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3.2_plot_INSIGHT.Rmd
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---
title: "Sequence conservation associated with regulatory pleiotropy"
author: "Zane Kliesmete"
date: "03/11/2020"
output: html_document
#editor_options:
# chunk_output_type: console
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
libs<-c("RMySQL","tidyverse","cowplot","data.table","GenomicRanges","patchwork", "ggpubr")
sapply(libs, require, character.only=T)
source("helper_functions.R")
tissueColors <- c( "#9E0142" ,"#D53E4F", "#F46D43", "#FDAE61", "#FFD92F" ,"#ABDDA4", "#66C2A5", "#3288BD", "#5E4FA2")
names(tissueColors) <- c("adrenal gland", "brain", "heart", "kidney", "large intestine", "lung", "muscle", "stomach", "thymus")
specificityColors <- c( "#A3753B", "#CC9B57", "#E7CF97", "#F8EDD0", "#F7F7F7", "#D2EEEA", "#99D7CE", "#5DACA5", "#33847E")
names(specificityColors) <- c(1:9)
regionColors <- c("#9CA578","#3288BD")
names(regionColors) <- c("Enhancer", "Promoter")
DA_colors<-c("#AEAEC2","#606080")
names(DA_colors)<-c("Not conserved", "Conserved")
basic_theme<- theme(axis.title=element_text(size=8.5),
axis.text = element_text(color="black", size=7),
#legend.title = element_text(size = 8),
legend.text = element_text(size = 6.5),
legend.key.size = unit(0.8, "lines"),
legend.margin=margin(0.3,0.3,0.3,0.3),
legend.box.margin=margin(0.2,0.2,0.2,0.2))
basic_theme_ins<- theme(axis.title=element_text(size=9),
axis.text = element_text(color="black", size=8),
legend.title = element_text(size = 7.5),
legend.text = element_text(size = 7.4),
legend.key.size = unit(0.6, "lines"),
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(0,0,0,0),
legend.background = element_blank(),
legend.box.background = element_blank(),
panel.grid.major = element_line(size = 0.35),
panel.grid.minor = element_blank())
```
## Getting and combining region information from the SQL database
```{r sql}
myDriver=dbDriver("MySQL")
con=dbConnect(myDriver, host="chimp.anthro.bzm",username="zane",password="zanezane", dbname="peters_stuff")
#dbListTables(con)
jamm_region_identifiers<-readRDS("../roadmap_DHS_summaries/region_summary/jamm_region_identifiers.rds")
jamm_DNase_binary<-readRDS("../roadmap_DHS_summaries/region_summary/jamm_DNase_binary.rds")
jamm_region_info_full<-readRDS("../roadmap_DHS_summaries/region_summary/jamm_region_info_full.rds")
jamm_peaks<-readRDS("../roadmap_DHS_summaries/region_summary/jamm_peaks.rds")
# identify region_ids that overlap with CDS
OL_CDS<-filter_by_overlaps(as_granges(jamm_region_info_full %>%
dplyr::rename(seqnames=chromosome)),
read_gff("../gtf/gencode.v19.annotation.gtf.gz") %>%
filter(type=="CDS"))
```
### Per region
```{r plotEnhIns}
results<-get_insight("../INSIGHT/per_region/")
#add number of regions
results$no_of_regions<-0
for (reg in c("enh","prom")){
for (i in c(1:9)){
regions<-read.table(paste0("../INSIGHT/per_region/",reg,"/sp",i,".bed"), header=F)
results$no_of_regions[results$dataID==paste0("sp",i,"_",reg)]<-dim(regions)[1]
}}
insight_region_long<-results %>%
filter(!grepl(".downs", dataID)) %>%
tidyr::separate(dataID,c("sp","region"), sep="_", extra="merge") %>%
pivot_longer_insight()
```
### Per tissue
```{r plotInsTissue}
results<-get_insight("../INSIGHT/all_per_tissue/")
insight_tissues_long<-results %>%
filter(!grepl(".downs", dataID)) %>%
tidyr::separate(dataID,c("sp","tissue"), sep="_", extra="merge") %>%
pivot_longer_insight()
combined<-bind_rows(insight_region_long,insight_tissues_long) %>%
filter( selection_type != 'E.A.') %>%
mutate(longLab = case_when( selection_type == 'E.W.' ~ "Weak negative selection (E.W.):",
selection_type == 'rho' ~ "All~negative~selection~symbol('(')*rho*symbol(')')*symbol(':')"),
region = ifelse( region == "enh", "Enhancer", "Promoter"),
tissue = gsub("_", " ", tissue))
```
### Brain and thymus peaks
```{r plotPeaks}
insight_peaks_long1<-get_insight("../INSIGHT/PeaksAntiPeaks/") %>%
tidyr::separate(dataID,c("sp","tissue", "peak"), sep="_") %>%
mutate(peak=ifelse(peak=="peaks","peak","adjacent")) %>%
pivot_longer_insight()
insight_peaks_long<-bind_rows(insight_tissues_long %>%
filter(sp=="sp1", tissue %in% c("brain","thymus")) %>%
mutate(peak="peak"), insight_peaks_long1) %>%
filter( selection_type != 'E.A.') %>%
mutate(type=paste0(tissue,"_",peak),
longLab = case_when( selection_type == 'E.W.' ~ "Weak negative selection (E.W.):",
selection_type == 'rho' ~ "All~negative~selection~symbol('(')*rho*symbol(')')*symbol(':')"))
```
## Plot main figures
# First main
```{r first}
basic_theme_ins2<- theme(axis.title=element_text(size=9),
axis.text = element_text(color="black", size=8),
legend.title = element_text(size = 7.5),
legend.text = element_text(size = 7.4),
legend.key.size = unit(0.6, "lines"),
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(0,0,0,0),
legend.background = element_blank(),
legend.box.background = element_blank(),
panel.grid.major = element_line(size = 0.35),
panel.grid.minor = element_blank(),
strip.text = element_text(size = 8))
DA_colors<-c("#AEAEC2","#606080")
names(DA_colors)<-c("Not conserved", "Conserved")
results<-get_insight("../INSIGHT/per_NPC_DA_stringent/")
#add number of regions
results$no_of_regions<-0
for (reg in c("DA","nonDA")){
for (i in c(1:9)){
regions<-read.table(paste0("../INSIGHT/per_NPC_DA/",reg,"/sp",i,".bed"), header=F)
results$no_of_regions[results$dataID==paste0("sp",i,"_",reg)]<-dim(regions)[1]
}}
insight_DA1_long<-results %>%
tidyr::separate(dataID,c("sp","DA"), sep="_", extra="merge") %>%
pivot_longer_insight() %>%
dplyr::filter( selection_type != 'E.A.') %>%
dplyr::mutate(longLab = case_when( selection_type == 'E.W.' ~ "Cross-species accessibility",
selection_type == 'rho' ~ "Cross-species accessibility"),
DA= ifelse(DA=="DA","Not conserved", "Conserved"))
pReg1_DA<-ggplot(insight_DA1_long %>% filter(selection_type=="E.W."),
aes(x=as.numeric(Tissues), y=selection_coeff, group=DA, color=DA))+
geom_smooth(aes(group=DA, color=DA), linewidth=0.5, alpha=0.07, method="lm")+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=DA),
stat = "identity",position=position_dodge(width=.1),width=0.1, size=0.4)+
geom_point(aes(color=DA),
stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=DA_colors)+
theme_bw()+
basic_theme_ins2+
xlab("")+
labs(color="")+
scale_x_continuous(breaks=c(1:9))+
labs(color="")+
ylim(c(0.15,1.25))+
ylab('Sites per kb')+
theme(legend.title=element_blank(),
legend.position=c(0.3,0.9),
plot.margin = unit(c(0.15,0.15,0,0.15),"cm"),
legend.box = "horizontal",
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank())
pReg2_DA<-ggplot(insight_DA1_long %>% filter(selection_type=="rho"),
aes(x=as.numeric(Tissues), y=selection_coeff, group=DA, color=DA))+
geom_smooth(aes(group=DA, color=DA), size=0.5, alpha=0.07)+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=DA),
stat = "identity",position=position_dodge(width=.1),width=0.1, size=0.4)+
geom_point(aes(color=DA),
stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=DA_colors)+
scale_size_continuous(range = c(1,2), breaks = c(2.5,7.5,12.5))+
theme_bw()+
basic_theme_ins2+
xlab("PD")+
scale_x_continuous(breaks=c(1:9))+
ylim(c(0.0,0.35))+
ylab('Fraction of sites')+
theme(legend.position="none",
legend.box = "horizontal",
plot.margin = unit(c(0,0.15,0.15,0.15),"cm"),
legend.title = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
pReg1_DA/pReg2_DA
pReg1<-ggplot(combined %>% filter(selection_type=="E.W.", !is.na(region)),
aes(x=as.numeric(Tissues), y=selection_coeff, group=region, color=region))+
geom_smooth(aes(group=region, color=region), linewidth=0.5, alpha=0.07, method="lm")+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=region),
stat = "identity",position=position_dodge(width=.1),width=0.1, size=0.4)+
geom_point(aes(color=region),
stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=c("#9CA578","#3288BD"))+
theme_bw()+
basic_theme_ins2+
xlab("")+
labs(color="")+
scale_x_continuous(breaks=c(1:9))+
labs(color="")+
ylim(c(0.15,1.25))+
ylab('E.W. (sites per kb)')+
theme(legend.title=element_blank(),
legend.position = c(0.23, 0.9),
plot.margin = unit(c(0.15,0.05,0,0),"cm"),
legend.box = "horizontal",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
pReg2<-ggplot(combined %>% filter(selection_type=="rho", !is.na(region)),
aes(x=as.numeric(Tissues), y=selection_coeff, group=region, color=region))+
geom_smooth(aes(group=region, color=region), size=0.5, alpha=0.07)+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=region),
stat = "identity",position=position_dodge(width=.1),width=0.1, size=0.4)+
geom_point(aes(color=region), #size=no_of_regions),
stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=c("#9CA578","#3288BD"))+
theme_bw()+
basic_theme_ins2+
xlab("PD")+
scale_x_continuous(breaks=c(1:9))+
ylim(c(0.0,0.35))+
ylab(expression(paste(rho~' (fraction of sites)')))+
theme(legend.position="none",
plot.margin = unit(c(0,0.05,0.15,0),"cm"))
p_seq1<-(pReg1|pReg1_DA)/(pReg2|pReg2_DA)+plot_annotation(tag_levels = 'A') & theme(plot.tag=element_text(size=12,face = "bold"))
ggsave("/data/share/htp/pleiotropy/paper_data/figures/fig3_part1.pdf",p_seq1, height=183*0.6, width=162.5*0.7, units="mm")
```
## Second main
```{r secondmain}
# Second separate figure
pTis1<-ggplot(combined %>% filter(selection_type=="E.W.", !is.na(tissue))) +
geom_line( data = filter(combined, ! (tissue %in% c("brain")) & selection_type == 'E.W.' & !is.na(tissue)) ,
aes( x = as.numeric(Tissues), y=selection_coeff , group=tissue , color=tissue), size=0.4, alpha=0.9) +
geom_line( data = filter(combined, (tissue %in% c("brain")) & selection_type == 'E.W.' & !is.na(tissue)) ,
aes( x = as.numeric(Tissues), y=selection_coeff , group=tissue , color=tissue), size=0.8, alpha=0.9) +
scale_color_manual(values=tissueColors)+
theme_bw()+
basic_theme_ins2+
scale_x_continuous(breaks=c(1:9))+
ylim(c(0.15,1.25))+
ylab('Sites per kb')+
theme(plot.margin = unit(c(0.15,0.02,0,0),"cm"),
legend.position="none")+
ylab('E.W. (sites per kb)')+
xlab("PD")
pTis2<-ggplot(combined %>% filter(selection_type=="rho", !is.na(tissue))) +
geom_line( data = filter(combined, ! (tissue %in% c("brain")) & selection_type == 'rho' & !is.na(tissue)) ,
aes( x = as.numeric(Tissues), y=selection_coeff , group=tissue , color=tissue), size=0.4, alpha=0.9) +
geom_line( data = filter(combined, (tissue %in% c("brain")) & selection_type == 'rho' & !is.na(tissue)) ,
aes( x = as.numeric(Tissues), y=selection_coeff , group=tissue , color=tissue), size=0.8, alpha=0.9) +
scale_color_manual(values=tissueColors)+
theme_bw()+
basic_theme_ins2+
xlab("PD")+
scale_x_continuous(breaks=c(1:9))+
ylim(c(0.0,0.35))+
ylab('Fraction of sites')+
theme(plot.margin = unit(c(0,0.02,0.15,0),"cm"),
legend.title = element_blank())+
ylab(expression(paste(rho~" (fraction of sites)")))
# add the peak - antipeak stuff
df<- tibble( s1= 500+rnorm(1000, 300, 100) ,
s2= 700+rnorm(1000, 500, 300),
s3= 200+rnorm(1000, 200, 200)) %>%
tidyr::gather(key,val,s1,s2,s3)
peak<-ggplot(df,aes(x=val,fill=key))+
geom_density(alpha=0.5,lwd=0.4) +
ylim(c(-0.0015,0.0045))+
scale_fill_manual(values=c("red","grey","grey"))+
annotate("segment", x = 1100, xend = 1800, y = -0.0007, yend = -0.0007, colour = "grey", size=0.75, alpha=0.6, arrow=arrow(ends="both",length = unit(0.13, "cm")))+
annotate("segment", x = 0, xend = 500, y = -0.0007, yend = -0.0007, colour = "grey", size=0.75, arrow=arrow(ends="both",length = unit(0.13, "cm")))+
annotate("segment", x = 500, xend = 1100, y = -0.0007, yend = -0.0007, colour = "red", size=0.75, arrow=arrow(ends="both",length = unit(0.13, "cm")))+
annotate("segment", x = 0, xend = 1800, y = -0.0015, yend = -0.0015, colour = "darkgrey", size=0.75, arrow=arrow(ends="both",length = unit(0.13, "cm")))+
annotate("text", x = 250, y = -0.00025, label = "adjacent", size=2.4) +
annotate("text", x = 1400, y = -0.00025, label = "adjacent", size=2.4) +
annotate("text", x = 800, y = -0.00025, label = "peak",col="red", size=2.4)+ theme_void()+
annotate("text", x = 800, y = -0.0011, label = "CRE", size=3)+ theme_void()+
theme(legend.position = "None",
text=element_text(family="Helvetica"),
plot.margin = unit(c(0,.05,0,0),"cm"),
panel.border = element_rect(colour = "black", fill=NA))
antiRho<-ggplot(insight_peaks_long %>% filter(selection_type=="rho", tissue=="brain"),
aes(x=as.numeric(Tissues),
y=selection_coeff,
group=type,
alpha=peak, shape=peak,
ymin=selection_coeff - stderr,
ymax=selection_coeff + stderr))+
geom_line(show_guide=FALSE, color=tissueColors[["brain"]])+
geom_errorbar(width=0.15, size=0.1, show.legend=FALSE, color=tissueColors[["brain"]])+
geom_point(size=2, color=tissueColors[["brain"]])+
theme_bw()+
basic_theme_ins2+
xlab("PD")+
scale_y_continuous()+
scale_alpha_manual(values= c(0.35,1))+
scale_x_continuous(breaks=c(1:9))+
theme(legend.title = element_blank(),
plot.margin=unit(c(0.2,0,0.05,0.02),"cm"),
legend.position = c(0.25, 0.25))+
ylab(expression(paste(rho~" (fraction of sites)")))
p_seq2<-(pTis1|pTis2)/(peak|antiRho)+plot_annotation(tag_levels = 'A') & theme(plot.tag=element_text(size=12,face = "bold"))
ggsave("/data/share/htp/pleiotropy/paper_data/figures/fig3_part2.pdf",p_seq2, height=183*0.6, width=162.5*0.85, units="mm")
```
# Supplementary figure
## CGI vs no CGI
```{r plotCGI}
results<-get_insight("../INSIGHT/CGI/")
target_dir="../INSIGHT/CGI/"
results$no_of_regions<-0
for (CGI in c("CGI", "noCGI")){
for (i in c(1:9)){
regions<-read.table(paste0(target_dir,CGI,"/sp",i,".bed"), header=F)
results$no_of_regions[results$dataID==paste0("sp",i,"_",CGI)]<-dim(regions)[1]
}}
sp_all_insight<-results %>%
filter(!grepl(".downs", dataID)) %>%
tidyr::separate(dataID,c("sp","CGI"), sep="_")
insight_CGI_long<-pivot_longer_insight(sp_all_insight) %>%
filter( selection_type != 'E.A.') %>%
mutate(longLab = case_when(selection_type == 'E.W.' ~ "Weak negative selection",
selection_type == 'rho' ~ "All negative selection"))
pReg1_CGI<-ggplot(insight_CGI_long %>% filter(selection_type=="E.W."),
aes(x=as.numeric(Tissues), y=selection_coeff, group=CGI, color=CGI))+
geom_smooth(aes(group=CGI, color=CGI), size=0.5, alpha=0.07)+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=CGI),
stat = "identity",position=position_dodge(width=.1),width=0.1, size=0.4) +
geom_point(aes(color=CGI),stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=c("#D1462F", "#D9AE61")) +
#facet_grid(.~longLab)+
theme_bw()+
basic_theme_ins+
scale_x_continuous(breaks=c(1:9))+labs(color="")+
ylab('E.W. (sites per kb)')+
xlab("PD")+
theme(legend.position = "none",
plot.margin = unit(c(0.2, 0.2, 0.2 ,0.2),"cm"))
pReg2_CGI<-ggplot(insight_CGI_long %>% filter(selection_type=="rho"),
aes(x=as.numeric(Tissues), y=selection_coeff, group=CGI, color=CGI))+
geom_smooth(aes(group=CGI, color=CGI), size=0.5, alpha=0.07)+
geom_errorbar(aes(ymax=selection_coeff+stderr, ymin=selection_coeff-stderr, color=CGI),
stat = "identity",position=position_dodge(width=.1), width=0.1, size=0.4) +
geom_point(aes(color=CGI),stat = "identity",position=position_dodge(width=.1))+
scale_color_manual(values=c("#D1462F", "#D9AE61"))+
#facet_grid(.~longLab)+
theme_bw()+
basic_theme_ins+
xlab("")+
xlab("PD")+
scale_x_continuous(breaks=c(1:9))+
ylab(expression(paste(rho~' (fraction of sites)')))+
theme(legend.position=c(0.2, 0.2),
legend.spacing.y = unit(0.05, "cm"),
legend.spacing.x = unit(0.05, "cm"),
legend.title = element_blank(),
legend.box = "horizontal",
plot.margin = unit(c(0.2, 0.2, 0.2, 0.2),"cm"))
```
## Phastcons and phyloP
```{r phastc}
jamm_bed<-jamm_region_info_full %>%
dplyr::rename(seqnames=chromosome) %>%
#filter(!region_id %in% OL_CDS$region_id) %>%
dplyr::select(seqnames, start, end, region_id, total, CGI) %>%
as_granges()
DHS_annot<-readRDS("../CRE_to_Gene/DHS_to_gene.rds") %>%
distinct(region_id, .keep_all = T)
# Phastcons
phastCons_DHS<-analysePhastCons(bigWigFile = "../bw/primates.phastCons46way.bw", gr=jamm_bed)
saveRDS(phastCons_DHS, "../RDS/phastCons_DHS.rds")
phastCons_DHS<-readRDS("../RDS/phastCons_DHS.rds")
p_phastCons_point<-phastCons_DHS %>%
inner_join(DHS_annot %>% dplyr::select(region_id, assignment)) %>%
mutate(assignment = ifelse(assignment=="enh","Enhancer","Promoter")) %>%
group_by(total, assignment) %>%
summarise(mean=mean(meanPhastCons),
SE=sd(meanPhastCons)/sqrt(length(total)),
n=length(total)) %>%
ggplot(aes(x=as.factor(total), y=mean, color = assignment, group=assignment)) +
geom_smooth(size=0.5, alpha=0.05) + #, color="grey")+
geom_errorbar(aes(ymin=mean-SE, ymax=mean+SE),stat = "identity",width=0.2, size=0.4) +
geom_point() +
xlab("PD") +
ylab("Average PhastCons") +
theme_bw() +
basic_theme_ins +
scale_color_manual(values=regionColors) +
theme(legend.position = "none",
legend.title = element_blank()) +
scale_size_continuous(range =c(2.5,4.5))
# phyloP
phyloP_DHS <- analysePhyloP(bigWigFile = "../bw/primates.phyloP46way.bw", gr=jamm_bed)
saveRDS(phyloP_DHS, "../RDS/phyloP_DHS.rds")
phyloP_DHS<-readRDS("../RDS/phyloP_DHS.rds")
p_phyloP_point<-phyloP_DHS %>%
inner_join(DHS_annot %>% dplyr::select(region_id, assignment)) %>%
mutate(combi=paste0(assignment,"_",total),
assignment = ifelse(assignment=="enh","Enhancer","Promoter")) %>%
group_by(total, assignment, combi) %>%
summarise(mean=mean(meanPhyloP),
SE=sd(meanPhyloP)/sqrt(length(total)),
n=length(total)) %>%
ggplot(aes(x=as.factor(total), y=mean, color = assignment, group=assignment)) +
#geom_line()+
geom_smooth(size=0.5, alpha=0.05) + #, color="grey")+
geom_errorbar(aes(ymin=mean-SE, ymax=mean+SE),stat = "identity",width=0.2, size=0.4) +
geom_point() +
xlab("PD") +
ylab("Average phyloP") +
theme_bw() +
basic_theme_ins +
scale_color_manual(values=regionColors) +
theme(legend.position = c(0.25,0.2),
legend.title = element_blank())
```
## CpG and GC content conservation
```{r cpgcons}
DHS_annot<-readRDS("../CRE_to_Gene/DHS_to_gene.rds") %>%
distinct(region_id, .keep_all = T)
# keep only similar length and N-free sequences
jamm_inATAC<-readRDS("../ATACseq/RDS/jamm_inATAC.rds") %>%
filter(similar_width=="yes", Ns_hg38==0, Ns_macFas6==0) %>%
dplyr::mutate(GC_cons = 1-abs(GC_content.mac - GC_content.hg),
CpG_obs_exp_cons = 1-abs(CpG_obs_exp.mac - CpG_obs_exp.hg)) %>%
inner_join(DHS_annot %>% dplyr::mutate(region_id = as.factor(as.character(region_id))))
p_cpg <- ggline(jamm_inATAC %>% mutate(label="CpG:"),
x = "total", y = "CpG_obs_exp_cons",
color = "assignment", palette = c("#9CA578","#3288BD"),
add = "mean_se", point_size = 0.00001,
plot_type = "l", size = 0.7) +
theme_bw() +
basic_theme_ins2 +
xlab("PD") +
#facet_grid(.~label)+
theme(legend.title = element_blank(), legend.position = "none") + #c(0.2, 0.8))+
ylab("CpG exp/obs conservation") +
geom_smooth(color = "transparent", alpha = 0.1, size = 0.5)+
scale_y_continuous(breaks=scales::pretty_breaks(n=4))
p_gc <- ggline(jamm_inATAC %>% mutate(label = "GC:"),
x = "total", y = "GC_cons",
color = "assignment", palette = c("#9CA578","#3288BD"),
add = "mean_se", point_size = 0.00001,
plot_type = "l", size = 0.7) +
theme_bw() +
basic_theme_ins2 +
xlab("PD") +
#facet_grid(.~label)+
theme(legend.title = element_blank(), legend.position = "none") +
ylab("GC conservation") +
geom_smooth(color = "transparent", alpha = 0.1, size = 0.5)+
scale_y_continuous(breaks=scales::pretty_breaks(n=4))
p_cpg|p_gc
```
## Put the figure together
```{r suppl3}
psuppl<-(p_phastCons_point|p_phyloP_point)/(pReg1_CGI|pReg2_CGI)/(p_cpg|p_gc)+plot_annotation(tag_levels = 'A') & theme(plot.tag=element_text(size=12,face = "bold"))
ggsave("/data/share/htp/pleiotropy/paper_data/figures/fig3_suppl.pdf",psuppl, height=183*0.9, width=162.5*0.78, units="mm")
```