through preflight checklist...
#> Parsing the entire contact matrice as a sparse matrix...
#> Modeling distance decay...
#> Filtering for contacts within provided targets...
plotMatrix(
aggr_centros, use.scores = 'detrended',
limits = c(-1,1),
scale = 'linear',
cmap = bgrColors()
) +
my_theme+
xlab("Right Anchor (CTCF)") +
ylab("Left Anchor (CTCF)")
#ggsave(paste0(path,"S_aggregated_intact_hic.png"), width=8, height=8)
```
<img src="Figures/S_aggregated_intact_hic.png" width="100%" />
### numbers of loops in common
```{r}
HiC_loops <- import("/aryeelab/users/corri/data/GSE237898_ENCFF256ZMD_loops_GRCh38.bedpe")
hichip_loops <- import("/aryeelab/users/corri/data/K562_CTCF_loops.bedpe")
```
```{r}
hichip_LA <- hichip_loops@first
hichip_LA$ID <- 1:length(hichip_LA)
hichip_RA <- hichip_loops@second
hichip_RA$ID <- 1:length(hichip_RA)
###################
HIC_LA <- HiC_loops@first
HIC_LA$ID <- 1:length(HiC_loops)
HIC_RA <- HiC_loops@second
HIC_RA$ID <- 1:length(HiC_loops)
##############
length(subsetByOverlaps(hichip_LA, HIC_LA))/ length(hichip_LA)
length(subsetByOverlaps(hichip_RA, HIC_RA))/ length(hichip_RA)
LA_OVL<- subsetByOverlaps(hichip_LA, HIC_LA)
RA_OVL<- subsetByOverlaps(hichip_RA, HIC_RA)
nrow(data.frame(LA_OVL) %>% filter(ID %in% RA_OVL$ID)) / length(hichip_loops)
```
### contact map comparison
*loop observed in both datasets*
```{r}
library(readr)
K562_CTCF_2_5kb_interactions_FitHiC_Q0_01 <- read_table("/aryeelab/users/corri/data/K562_CTCF_2.5kb.interactions_FitHiC_Q0.01.bed")
K562_CTCF_2_5kb_interactions_FitHiC_Q0_01 %>%
filter(dplyr::between(s1, 30717500, 30720000)) %>%
arrange(`P-Value_Bias`)
data.frame(HiC_loops) %>%
filter(first.seqnames == "chr1") %>%
filter(dplyr::between(first.start, 30717500, 30720000)) %>%
filter(dplyr::between(second.start, 30777500, 30780000))
```
```{r}
hichip_loops <- import("/aryeelab/users/corri/data/K562_CTCF_loops.bedpe")
hichip_loops <- hichip_loops[seqnames(hichip_loops@first) == "chr1"]
data.frame(hichip_loops) %>%
filter(dplyr::between(`first.start`, 30717500, 30720000)) %>%
filter(dplyr::between(`second.start`,30777500, 30780000))
```
```{r}
hic_1 <- import("/aryeelab/users/corri/data/K562_MicroC_GSE237898_chr1.mcool",
format = 'mcool', focus = 'chr1:30500000-31000000',resolution = 2000)
hic_2 <- import("/aryeelab/users/corri/data/k562_ctcf_chr1.mcool",
format = 'mcool', focus = 'chr1:30500000-31000000',resolution = 2000)
```
```{r}
bw <- import("/aryeelab/users/corri/data/K562_CTCF_pval_ENCFF168IFW.bigWig")
CTCF_bw <- data.frame(bw) %>%
filter(seqnames == "chr1") %>%
filter(dplyr::between(start, 30500000,31000000)) %>%
ggplot(aes(x = start, y = score))+
scale_x_continuous(labels = comma_format())+
geom_area(color = "lightseagreen",
fill = "lightseagreen")+
my_theme+
xlab("Position (chr1)") +
ggtitle("CTCF ChIP-seq")+
ylab("")
```
```{r,fig.width=8,fig.height=8}
plot_grid(plotlist = list(
plotMatrix(hic_2,
compare.to = hic_1,
use.scores = 'balanced',
caption = FALSE,
scale = 'log10')+
scale_x_continuous(limits = c(30500000,31000000),
breaks = pretty_breaks(n =3),
labels = comma_format())+
coord_cartesian(xlim = c(30500000,31000000))+
xlab("Genomic location (chr1)")+
ylab("Genomic location (chr1)")+
my_theme,
CTCF_bw +
scale_y_continuous(breaks = pretty_breaks(n = 3))+
ggtitle("")+
xlab("Genomic location (chr1)")+
my_theme+
scale_x_continuous(limits = c(30500000,31000000),
breaks = pretty_breaks(n =3),
labels = comma_format())+
theme(legend.position = "none")+
coord_cartesian(xlim = c(30500000,31000000))),
align = "hv",
axis = "tblr",
ncol=1,
rel_heights = c(4,1))
#path <- "/aryeelab/users/corri/data/replicate_FF_results/"
#ggsave(paste0(path,"S_hichip_intact_hic_balanced_contact_map.png"), width=8, height=8)
```
<img src="Figures/S_hichip_intact_hic_balanced_contact_map.png" width="100%" />
### scatterplot comparison
```{r}
ctcf_gr <- readRDS(file = "/aryeelab/users/corri/data/ALL_FIMO_CTCF_hg38.RDS")
chip <- read_tsv("/aryeelab/users/corri/data/K562_CTCF_peaks_ENCFF736NYC.bed", col_names = FALSE)
colnames(chip) <- c("chr", "start", "end", "name", "score", "strand", "signalValue", "pval", "qval", "peak")
chip_gr <- makeGRangesFromDataFrame(chip)
chip_gr$qval <- chip$qval
chip_gr$peak_mid <- (start(chip_gr) + end(chip_gr))/2
ovl <- findOverlaps(ctcf_gr,chip_gr)
out <- as.data.frame(ctcf_gr[queryHits(ovl)]) %>%
cbind(motif_id = queryHits(ovl),
chip_id = subjectHits(ovl),
chip_start = start(chip_gr)[subjectHits(ovl)],
chip_end = end(chip_gr)[subjectHits(ovl)])
motifs <- out %>%
mutate(motif_mid = (start+end)/2) %>%
mutate(chip_mid = (chip_start+chip_end)/2) %>%
mutate(dist = abs(chip_mid-motif_mid)) %>%
filter(dist < 30) %>%
group_by(chip_id) %>%
arrange(dist) %>%
dplyr::slice(1) %>%
group_by(motif_id) %>%
arrange(dist) %>%
dplyr::slice(1) %>%
ungroup()
CBSPLUS <- motifs %>%
filter(seqnames == "chr1") %>%
filter(dplyr::between( start, 30717500, 30720000)) %>%
dplyr::pull(motif_mid)
CBSMINUS <-motifs %>%
filter(seqnames == "chr1") %>%
filter(dplyr::between( start, 30777500, 30780000)) %>%
filter(strand == "-") %>%
dplyr::pull(motif_mid)
```
```{r}
#W <- 1500
W <- 5000
left_anchor <- GRanges("chr1", IRanges(CBSPLUS-W, CBSPLUS+W))
right_anchor <- GRanges("chr1", IRanges(CBSMINUS-W, CBSMINUS+W))
```
```{r}
x1 <- 30775000-1000
x2 <- 30785000+1000
```
```{r}
chr <- "chr1"
pairs <- readRDS(paste0("/aryeelab/users/corri/data/CTCF_chr_pairs/k562_ctcf_",chr, ".RDS"))
```
```{r,fig.width=8,fig.height=5}
library(ggExtra)
p <- pairs %>%
filter(dplyr::between(pos1, start(left_anchor), end(left_anchor))) %>%
filter(dplyr::between(pos2, x1,x2)) %>%
ggplot(aes(y = pos1, x = pos2))+
geom_point() +
geom_pointdensity(adjust = 1000) +
scale_color_distiller(direction =1, palette = "Blues")+
scale_x_continuous(limits = c(x1, x2),
breaks = c(30775000,30780000,30785000),
labels = comma_format())+
scale_y_continuous(labels = comma_format(),
breaks = pretty_breaks(n = 3)) +
geom_vline(xintercept =CBSMINUS, col = "grey") +
geom_hline(yintercept = CBSPLUS, col = "grey") +
# theme(legend.position = "none")+
my_theme +
xlab("Right Fragment (chr1)")+
ylab("Left Fragment (chr1)")
coord_cartesian(xlim = c(x1, x2))
p
```
```{r}
cols <- c("chr1", "pos1","chr2", "pos2")
pairs_hic <- read_table("/aryeelab/users/corri/data/K562_MicroC_GSE237898_chr1_R.pairs",
col_names = cols, comment="#",
col_types = "-cici----")
```
```{r,fig.width=8,fig.height=5}
x1 <- 30775000-1000
x2 <- 30785000+1000
p1 <- pairs_hic %>%
filter(dplyr::between(pos1, start(left_anchor), end(left_anchor))) %>%
filter(dplyr::between(pos2, x1,x2)) %>%
ggplot(aes(y = pos1, x = pos2))+
geom_point() +
scale_x_continuous(limits = c(x1, x2),
breaks = c(30775000,30780000,30785000),
labels = comma_format())+
coord_cartesian(xlim = c(x1, x2))+
scale_y_continuous(labels = comma_format(),
breaks = pretty_breaks(n = 3)) +
geom_pointdensity(adjust = 1000) +
scale_color_distiller(direction =1, palette = "Blues")+
geom_vline(xintercept =CBSMINUS, col = "grey") +
geom_hline(yintercept = CBSPLUS, col = "grey") +
my_theme +
#theme(legend.position = "none")+
xlab("Right Fragment (chr1)")+
ylab("Left Fragment (chr1)")
# geom_vline(xintercept = c(30778700))
# geom_vline(xintercept = c(30778700))
```
```{r}
motifs_gr <- readRDS("/aryeelab/users/corri/data/ALL_FIMO_CTCF_hg38.RDS")
motifs <- data.frame(motifs_gr) %>%
mutate(symbol = ifelse(strand == "+", ">", "<")) %>%
filter(seqnames == "chr1") %>%
mutate(motif_mid = (start+end)/2)
motif_plot <-data.frame(motifs_gr) %>%
mutate(symbol = ifelse(strand == "+", ">", "<")) %>%
filter(seqnames == "chr1") %>%
ggplot()+
geom_text(aes(x = (start+end)/2, y = 1, fontface=2,
label = symbol,color=symbol),
family = "Times New Roman", size=8)+
theme_classic()+
my_theme+
xlab("")+
scale_x_continuous(limits = c(x1,x2))+
scale_color_manual(values = c("darkblue","darkred")) +
theme(legend.position = "none")
```
```{r}
x1 <- 30775000-1000
x2 <- 30785000+1000
bw <- import("/aryeelab/users/corri/data/K562_CTCF_pval_ENCFF168IFW.bigWig")
CTCF_bw <- data.frame(bw) %>%
filter(seqnames == "chr1") %>%
filter(dplyr::between(start, x1,x2)) %>%
ggplot(aes(x = start, y = score))+
scale_x_continuous(labels = comma_format())+
geom_area(color = "lightseagreen",
fill = "lightseagreen")+
my_theme+
xlab("Position (chr1)") +
ggtitle("CTCF ChIP-seq")+
ylab("")
bw <- import("/aryeelab/users/corri/data/K562_RNAPII_ENCFF914WIS.bigWig")
bw_RNAPII <- data.frame(bw) %>%
filter(seqnames == "chr1") %>%
filter(dplyr::between(start, x1,x2)) %>%
ggplot(aes(x = start, y = score))+
scale_x_continuous(labels = comma_format())+
geom_area(color = "darkred",
fill = "darkred")+
my_theme+
xlab("Position (chr1)") +
ggtitle("RNAPII ChIP-seq")+
ylab("")
```
```{r,fig.width=8,fig.height=4}
x1 <- 30775000-1000
x2 <- 30785000+1000
plot_grid(plotlist = list(
CTCF_bw +
scale_y_continuous(breaks = pretty_breaks(n = 3))+
ggtitle("")+
my_theme+
scale_x_continuous(limits = c(x1, x2),
breaks = c(30775000,30780000,30785000),
labels = comma_format())+
theme(legend.position = "none")+
coord_cartesian(xlim = c(x1, x2))+
theme(plot.margin = unit(c(-0.5,-0.5,-0.5,-0.5), "cm")),
motif_plot +
scale_y_continuous(breaks = pretty_breaks(n = 2))+
ggtitle("")+
scale_x_continuous(
breaks = c(30775000,30780000,30785000),
limits = c(x1, x2),
labels = comma_format())+
coord_cartesian(xlim = c(x1, x2))+
theme(axis.line=element_blank(),axis.text.x=element_blank(),
axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.y=element_blank(),
plot.margin = unit(c(-0.5,-0.5,-0.5,-0.5), "cm")),
bw_RNAPII +
ggtitle("")+
my_theme+
scale_y_continuous(breaks = pretty_breaks(n = 3))+
scale_x_continuous(limits = c(x1, x2),
breaks = c(30775000,30780000,30785000),
labels = comma_format())+
theme(legend.position = "none")+
coord_cartesian(xlim = c(x1, x2))+
theme(plot.margin = unit(c(-0.5,-0.5,-0.5,-0.5), "cm"))
),
align = "hv",
axis = "tblr",
ncol=1,
rel_heights = c(1,0.5,1))
#ggsave(paste0(path,"S_hichip_intact_hic_chipseq.png"), width=8, height=4)
```
<img src="Figures/S_hichip_intact_hic_chipseq.png" width="100%" />
```{r,fig.width=8,fig.height=10}
plot_grid(plotlist = list(
ggMarginal(p+
theme(legend.position = "none"),
type = "density", color = "darkblue",
fill = "royalblue",alpha = 0.5),
ggMarginal(p1+
theme(legend.position = "none"),
type = "density", color = "darkblue",
fill = "royalblue",alpha = 0.5)
),
align = "hv",axis = "tblr",ncol=1)
#ggsave(paste0(path,"S_hichip_intact_hic_scatterplot_marginal.png"), width=8, height=8)
```
<img src="Figures/S_hichip_intact_hic_scatterplot_marginal.png" width="100%" />
# S2 Observed ligations
Files I'm using: stats_50bp.txt, stats_75bp.txt, stats_100bp.txt, stats_125bp.txt, stats_150bp.txt
Note to self: files moved from "/data/aryee/corri/CTCF_paper_fastq_dif_length/" to /aryeelab/users/corri/data/
```{r}
dir <- "/aryeelab/users/corri/data/"
stats_files <- (vector(mode = "list", length = 5))
iter <- 1
for (read_len in c(50,75,100,125,150)){
print(read_len)
stats <- read_table2(paste0(dir,"stats_",read_len,"bp.txt"), col_names = FALSE)
stats$read_length <- read_len
stats_files[[iter]] <- stats
iter <- iter + 1
}
stats_df <- bind_rows(stats_files)
```
```{r, fig.width = 8, fig.height = 4}
colnames(stats_df) <- c("Name","Number","Read_Length")
obs_lig <- stats_df %>%
filter(Name %in% c("pair_types/uu","pair_types/Uu","pair_types/uU","pair_types/UR","pair_types/RU")) %>%
group_by(Read_Length) %>%
summarize(obs_lig = sum(Number))
unobs_lig <- stats_df %>%
filter(Name %in% c("pair_types/UU")) %>%
group_by(Read_Length) %>%
summarize(unobs_lig = sum(Number))
all_types <- cbind(obs_lig,unobs_lig)[,c(1,2,4)]
all_types <- all_types %>%
mutate(obs_lig_freq = obs_lig / (obs_lig + unobs_lig))
RL_obs_lig <- all_types %>%
mutate(perc_obs_lig = obs_lig_freq * 100) %>%
ggplot(aes(x = Read_Length, y = perc_obs_lig,label =paste0(round(perc_obs_lig, 1), "%" ))) +
geom_point(col = "royalblue") +
geom_text(hjust=0.5, vjust=-1.2, colour = "black", family = "Times New Roman",size = 5) +
geom_line(col = "darkblue", linetype = "dashed") +
xlab("Read Length (bp)") +
ylab("Percent Observed Ligations")+
theme_classic() +
theme(panel.grid.major = element_line(color = "#f0f0f0",
size = 0.5))+
theme(plot.title = element_text(color = "black", family = "Times New Roman", size = 18, face = "bold"),
axis.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.text.y = element_text(color = "black", family = "Times New Roman", size = 16,face = "bold"),
axis.title.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.ticks.x=element_blank(),
legend.text=element_text(color = "black", family = "Times New Roman", size =18,face = "bold"),
legend.title=element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"))+
ylim(c(0,37)) +
ggtitle("% Observed Ligations Increases with Read Length")
RL_obs_lig
#ggsave(paste0(path,"S_obs_lig.png"), width=8, height=4)
```
<img src="Figures/S_obs_lig.png" width="100%" />
# S3 Replicates
### S3A Density
```{r}
ctcf_motifs<- readRDS("/aryeelab/users/corri/data/ALL_FIMO_CTCF_hg38.RDS")
data.frame(ctcf_motifs) %>%
filter(seqnames == "chr1") %>%
filter(start == 30779763)
```
```{r}
# PAIRS BY SAMPLE
pairs <- readRDS("/aryeelab/users/corri/data/k562_ctcf/pairs_CTCF_chr1_sample_id_Oct24_2022.RDS")
ctcf_loc <- (30779763+30779781)/2
left_anchor <- pairs %>%
dplyr::filter(type %in% c("uu", "uU", "UR")) %>%
dplyr::select(chr1, pos1, strand1, type, sample_id) %>%
dplyr::rename(chr = chr1, pos = pos1, strand = strand1)
right_anchor <- pairs %>%
dplyr::filter(type %in% c("uu", "Uu", "RU")) %>%
dplyr::select(chr2, pos2, strand2, type, sample_id) %>%
dplyr::rename(chr = chr2, pos = pos2, strand = strand2)
pairs <- rbind(left_anchor, right_anchor)
r1_gr <- pairs %>%
makeGRangesFromDataFrame(seqnames.field = "chr",
start.field = "pos",
end.field = "pos",
keep.extra.columns = TRUE)
width <- 200
right_anchor <- GRanges("chr1", IRanges(ctcf_loc - 200, ctcf_loc + 200))
idx2 <- queryHits(findOverlaps(r1_gr, right_anchor))
```
```{r}
pos_density <- (vector(mode = "list", length = 4))
neg_density <- (vector(mode = "list", length = 4))
iter <- 1
for (sample_id1 in levels(as.factor(pairs$sample_id))){
print(sample_id1)
print(iter)
summ <- pairs[idx2,] %>%
filter(sample_id == sample_id1) %>%
mutate(pos2 = pos- ctcf_loc)
pos_density[[iter]] <- density(summ$pos2[summ$strand == "+"])
neg_density[[iter]] <- density(summ$pos2[summ$strand == "-"])
iter <- iter + 1
}
```
```{r, warning = FALSE, message = FALSE,fig.width=8,fig.height=5}
# plot the results of the density call
linetype <- "solid"
alpha <- 1
ggplot(data.frame(x = pos_density[[1]]$x, y = pos_density[[1]]$y)) +
aes(x = x, y = y) +
geom_line(color = "darkred", alpha = alpha, linetype = linetype) +
geom_line(aes(x = neg_density[[1]]$x, y = -1 * neg_density[[1]]$y), color="darkblue", alpha = alpha, linetype = linetype) +
geom_line(aes(x = neg_density[[2]]$x, y = -1 * neg_density[[2]]$y), color="darkblue", alpha = alpha, linetype = linetype) +
geom_line(aes(x = neg_density[[3]]$x, y = -1 * neg_density[[3]]$y), color="darkblue", alpha = alpha, linetype = linetype) +
geom_line(aes(x = neg_density[[4]]$x, y = -1 * neg_density[[4]]$y), color="darkblue", alpha = alpha, linetype = linetype) +
geom_line(aes(x = pos_density[[2]]$x, y = pos_density[[2]]$y), color="darkred", alpha = alpha, linetype = linetype) +
geom_line(aes(x = pos_density[[3]]$x, y = pos_density[[3]]$y), color="darkred", alpha = alpha, linetype = linetype) +
geom_line(aes(x = pos_density[[4]]$x, y = pos_density[[4]]$y), color="darkred", alpha = alpha, linetype = linetype) +
geom_vline(xintercept = 75, linetype = "dashed") +
geom_vline(xintercept = -75, linetype = "dashed") +
annotate("text", x = -180, y = -0.025, label = "CTCF",family = "Times New Roman",size = 5,fontface =2) +
annotate("text", x = -180, y = 0.015, label = "+ Reads", color="darkred",family = "Times New Roman",size = 5,fontface =2) +
annotate("text", x = -180, y = -0.015, label = "- Reads", color="darkblue",family = "Times New Roman",size = 5,fontface =2) +
xlab("Distance from CTCF Motif") +
ylab("Density") +
annotate("text", x = 0, y = -0.025, label = "<", color="black",family = "Times New Roman",size = 8,fontface =2) +
geom_hline(yintercept = 0, linetype=2) +
scale_x_continuous(labels = comma_format(), limits=c(-200,200),breaks = c(-150,-75,0,75,150)) +
annotate("text",x = 60, y = 0.015, label = "Q1", size=5, col = "darkred",family = "Times New Roman",fontface =2) +
annotate("text",x = -60, y = 0.015, label = "Q2", size=5, col = "darkred",family = "Times New Roman",fontface =2) +
annotate("text",x = -60, y = -0.015, label = "Q3", size=5, col = "darkblue",family = "Times New Roman",fontface =2) +
annotate("text",x = 60, y = -0.015, label = "Q4", size=5, col = "darkblue",family = "Times New Roman",fontface =2) +
annotate("text",x = -100, y = -0.005, label = "-75", size=5, col = "black",family = "Times New Roman",fontface =2)+
annotate("text",x = 100, y = -0.005, label = "+75", size=5, col = "black",family = "Times New Roman",fontface =2)+
annotate("text",x = 0, y = -0.034, label = "150 bp", size=5, col = "black",family = "Times New Roman",fontface =2)+
annotate("segment", x=-75, xend=75, y = -0.04, yend=-0.04, colour = "black", arrow=arrow(ends='both'),size = 2)+
ylim(c( -0.04, 0.04))+
geom_vline(xintercept = 0, col = "magenta", linetype = "dashed")+
theme_classic()+
theme(panel.grid.major = element_line(color = "#f0f0f0",
size = 0.5))+
theme(plot.title = element_text(color = "black", family = "Times New Roman", size = 18, face = "bold"),
axis.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.ticks.x=element_blank(),
legend.text=element_text(color = "black", family = "Times New Roman", size =18,face = "bold"),
legend.title=element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"))
#ggsave(paste0(path,"S_replicate_density.png"), width=8, height=5)
```
<img src="Figures/S_replicate_density.png" width="100%" />
### S3B heatmaps
Made these files in the protocol RMD.
```{r}
rep1 <- readRDS("/aryeelab/users/corri/data/k562_ctcf/K562_CTCF_DTG_1_heatmap_sort_readcounts.RDS")
rep1$sample <- "rep1"
rep2 <- readRDS("/aryeelab/users/corri/data/k562_ctcf/K562_CTCF_DTG_2_heatmap_sort_readcounts.RDS")
rep2$sample <- "rep2"
rep3 <- readRDS("/aryeelab/users/corri/data/k562_ctcf/K562_CTCF_ET_1_heatmap_sort_readcounts.RDS")
rep3$sample <- "rep3"
rep4 <- readRDS("/aryeelab/users/corri/data/k562_ctcf/K562_CTCF_ET_2_heatmap_sort_readcounts.RDS")
rep4$sample <- "rep4"
results_anc1 <- rbind(rep1,rep2,rep3,rep4)
```
```{r,fig.width=16,fig.height=8}
#results_anc1 <- readRDS(file = paste0(path,"heatmap_sort_readcounts.RDS"))
results_anc1$log_min_max_thresh <- results_anc1$log_min_max
results_anc1$log_min_max_thresh[results_anc1$log_min_max_thresh > 3] <- 3
results_anc1$log_min_max_thresh[results_anc1$log_min_max_thresh < -3] <- -3
results_anc1 %>%
ggplot(aes(dist, order, fill=log_min_max_thresh )) +
geom_tile() +
ylab("log2 (min/max)") +
xlab("Distance from CTCF Motif")+
labs(fill = "log2 (min/max)")+
facet_wrap(~sample)+
theme_classic()+
theme(panel.grid.major = element_line(color = "#f0f0f0",
size = 0.5))+
theme(plot.title = element_text(color = "black", family = "Times New Roman", size = 18, face = "bold"),
axis.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.ticks.x=element_blank(),
legend.text=element_text(color = "black", family = "Times New Roman", size =18,face = "bold"),
legend.title=element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"))+
# scico::scale_fill_scico(palette = "vik")+
scale_fill_gradient2(
low = "darkblue",
mid = "white",
high = "darkred")+
geom_vline(xintercept = c(0))+
scale_x_continuous(breaks=seq(-100,100,20))
#ggsave("/aryeelab/users/corri/plots/3A_min_max_replicate_heatmap.png", width=16, height=8)
```
<img src="Figures/3A_min_max_replicate_heatmap.png" width="100%" />
### S3C precision recall curves
```{r}
loops <-read_tsv("/aryeelab/users/corri/data/K562_CTCF_2.5kb.interactions_FitHiC_Q0.01.bed")
loops <- loops %>%
filter(chr1 != "chrX") %>%
filter(chr2 != "chrX")
LA <- loops %>%
dplyr::select(chr1, s1, e1) %>%
dplyr::rename(chr = chr1, start = s1, end = e1)
RA <- loops %>%
dplyr::select(chr2, s2, e2) %>%
dplyr::rename(chr = chr2, start = s2, end = e2)
anchors <- rbind(LA,RA) %>%
mutate(loc = paste0(chr, ":",start)) %>%
distinct(loc, .keep_all = TRUE)
anchors_gr <- makeGRangesFromDataFrame(anchors,
keep.extra.columns = TRUE,
seqnames.field = "chr",
start.field = "start",
end.field = "end")
ctcf_motifs<- readRDS("/aryeelab/users/corri/data/ALL_FIMO_CTCF_hg38.RDS")
gr_motifs <- ctcf_motifs[width(ctcf_motifs)==19]
gr_motifs$motif_mid <- round( ( start(gr_motifs) + end(gr_motifs) )/2)
ovl <- findOverlaps(gr_motifs, anchors_gr, maxgap = 0)
out <- as.data.frame(gr_motifs[queryHits(ovl)]) %>%
cbind(motif_id = queryHits(ovl),
loop_id = subjectHits(ovl),
loop_start = start(anchors_gr)[subjectHits(ovl)],
loop_end = end(anchors_gr)[subjectHits(ovl)])
out %>%
group_by(loop_id) %>%
dplyr::summarize(count =dplyr::n()) %>%
filter(count < 10) %>%
ggplot(aes(x = count))+
geom_histogram(fill = "darkblue", col = "white")+
theme_classic()+
theme(panel.grid.major = element_line(color = "#f0f0f0",
size = 0.5))+
theme(plot.title = element_text(color = "black", family = "Times New Roman", size = 18, face = "bold"),
axis.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.text.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.title.y = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
axis.ticks.x=element_blank(),
legend.text=element_text(color = "black", family = "Times New Roman", size =18,face = "bold"),
legend.title=element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.text.x = element_text(color = "black", family = "Times New Roman", size = 18,face = "bold"),
strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"))+
xlab("Number of CTCF motifs per 2.5kb loop anchor")+
ylab("Count") +
scale_x_continuous(breaks=seq(1:10))
```
```{r}
temp<- out %>%
group_by(loop_id) %>%
dplyr::summarize(count =dplyr::n())
# 70% have more than one CTCF motif
temp %>%
filter(count >1) %>%
dplyr::summarize(RN = max(row_number())) %>%
mutate(freq = RN / nrow(temp))
```
*filter to loop anchors with only one CTCF motif*
```{r}
uniq<- out %>%
dplyr::group_by(loop_id) %>%
dplyr::mutate(count = max(dplyr::row_number())) %>%
ungroup() %>%
filter(count == 1) %>%
arrange(loop_id)
uniq_gr <- makeGRangesFromDataFrame(uniq,
keep.extra.columns = FALSE,
seqnames.field = "seqnames",
start.field = "start",
end.field = "end")
uniq_gr$motif_mid <- uniq$motif_mid
uniq_gr$loop_start <- uniq$loop_start
uniq_gr$loop_end <- uniq$loop_end
chip <- read_tsv("/aryeelab/users/corri/data/K562_CTCF_peaks_ENCFF736NYC.bed", col_names = FALSE)
colnames(chip) <- c("chr", "start", "end", "name", "score", "strand", "signalValue", "pval", "qval", "peak")
chip_gr <- makeGRangesFromDataFrame(chip)
chip_gr$qval <- chip$qval
chip_gr$peak_mid <- (start(chip_gr) + end(chip_gr))/2
anchors <- chip_gr %>%
plyranges::anchor_center() %>%
plyranges::mutate(width = 301)
ovl <- findOverlaps(uniq_gr, anchors, maxgap = 0)
out <- as.data.frame(uniq_gr[queryHits(ovl)]) %>%
cbind(motif_id = queryHits(ovl),
chip_id = subjectHits(ovl),
chip_start = start(anchors)[subjectHits(ovl)],
chip_mid = anchors$peak_mid[subjectHits(ovl)],
chip_end = end(anchors)[subjectHits(ovl)],
qval_score = anchors$qval[subjectHits(ovl)]) %>%
mutate(dist = chip_mid - motif_mid)
out <- out %>%
ungroup() %>%
group_by(chip_id) %>%
arrange(abs(dist)) %>%
dplyr::slice(1)
out <- out %>%
filter(abs(dist) < 30)
# going to use this dataset for precision-recall curve later
ctcf_chip <- out %>%
ungroup()
```
```{r}
PR_function <- function(path, ctcf_chip){
df <- readRDS(file =paste0(path, "GW_pvals.RDS"))
peaks_gr <- makeGRangesFromDataFrame(df,
seqnames.field = "chr",
start.field = "window_mid",
end.field = "window_mid",
keep.extra.columns = TRUE)
peaks_gr$FF_pval <- df$pvalue
peaks_gr$window_mid <- df$window_mid
#####################################
chip_motif <- makeGRangesFromDataFrame(ctcf_chip,
seqnames.field = "seqnames",
start.field = "motif_mid",
end.field = "motif_mid")
ovl <- findOverlaps(chip_motif, peaks_gr, maxgap = 40)
TP <- as.data.frame(chip_motif[queryHits(ovl)]) %>%
cbind(motif_id = queryHits(ovl),
peak_id = subjectHits(ovl),
peak = start(peaks_gr)[subjectHits(ovl)],
FF_pval = peaks_gr$FF_pval[subjectHits(ovl)],
q1 = peaks_gr$q1[subjectHits(ovl)],
q2 = peaks_gr$q2[subjectHits(ovl)],
q3 = peaks_gr$q3[subjectHits(ovl)],
q4 = peaks_gr$q4[subjectHits(ovl)],
num_reads = peaks_gr$num_reads[subjectHits(ovl)],
min24 = peaks_gr$min24[subjectHits(ovl)],
max13 = peaks_gr$max13[subjectHits(ovl)],
log_min_max = peaks_gr$log_min_max[subjectHits(ovl)]) %>%
mutate(dist = start-peak) %>%
group_by(motif_id) %>%
filter(abs(dist) < 30) %>%
arrange(desc(log_min_max)) %>%
dplyr::slice(1) %>%
ungroup()
#####################################
out <- ctcf_chip
out <- out %>%
mutate(s1 = loop_start+20,
e1 = motif_mid - 200,
s2 = motif_mid + 200,
e2 = loop_end-20)
check <- out %>%
filter(s1<e1)
chip_motif1 <- makeGRangesFromDataFrame(check,
seqnames.field = "seqnames",
start.field = "s1",
end.field = "e1")
check <- out %>%
filter(s2<e2)
chip_motif2 <- makeGRangesFromDataFrame(check,
seqnames.field = "seqnames",
start.field = "s2",
end.field = "e2")
chip_motif<- c(chip_motif1, chip_motif2)
ovl <- findOverlaps(chip_motif, peaks_gr, maxgap = 0)
FP <- as.data.frame(chip_motif[queryHits(ovl)]) %>%
cbind(loop_id = queryHits(ovl),
peak_id = subjectHits(ovl),
peak = start(peaks_gr)[subjectHits(ovl)],
FF_pval = peaks_gr$FF_pval[subjectHits(ovl)],
q1 = peaks_gr$q1[subjectHits(ovl)],
q2 = peaks_gr$q2[subjectHits(ovl)],
q3 = peaks_gr$q3[subjectHits(ovl)],
q4 = peaks_gr$q4[subjectHits(ovl)],
num_reads = peaks_gr$num_reads[subjectHits(ovl)],
min24 = peaks_gr$min24[subjectHits(ovl)],
max13 = peaks_gr$max13[subjectHits(ovl)],
log_min_max = peaks_gr$log_min_max[subjectHits(ovl)])
#####################################
FP$group <- floor(FP$peak/150)
FP <- FP %>%
mutate(group = paste0(seqnames, ":", group))
num_TP<- 4523 # number of TP motifs
thresh_vec <- seq(1,10, by = 1)
PR <- data.frame(thresh= thresh_vec,
lik_precision = rep(NA, length(thresh_vec)),
lik_recall = rep(NA, length(thresh_vec)))
iter <- 1
for (score_val in thresh_vec){
print(score_val)
thresh <- 10^(-1 * score_val)
### False positives
num_FP_lik <- FP %>%
filter(FF_pval<= thresh) %>%
group_by(group) %>%
dplyr::slice(1) %>%
ungroup() %>%
dplyr::summarize(FP = max(dplyr::row_number())) %>%
pull(FP)
if(num_FP_lik == "-Inf"){
num_FP_lik <- 0
}
### True positives
lik_TP <- TP %>%
filter(FF_pval <= thresh) %>%
dplyr::summarize(truepos = max(dplyr::row_number())) %>%
pull(truepos)
# recall
PR$lik_recall[iter] <- lik_recall <- lik_TP /num_TP
# precision
PR$lik_precision[iter] <- lik_prec <- lik_TP / (lik_TP + num_FP_lik)
iter <- iter + 1
}
#####################################
return(PR)
}
```
```{r}
combined_reps <- PR_function(path = "/aryeelab/users/corri/data/replicate_FF_results/",
ctcf_chip = ctcf_chip)
```
*Great, this exactly matches our figure in our paper. Now let's make it for each replicate.*
```{r,fig.width=8,fig.height=4}
library(ggrepel)
combined_reps %>%