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RepOri_revision_VOCHiC_may22.Rmd
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##Code for VOCHiC
# library(devtools)
# devtools::install_bitbucket("eraineri/chaser", build_opts=c(), force=T)
```{r}
library(chaser)
library(GenomicRanges)
source('/media/data/home/vera/RepOriData/coloursdef.R')
datapath='/media/zeus/Verbatim HDD/RepOriKarolina2021/RepOriData'
#load(paste0(datapath,'/chaser_reproducing_ori_paper.RData'))
```
read BonevORInet
WT
```{r}
################# WT
wtscores=get(load(file=paste0(datapath,'/cav/wt_scores_25kb.df')))
colnames(wtscores)[c(1:3)]=c('chr', 'start', 'end' )
sel=(which(wtscores$maxScore>35))
wtscoressel=wtscores[sel,]
wtnet=make_chromnet(wtscoressel[,c(1:6)])
path=paste0(datapath,'/cav/')
orifiles=list.files(paste0(datapath, '/cav/'))
wtorinets=as.list(paste0(path,orifiles[grep('^wt_scores', orifiles)]))
names(wtorinets)=orifiles[grep('^wt_scores', orifiles)]
```
Make VOCHiC net WT
```{r}
###WT scores>0
wtorinetsprocl=lapply(wtorinets, function(x){
x=get(load(x))
print(head(x))
sel=which(x$maxScore>0)
return(wtscores[sel,1:6])
})
```
### WT scores>25
```{r}
wtorinetsprocl25=lapply(wtorinets, function(x){
x=get(load(x))
# print(head(x))
sel=which(x$maxScore>25)
return(x[sel,1:6])
})
win=c(10000, 25000, 5000)
wtorinetsprocl25ext=wtorinetsprocl25
for (w in c(1:3)){
wtorinetsprocl25ext[[w]]$start1=sapply(wtorinetsprocl25ext[[w]]$start1, function(x){return(max((x-win[w]),0))})
wtorinetsprocl25ext[[w]]$start2=sapply(wtorinetsprocl25ext[[w]]$start2, function(x){return(max((x-win[w]),0))})
wtorinetsprocl25ext[[w]]$end1=sapply(wtorinetsprocl25ext[[w]]$end1, function(x){return((x+win[w]))})
wtorinetsprocl25ext[[w]]$end2=sapply(wtorinetsprocl25ext[[w]]$end2, function(x){return((x+win[w]))})
}
wtnetsl25=lapply(wtorinetsprocl25ext, make_chromnet)
```
```{r}
wtorinetsprocscorel=lapply(wtorinets, function(x){
x=get(load(x))
print(head(x))
sel=which(x$maxScore>0)
return(x$maxScore[sel])
})
wtorinetsprocl=lapply(wtorinets, function(x){
x=get(load(x))
# print(head(x))
sel=which(x$maxScore>0)
return(x[sel,1:6])
})
win=c(10000, 25000, 5000)
wtorinetsproclext=wtorinetsprocl
for (w in c(1:3)){
wtorinetsproclext[[w]]$start1=sapply(wtorinetsproclext[[w]]$start1, function(x){return(max((x-win[w]),0))})
wtorinetsproclext[[w]]$start2=sapply(wtorinetsproclext[[w]]$start2, function(x){return(max((x-win[w]),0))})
wtorinetsproclext[[w]]$end1=sapply(wtorinetsproclext[[w]]$end1, function(x){return((x+win[w]))})
wtorinetsproclext[[w]]$end2=sapply(wtorinetsproclext[[w]]$end2, function(x){return((x+win[w]))})
}
wtnetsl=lapply(wtorinetsproclext, make_chromnet)
#write.table(cbind(paste0(wtnetsl[[3]]$edgesdf[,7],' (interacts with) ' ,wtnetsl[[3]]$edgesdf[,8]) ,wtorinetsprocscorel[[3]]), '5kbfull_wtscores.txt', quote=F, sep='\t', col.names=F)
```
Read new ori efficiencies centred on original ori centers
```{r}
wtnetsl25origef=wtnetsl25
##For HiC OriNet need oris in mm10
pathf=paste0(datapath,'/OriHiCeffNov2020/2020_11_30_efficiency_windows_classic_oris/mm10/')
files=list.files(pathf)
filesbed=files[grep('.bed', files)]
orisC=list()
for (f in filesbed){
oriname=sub('.bed|mean_efficiencies_', '', f)
orisC[[oriname]] <- read.table(paste(pathf,f, sep='/'), header = T, stringsAsFactors = F)
colnames(orisC[[oriname]])[c(4,5,6,7)]=paste(oriname, colnames(orisC[[oriname]][c(4,5,6,7)]), sep='_')
}
##Now create combined sets:
orisCnames=lapply(orisC, function(x){
return(paste0(x[,1],':', x[,2], '-', x[,3]))
})
```
Add feature at eachresol.
```{r}
wtnetefl25=wtnetsl25
orisCsel=orisC[grep('10kb', names(orisC))]
for (s in names(orisCsel)){
print(s)
wtnetefl25[[ "wt_scores_10kb.df"]]=load_features(wtnetefl25[[ "wt_scores_10kb.df"]],orisCsel[[s]][,-c(4:6)],type='features_table',auxfun=mean,featname=gsub('10kb_mm10.bed_norm_background_corrected',s), missingv=0)
}
orisCsel=orisC[grep('25kb', names(orisC))]
for (s in names(orisCsel)){
wtnetefl25[[ "wt_scores_25kb.df"]]=load_features(wtnetefl25[[ "wt_scores_25kb.df"]],orisCsel[[s]][,-c(4:6)],type='features_table',auxfun=mean,featname=gsub('25kb_mm10.bed_norm_background_corrected',s), missingv=0)
}
orisCsel=orisC[grep('_5kb', names(orisC))]
for (s in names(orisCsel)){
wtnetefl25[[ "wt_scores_5kb.df"]]=load_features(wtnetefl25[[ "wt_scores_5kb.df"]],orisCsel[[s]][,-c(4:6)],type='features_table',auxfun=mean,featname=gsub('5kb_mm10.bed_norm_background_corrected',s), missingv=0)
}
orisef=lapply(orisCsel, function(x){
x=x[,-c(4:6)]
colnames(x)[4]='ef'
return(x)
})
alloris=Reduce(rbind,orisef)
colnames(alloris)=c('chr', 'start', 'end', 'All-Ori')
wtnetefl25[[ "wt_scores_5kb.df"]] <- load_features(wtnetefl25[[ "wt_scores_5kb.df"]], alloris, type='features_table',missingv=0, auxfun=mean)
wtnetefl25[[ "wt_scores_25kb.df"]] <- load_features(wtnetefl25[[ "wt_scores_25kb.df"]], alloris, type='features_table',missingv=0, auxfun=mean)
wtnetefl25[[ "wt_scores_10kb.df"]] <- load_features(wtnetefl25[[ "wt_scores_10kb.df"]], alloris, type='features_table',missingv=0, auxfun=mean)
randwtmm10oris=list.files(paste0(datapath,'/RandOriTSSExp/mm10/random_efficiency_mm10/wt_all'))
for (f in randwtmm10oris){
print(f)
oris <- read.table(paste0(datapath,'/RandOriTSSExp/mm10/random_efficiency_mm10/wt_all/',f), header = T, stringsAsFactors = F)
wtnetefl25[[ "wt_scores_5kb.df"]]<- load_features(wtnetefl25[[ "wt_scores_5kb.df"]], oris[-c(4:6)], type='features_table',missingv=0)
wtnetefl25[[ "wt_scores_10kb.df"]]<- load_features(wtnetefl25[[ "wt_scores_10kb.df"]], oris[-c(4:6)], type='features_table',missingv=0)
wtnetefl25[[ "wt_scores_25kb.df"]]<- load_features(wtnetefl25[[ "wt_scores_25kb.df"]], oris[-c(4:6)], type='features_table',missingv=0)
}
colnames(wtnetefl25[[ "wt_scores_25kb.df"]]$features)=c(gsub('mean_efficiency_|_mm10.bed','',names(orisCsel)),'ALL-ORI', paste0('r', c(1:20)))
colnames(wtnetefl25[[ "wt_scores_5kb.df"]]$features)=c(gsub('mean_efficiency_|_mm9.bed','',names(orisCsel)),'ALL-ORI', paste0('r', c(1:20)))
colnames(wtnetefl25[[ "wt_scores_10kb.df"]]$features)=c(gsub('mean_efficiency_|_mm9.bed','',names(orisCsel)),'ALL-ORI', paste0('r', c(1:20)))
#wtorinodes=which(wtnetefl25[[2]]$features>0)
wtchasl25=lapply(wtnetefl25, chas)
wtfeatl25=lapply(wtnetefl25, export)
```
```{r}
##randomization
nrand=50
#wtC25_5kb_randlnew29july=randomize(wtnetefl25[[3]],nrandom=nrand, preserve.nodes = NULL, dist.match=T)
#save(wtC25_5kb_randlnew29july, file=paste0('wtC25_5kb_randl',nrand,'_dataset29july.Rdata'))
load(paste0('/media/data/home/vera/RepOri/RepOriCode/RepOri3D/wtC25_5kb_randlnew50_dataset.Rdata'))
```
```{r}
wtC25_5kb_randlchas=lapply(wtC25_5kb_randlnew, chas)
wtC25_5kb_randlfeat=lapply(wtC25_5kb_randlnew, export)
randchas_WT=lapply(wtC25_5kb_randlchas, function(x){
return(x['wt_5kb_mm10'])
})
```
Plot function
```{r}
plotchas<-function(feat, chas, randfeat,randchas, xmin, xmax, ymin, ymax, title, labon, exclude, valuef){
sel=grep(exclude, names(chas), invert=T)
print(names(chas)[sel])
drdf=as.data.frame(randchas)
# print(head(randchas))
drmean=rowMeans(drdf)
drsd=apply(drdf, 1, sd)
zs=0
for (d in 1:length(chas)){
# print(chas[d])
# print(drmean[d])
zs[d]=round((chas[d]-drmean[d])/drsd[d],2)
#print(zs[d])
}
names(zs)=names(chas)
labels=names(chas)
labsel=gsub('_5KB_MM10','',toupper(labels))
labsel[grep('^R', labsel)]<-''
labsel=gsub('CONSTITUTIVE', 'COMM', labsel)
colssel=cols[labsel]
colssel[8:length(colssel)]<-'purple'
if(labon=='OFF'){
labs=rep('', ncol(feat))
}
if(missing(xmin)) {
xmin=min(unlist(lapply(randfeat, colMeans)))
}
if(missing(xmax)){
xmax=max(unlist(lapply(randfeat, colMeans)))
}
if(missing(ymin)) {
ymin=min(unlist(randchas))
}
if(missing(ymax)){
ymax=max(unlist(randchas))
}
if(missing(ymax)){
title='ChAs Plot'
}
##define abundance as mean origin eff divided by number of origins (excluding 0)
#ab=colSums(feat)/(colSums(feat>0)*valuef)
ab=colSums(feat)/nrow(feat)
par(oma=c(1,1,1,1))
plot( ab[sel],chas[sel], pch=21, cex=1.5, xlab='Efficiency averaged across network nodes', ylab='OriEfAs', cex.axis=1.5, cex.lab=1.5, xlim=c(xmin, xmax), ylim=c(ymin, ymax), bg=colssel[sel], col='black', main=title)
# text( ab[sel],chas[sel],labels=labsel[sel], pos=4, srt=90, cex=1.5)
for (i in 1:length(randchas)){
#abrand=colSums(randfeat[[i]])/(colSums(randfeat[[i]]>0)*valuef)
abrand=colSums(randfeat[[i]])/nrow(randfeat[[i]])
# print(summary(abrand[sel]))
points( jitter(abrand[sel], amount=1/60),randchas[[i]][sel], pch=1, col=colssel[sel], cex=3, lwd=0.5)
}
text( abrand[sel],randchas[[i]][sel],chas[sel],labels='', pos=4, srt=90)
##add Zscores
#print('nameszs')
#print(zs)
text(ab[sel][1:5], rep(-0.1, 5), labels=round(zs[sel][1:5],0), pos=3, col=colssel[sel][1:5])
text(ab[sel][6], rep(-0.1), labels=round(zs[sel][5],0), pos=3, col='black')
#text(ab[sel][16], rep(-0.1), labels=round(mean(zs[sel][17:25]),0), pos=4, col='black')
points( ab[sel],chas[sel], pch=21, cex=1.5)
abline(h=0)
#print(summary(ab[sel]))
#print(summary(chas[sel]))
}
###end of function
```
```{r}
#pdf('VOCHiC_OriEfAs_cor.pdf')
plotchas(wtfeatl25[[3]], wtchasl25[[3]], wtC25_5kb_randlfeat,wtC25_5kb_randlchas ,xmin=0.25, xmax=2.1, ymin=-0.1, ymax=0.2, labon='ON', title='VOCHiC 5kb', exclude='allOris|exclusive|ALL-ORI|r1|r2$|r3|r4|r5|r6|r7|r8|r9|r10', 1)
#dev.off()
```
```{r}
### compartments
comps=read.table(paste0(datapath,'/cav/BonevCompartments.csv'), sep=',', header=T)
compsbin=comps
compsbin[,4]=(compsbin[,4]=='A')
compsbin[,4]=sapply(compsbin[,4], as.numeric)
###now load compartment info as a feature
wtnetefl25COMP=wtnetefl25
wtnetefl25COMP[[ "wt_scores_5kb.df"]]<- load_features(wtnetefl25COMP[[ "wt_scores_5kb.df"]], compsbin, type='features_table',missingv=0, auxfun=mean, featnames='comp')
wtnetefl25COMP[[ "wt_scores_25kb.df"]]<- load_features(wtnetefl25COMP[[ "wt_scores_25kb.df"]], compsbin, type='features_table',missingv=0, auxfun=mean, featnames='comp')
wtnetefl25COMP[[ "wt_scores_10kb.df"]]<- load_features(wtnetefl25COMP[[ "wt_scores_10kb.df"]], compsbin, type='features_table',missingv=0, auxfun=mean, featnames='comp')
###write files with edges
#save(wtnetefl25COMP[[ "wt_scores_5kb.df"]],
#, wtnetefl25COMP[[ "wt_scores_25kb.df"]], wtnetefl25COMP[[ "wt_scores_10kb.df"]],
# file=paste0(datapath,'/VOCHiC_networks_forKaustav.RData'))
###identify which nodes are A or B
Acompnodes5= wtnetefl25COMP[[ "wt_scores_5kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_5kb.df"]]$features[,'Compartment']==1)]
Bcompnodes5= wtnetefl25COMP[[ "wt_scores_5kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_5kb.df"]]$features[,'Compartment']<1)]
Acompnodes10= wtnetefl25COMP[[ "wt_scores_10kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_10kb.df"]]$features[,'Compartment']==1)]
Bcompnodes10= wtnetefl25COMP[[ "wt_scores_10kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_10kb.df"]]$features[,'Compartment']<1)]
Acompnodes25= wtnetefl25COMP[[ "wt_scores_25kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_25kb.df"]]$features[,'Compartment']==1)]
Bcompnodes25= wtnetefl25COMP[[ "wt_scores_25kb.df"]]$nodes[which(wtnetefl25COMP[[ "wt_scores_25kb.df"]]$features[,'Compartment']<1)]
##create A subnetwork for each compartment
wtnetefl25COMPA_5kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_5kb.df"]], method="nodes", nodes1=Acompnodes5)
wtnetefl25COMPB_5kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_5kb.df"]], method="nodes", nodes1=Bcompnodes5)
COMPA5kb_chas=chas(wtnetefl25COMPA_5kb)
COMPA5kb_feat=export(wtnetefl25COMPA_5kb)
COMPB5kb_chas=chas(wtnetefl25COMPB_5kb)
COMPB5kb_feat=export(wtnetefl25COMPB_5kb)
wtnetefl25COMPA_10kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_10kb.df"]], method="nodes", nodes1=Acompnodes10)
wtnetefl25COMPB_10kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_10kb.df"]], method="nodes", nodes1=Bcompnodes10)
wtnetefl25COMPA_25kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_25kb.df"]], method="nodes", nodes1=Acompnodes25)
wtnetefl25COMPB_25kb=subset_chromnet(wtnetefl25COMP[[ "wt_scores_25kb.df"]], method="nodes", nodes1=Bcompnodes25)
```
```{r}
###randomization within each compartment
nrand=50
#wtnetefl25COMPA_5kb_rand50=randomize(wtnetefl25COMPA_5kb,nrandom=nrand, preserve.nodes = NULL, dist.match=T)
#wtnetefl25COMPB_5kb_rand50=randomize(wtnetefl25COMPB_5kb,nrandom=nrand, preserve.nodes = NULL, dist.match=T)
#save(wtnetefl25COMPA_5kb_rand50, wtnetefl25COMPB_5kb_rand50, file=paste0(datapath, '/Compartmentsrand50.RData'))
load(paste0(datapath, '/Compartmentsrand50.RData'))
COMPA5kb_randlchas=lapply( wtnetefl25COMPA_5kb_rand50, chas)
COMPA5kb_randlfeat=lapply (wtnetefl25COMPA_5kb_rand50, export)
COMPB5kb_randlchas=lapply( wtnetefl25COMPB_5kb_rand50, chas)
COMPB5kb_randlfeat=lapply (wtnetefl25COMPB_5kb_rand50, export)
```
```{r}
pdf(paste0(datapath,'/CompartmentAnalysisOriEfAs50rand.pdf'))
plotchas(wtfeatl25[[3]], wtchasl25[[3]], wtC25_5kb_randlfeat,wtC25_5kb_randlchas ,xmin=0.25, xmax=2.5, ymin=-0.1, ymax=0.3, labon='ON', title='VOCHiC 5kb', exclude='allOris|exclusive|ALL-ORI|r1|r2$|r3|r4|r5|r6|r7|r8|r9|r10', 1)
dev.off()
pdf(paste0(datapath,'/CompartmentAnalysisOriEfAs50randA.pdf'))
plotchas(COMPA5kb_feat, COMPA5kb_chas, COMPA5kb_randlfeat,COMPA5kb_randlchas ,xmin=0.25, xmax=2.5, ymin=-0.1, ymax=0.3, labon='ON', title='VOCHiC COMPA 5kb', exclude='allOris|exclusive|ALL-ORI|r1|r2$|r3|r4|r5|r6|r7|r8|r9|r10|Compartment', 1)
dev.off()
pdf(paste0(datapath,'/CompartmentAnalysisOriEfAs50randB.pdf'))
plotchas(COMPB5kb_feat, COMPB5kb_chas, COMPB5kb_randlfeat,COMPB5kb_randlchas ,xmin=0.25, xmax=2.5, ymin=-0.1, ymax=0.3, labon='ON', title='VOCHiC COMPB 5kb', exclude='allOris|exclusive|ALL-ORI|r1|r2$|r3|r4|r5|r6|r7|r8|r9|r10|Compartment', 1)
dev.off()
```
```{r}
oritype25_5=rep(0, nrow(wtfeatl25[[3]]))
names(oritype25_5)=rownames(wtfeatl25[[3]])
oritype25_5[which(wtfeatl25[[3]][,'wt_5kb_mm10']>0)]='WT'
oritype25_5[which(wtfeatl25[[3]][,'aph_5kb_mm10']>0)]='APH'
oritype25_5[which(wtfeatl25[[3]][,'cdc6_5kb_mm10']>0)]='CDC6'
oritype25_5[which(wtfeatl25[[3]][,'constitutive_5kb_mm10']>0)]='COMM'
write.table(cbind(names(oritype25_5),oritype25_5), paste0(datapath,'/OriType25_5.txt'), quote=F, sep='\t', row.names=F)
###print wtef
write.table(cbind(names(oritype25_5),wtfeatl25[[3]][,'wt_5kb_mm10']), paste0(datapath,'/WTef_25_5.txt'), quote=F, sep='\t', row.names=F)
```
```{r}
###distribution of efficiencies
boxplot(wtfeatl25[[3]][,c(1:5)]/colSums(wtfeatl25[[3]][,c(1:5)]>0), las=2, outline=F, names=gsub('_5kb_mm10', '', colnames(wtfeatl25[[3]][,c(1:5)])))
###distribution of efficiencies
boxplot(wtfeatl25[[3]][,c(1:5)]/colSums(wtfeatl25[[3]][,c(1:5)]>0), las=2, outline=F, names=gsub('_5kb_mm10', '', colnames(wtfeatl25[[3]][,c(1:5)])))
```
```{r}
#Distance analysis
distne<-function(ne){
dist=apply(ne$edgesdf, 1, function(x){
res=NA
#print(x)
#print(x["start_from"])
if(x["chrom_from"] == x["chrom_to"]){
res= abs(as.numeric(x["start_from"])-as.numeric(x["start_to"]))
}
return(res)
})
print(summary(dist))
plot(density(log10(dist), na.rm=T), xlab='log10 distance', main=deparse(substitute(ne)))
return(dist)
}
```
```{r}
dist_VOCHiC=distne(wtnetsl[[3]])
```
```{r}
#Consider overlap with promoters
promsbed=read.table(paste0(datapath,'/Active_Promoter_mm10.bed.txt'), sep='\t')
colnames(promsbed)=c('chr', 'start', 'end')
bedproms=with(promsbed, GRanges(chr, IRanges(start, end)))
bedproms$ID=paste0('p','_',promsbed[,1], ':',promsbed[,2],'-', promsbed[,3])
tssbed=read.table(paste0(datapath,'/TSS_ranges_mm10.bed.txt'), sep='\t')
colnames(tssbed)[1:3]=c('chr', 'start', 'end')
bedtss=with(tssbed, GRanges(chr, IRanges(start, end)))
bedtss$ID=paste0('t','_',tssbed[,1], ':',tssbed[,2],'-', tssbed[,3])
```
```{r}
###match between HiCori and promsbed
promstatsl=lapply(wtnetefl25, function(x){
bedHiCwtori=with(x$nodesdf[,c(1:3)], GRanges(chrom, IRanges(start, end)))
bedHiCwtori$ID=paste0(x$nodesdf[,1], ':', x$nodesdf[,2], '-', x$nodesdf[,3])
names(bedHiCwtori)=bedHiCwtori$ID
over=findOverlaps(bedHiCwtori,bedproms)
match_hitwtproms <- data.frame(bedHiCwtori$ID[queryHits(over)],bedproms$ID[subjectHits(over)])
HiCoriP=unique(as.vector(match_hitwtproms[,1]))
HiCoriNONP=setdiff(x$nodes, HiCoriP)
print(paste('P ',length(HiCoriP), ' nonP ',length(HiCoriNONP)))
print('P')
wtnetefl25_P=subset_chromnet(x, method='nodes', nodes1=HiCoriP)
print('NonP')
wtnetefl25_nonP=subset_chromnet(x, method='nodes', nodes1=HiCoriNONP)
#print(head(HiCoriP))
print('PO')
wtnetefl25_PO <- chaser::subset_chromnet(x, method="nodes", nodes1=HiCoriP, nodes2=HiCoriNONP)
###match between HiCori and bedtss
over=findOverlaps(bedHiCwtori,bedtss)
match_hitwttss <- data.frame(bedHiCwtori$ID[queryHits(over)],bedtss$ID[subjectHits(over)])
HiCoriTSS=unique(as.vector(match_hitwttss[,1]))
HiCoriNONTSS=setdiff(x$nodes, HiCoriTSS)
print('TSS')
wtnetefl25_TSS=subset_chromnet(x, method='nodes', nodes1=HiCoriTSS)
print('nonTSS')
wtnetefl25_nonTSS=subset_chromnet(x, method='nodes', nodes1=HiCoriNONTSS)
return(list('bed'=bedHiCwtori,'TSS'=wtnetefl25_TSS, 'P'=wtnetefl25_P, 'nonTSS'=wtnetefl25_nonTSS, 'nonP'=wtnetefl25_nonP, 'PO'=wtnetefl25_PO))
})
```
Create annotation files
```{r}
promannot5= rep(0, length(wtnetefl25[[3]]$nodes))
names(promannot5)=wtnetefl25[[3]]$nodes
promannot5[which(names(promannot5) %in% promstatsl[[3]]$P$nodes )]<-'P'
write.table(promannot5, paste0(datapath,'/promannot5.txt'), quote=F, sep='\t', row.names=T, col.names=F)
features5=wtnetefl25[[3]]$features
write.table(cbind(rownames(features5),features5),'featuresVOCHiC5.txt', quote=F, sep='\t', row.names=F)
```
Extract graphs
```{r}
library(igraph)
Gwt25l=lapply(wtnetefl25, function(x){
return(graph.data.frame(x$edgesdf[,c(7,8)], directed=F))
})
wt25_5_chr1=subset_chromnet(wtnetefl25[[3]], chrom='chr1')
write.table(wt25_5_chr1$edgesdf, paste0(datapath,'/Gwt25_5_chr1.txt'), quote=F, sep='\t')
wt25_5_chr6=subset_chromnet(wtnetefl25[[3]], chrom='chr6')
write.table(wt25_5_chr6$edgesdf, paste0(datapath,'/Gwt25_5_chr6.txt'), quote=F, sep='\t')
wt25_5_chr19=subset_chromnet(wtnetefl25[[3]], chrom='chr19')
write.table(cbind(rownames(wt25_5_chr19$edgesdf),wt25_5_chr19$edgesdf), paste0(datapath,'/Gwt25_5_chr19.txt'), quote=F, sep='\t', row.names=F)
Gwt25Pl=lapply(promstatsl, function(x){
return(graph.data.frame(x$P$edgesdf[,c(7,8)], directed=F))
})
Gwt25nonPl=lapply(promstatsl, function(x){
return(graph.data.frame(x$nonP$edgesdf[,c(7,8)], directed=F))
})
Gwt25TSSl=lapply(promstatsl, function(x){
return(graph.data.frame(x$TSS$edgesdf[,c(7,8)], directed=F))
})
Gwt25nonTSSl=lapply(promstatsl, function(x){
return(graph.data.frame(x$nonTSS$edgesdf[,c(7,8)], directed=F))
})
glist=list(Gwt25l[[3]], Gwt25Pl[[3]], Gwt25nonPl[[3]], Gwt25TSSl[[3]], Gwt25nonTSSl[[3]])
names(glist)=c('tot', 'P', 'nonP','TSS', 'nonTSS' )
Gncomp=lapply(glist, function(x){
return(clusters(x)$no)
})
Gdense=lapply(glist, function(x){
return(graph.density(x))
})
write.table(promstatsl[[3]][['nonTSS']]$edgesdf, paste0(datapath,'/Gwt25_nonTSS.txt'), quote=F, sep='\t')
##annotation of nodes
nodeannot=cbind(wtnetefl25[[3]]$nodes, rep(0, length(wtnetefl25[[3]]$nodes)))
nodeannot[which(nodeannot[,1] %in% promstatsl[[3]][['TSS']]$nodes),2]<-1
write.table(nodeannot, 'TSSnodes5kb.txt', quote=F, sep='\t')
```
```{r}
###Check relationship of degree to origin type
VOCHiC_5kb=Gwt25l[[3]]
###check which nodes are APH, WT, CDC6 etc
wtnetefl25[[3]]
##check if nonCOMM oris have lower degree
NonCOMMoris=rownames(wtnetefl25[[3]]$features)[which(wtnetefl25[[3]]$features[,'constitutive_5kb_mm10']==0)]
COMMoris=rownames(wtnetefl25[[3]]$features)[which(wtnetefl25[[3]]$features[,'constitutive_5kb_mm10']>0)]
pdf('Deg_COMMvsNON.pdf')
boxplot(degree(Gwt25l[[3]])[COMMoris], degree(Gwt25l[[3]])[NonCOMMoris], names=c('COMM', 'WT specific'), ylab='Degree', cex.axis=1.5, cex.lab=1.5)
dev.off()
wilcox.test(degree(Gwt25l[[3]])[COMMoris], degree(Gwt25l[[3]])[NonCOMMoris])
write.table(promstatsl[[3]][['nonTSS']]$edgesdf, paste0(datapath,'/Gwt25_nonTSS.txt'), quote=F, sep='\t')
##annotation of nodes
nodeannot=cbind(wtnetefl25[[3]]$nodes, rep(0, length(wtnetefl25[[3]]$nodes)))
nodeannot[which(nodeannot[,1] %in% promstatsl[[3]][['TSS']]$nodes),2]<-1
write.table(nodeannot, paste0(datapath,'/TSSnodes5kb.txt'), quote=F, sep='\t')
##Network properties
colSums(wtnetefl25[[3]]$features>0)
write.table(colSums(wtnetefl25[[3]]$features>0),'VOCHiC5kb_summary.txt', quote=F, sep='\t')
##cliques counting
#cliques=cliques(Gwt25l[[3]])
```
Correlations degree, efficiency RT
```{r}
library(gtools)
##Check relation of degree with efficiency in non-promoter nodes
effsnonP5=promstatsl[[3]]$nonP$features[,1]
degnonP5=degree(Gwt25nonPl[[3]])
boxplot(effsnonP5~degnonP5)
cor.test(effsnonP5,degnonP5)
quantilesEffsnonP5 <- quantcut( effsnonP5, q=4 )
#pdf('Effvsdeg_nonP.pdf')
boxplot(degnonP5~quantilesEffsnonP5, varwidth=T, xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5)
#dev.off()
cor.test(effsnonP5,degnonP5)
effs=wtnetefl25[[3]]$features[,1]
deg=degree(Gwt25l[[3]])
quantilesEffs <- quantcut( effs, q=4 )
#pdf('Effsvsdeg_all.pdf')
boxplot(deg~quantilesEffs, varwidth=T, xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5)
#dev.off()
cor.test(effs,deg)
```
```{r}
effsnonP10=promstatsl[[1]]$nonP$features[,1]
quantilesEffsnonP10 <- quantcut( effsnonP10, q=4 )
degnonP10=degree(Gwt25nonPl[[1]])
#pdf('Effsvsdeg_all_10kb.pdf')
boxplot(degnonP10~quantilesEffsnonP10,xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5)
#dev.off()
cor.test(effsnonP10,degnonP10)
effs10=wtnetefl25[[1]]$features[,1]
quantilesEffs10 <- quantcut( effs10, q=4 )
deg10=degree(Gwt25l[[1]])
#pdf('Effsvsdeg_all_10kb.pdf')
boxplot(deg10~quantilesEffs10,xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5)
#dev.off()
cor.test(effs10,deg10)
effsnonP25=promstatsl[[2]]$nonP$features[,1]
quantilesEffsnonP25 <- quantcut( effsnonP25, q=4 )
degnonP25=degree(Gwt25nonPl[[2]])
#pdf('Effsvsdeg_all_25kb.pdf')
boxplot(degnonP10~quantilesEffsnonP10,xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5)
#dev.off()
cor.test(effsnonP25,degnonP25)
effs25=wtnetefl25[[2]]$features[,1]
quantilesEffs25 <- quantcut( effs25, q=4 )
deg25=degree(Gwt25l[[2]])
#pdf('Effsvsdeg_all_25kb.pdf')
boxplot(deg25~quantilesEffs25,xlab='Efficiency quartiles', ylab='Degree', cex.axis=1.5, cex.lab=1.5, las=2)
#dev.off()
cor.test(effs25,deg25)
```
RT data
```{r}
#Part on Replication Timing
RT=read.table(paste0(datapath,'/Hiratani2010_mousereptiming/RT_BAF250a f_f_ESC_Int52769503_mm10.bedgraph'),sep='\t', skip=11, header=F)
RTfin=read.table(paste0(datapath, '/RTFeb2021/RT_46C_ESC_Int62150809_mm10.bedgraph'), sep='\t', skip=11, header=F)
colnames(RTfin)=c('chr', 'start', 'end', 'RTfin')
```
```{r}
netwtRT=load_features(wtnetsl25[[3]], RTfin, type='features_table', auxfun=mean, featnames='RTfin', missingv=0)
wtnetefl25_C5kbRT=load_features(wtnetsl25[[3]], RTfin, type='features_table', auxfun=mean, featnames = 'RT', missingv=0)
write.table(cbind(rownames(wtnetefl25_C5kbRT$features), wtnetefl25_C5kbRT$features), paste0(datapath,'/wtC5kb_RT.txt'), quote=F, sep='\t', row.names=F)
##rank repT
RT=wtnetefl25_C5kbRT$features[,1]
rankRT=(1-rank(RT)/length(RT))
RTdf=netwtRT$nodesdf
RTdf=data.frame( rankRT)
#wtnetefl25_C5kbRT=load_features(wtnetefl25_C5kbRT,RTdf, type='features_on_nodes')
#wtC25_5kb_randlRT=randomize(wtnetefl25_C5kbRT,nrandom=nrand, preserve.nodes = NULL, dist.match=T)
#save(wtC25_5kb_randlRT, file=paste0(datapath, '/RTFeb2021/wtC25_5kb_randlRT.Rdata'))
load(paste0(datapath, '/RTFeb2021/wtC25_5kb_randlRT.Rdata'))
drRTfeat=lapply(wtC25_5kb_randlRT, export)
drRTchas=lapply(wtC25_5kb_randlRT, chas)
###Calculate Zscore:
##flatten out random list
drRTchasdf=as.data.frame(drRTchas)[2,]
drRTchasmean=rowMeans(drRTchasdf)
drRTchassd=apply(drRTchasdf,1, sd)
zsP=0
for (d in 1:length(drRTchas)){
zsP[d]=round((drRTchasdf[d]-drRTchasmean)/drRTchassd,2)
}
names(zsP)=names(drRTchas)
```
```{r}
###now with RT
quantilesRT <- quantcut(wtnetefl25_C5kbRT$features[,1] , q=4 )
#pdf('RTvsEff.pdf')
boxplot(wtnetefl25_C5kbRT$features[,1]~quantilesEffs, xlab='Efficiency quartiles', ylab='RT')
#dev.off()
cor.test(wtnetefl25_C5kbRT$features[,1],effs)
#pdf('RTvdeg.pdf')
boxplot( deg~quantilesRT, xlab='RT quartiles', ylab='Degree')
#dev.off()
cor.test(wtnetefl25_C5kbRT$features[,1],deg)
```
```{r}
distne<-function(ne){
dist=apply(ne$edgesdf, 1, function(x){
res=NA
#print(x)
#print(x["start_from"])
if(x["chrom_from"] == x["chrom_to"]){
res= abs(as.numeric(x["start_from"])-as.numeric(x["start_to"]))
}
return(res)
})
print(summary(dist))
plot(density(log10(dist), na.rm=T), xlab='log10 distance', main=deparse(substitute(ne)))
return(dist)
}
##PCHiC
dist_VOCHiC5kb=distne(wtnetsl25[[3]])
quantdistVOCHiC5kb=quantcut( dist_VOCHiC5kb, q=10 )
dist_VOCHiC10kb=distne(wtnetsl25[[1]])
plot(density(log10(dist_VPCHiC5kb), na.rm=T), type='line', lwd=3, xlab='Distance in log10(bases)', main='VPCHIC networks 5kb')
points(density(log10(dist_PCHiC), na.rm=T), col='cyan', type='l', lwd=3)
```
Load TAD coordinates and map VOCHiC fragments to TADs
```{r Comparison with tads}
#tadold=read.table(paste(datapath,paste0('/total.HindIII.combined.domain'), sep='/'))
tadbonev=read.table('/media/data/home/vera/RepOriData/cav/TADdetailsBonev.csv', sep=',', header=T)
tad=tadbonev
rownames(tad)=paste(rep('TAD', nrow(tad)),seq(from=1, to=nrow(tad)), sep='')
colnames(tad)=c('chr', 'start', 'end')
write.table(tad, 'TADbonannot.txt', quote=F, sep='\t')
## use chaser to assign features
orinet=wtnetsl25[[3]]
taddf=cbind(tad,rownames(tad))
oribed=with(orinet$nodesdf, GRanges(chrom, IRanges(start, end)))
oribed$ID=rownames(orinet$nodesdf)
tadbed=with(tad, GRanges(chr, IRanges(start, end)))
tadbed$ID=rownames(tad)
tadover=findOverlaps(tadbed,oribed)
tadmatch_hit <- data.frame(tadbed$ID[queryHits(tadover)],oribed$ID[subjectHits(tadover)] )
tadfrag=tadmatch_hit[!duplicated(tadmatch_hit),]
colnames(tadfrag)=c('tad', 'frag')
#table(table(tadfrag$tad))
#table(table(tadfrag$frag))
summary(as.numeric(table(tadfrag$tad)))
summary(as.numeric(table(tadfrag$frag)))
tadfragproc=tadfrag[-which(table(tadfrag$frag)>1),]
write.table(tadfrag,'Orinet_TADbonannot.txt', quote=F, sep='\t', row.names=F)
###load efficiency values for frag
Orief5kb=wtnetefl25[[3]]$features[,'ALL-ORI']
tadfrag[,1]=as.character(tadfrag[,1])
tadorief=cbind(tadfrag[,1], tadfrag[,2], Orief5kb[tadfrag$frag], wtnetefl25[[3]]$nodesdf[tadfrag$frag,'start'])
colnames(tadorief)=c('tad', 'num', 'allef', 'start')
TAD856=as.vector(unique(tadfragproc[which(tadfragproc[,1]=='TAD856'),2]))
#TAD856orinet=induced.subgraph(Goril[['ALL-ORI']],V(Goril[['ALL-ORI']])[which(V(Goril[['ALL-ORI']])$name %in% TAD856)])
###make orinet Graph
Gorinet=graph.data.frame(wtnetsl25[[3]]$edgesdf[,-c(1:6)], directed=F)
```
Identify intra- and inter-TAD edges
```{r associate each int to intra or inter tad}
el=get.edgelist(Gorinet)
rownames(el)=paste(el[,1], el[,2], sep='-')
E(Gorinet)$enames=rownames(el)
###elt list of edges in terms of TADS
elt=el
for (t in as.vector(unique(tadfragproc$tad))){
#print(t)
fr=as.vector(tadfragproc$frag[which(tadfragproc$tad ==t)])
elt[which(el[,1] %in% fr),1]=t
elt[which(el[,2] %in% fr),2]=t
}
intertads=elt[which(elt[,1]!=elt[,2]),]
intertad_edges=el[which(elt[,1]!=elt[,2]),]
###nodes involved in intertads
intertadnodes=unique(c(intertad_edges[,1], intertad_edges[,2]))
intratadnodes=setdiff(names(Orief5kb), intertadnodes)
Orief5kb[intertadnodes]
boxplot(Orief5kb[intertadnodes], Orief5kb[intratadnodes], outline=F)
intertadsproc=intertads#[-c(grep('_', intertads[,1]), grep('_', intertads[,2])),]
```
```{r}
E(Gorinet)$intert=rep(0, length(E(Gorinet)))
E(Gorinet)$intert[which(E(Gorinet)$enames %in% rownames(intertadsproc))]=1
intere=E(Gorinet)$enames[which(E(Gorinet)$enames %in% rownames(intertadsproc))]
interchrom=E(Gorinet)$enames[which(is.na(E(Gorinet)$dist))]
```
Process data on interaction distance
```{r}
#length(intersect(E(Goril_enames[['WT']])$enames, intere))
E(Gorinet)$dist=abs(orinet$edgesdf$start_to-orinet$edgesdf$start_from)
distori=abs(as.numeric(E(Gorinet)$dist))
distintertad=abs(as.numeric(E(Gorinet)$dist)[which(E(Gorinet)$enames %in% intere)])
distintratad=abs(as.numeric(E(Gorinet)$dist)[-which(E(Gorinet)$enames %in% intere)])
distintertadori=abs(as.numeric(E(Gorinet)$dist)[which(E(Gorinet)$enames %in% intere)])
names(distintertadori)=E(Gorinet)$enames[which(E(Gorinet)$enames %in% intere)]
distintratadori=abs(as.numeric(E(Gorinet)$dist)[-which(E(Gorinet)$enames %in% intere)])
names(distintratadori)=E(Gorinet)$enames[-which(E(Gorinet)$enames %in% intere)]
##Find peaks function to identify peaks in distribution
find_peaks <- function (x, m){
shape <- diff(sign(diff(x, na.pad = FALSE)))
pks <- sapply(which(shape < 0), FUN = function(i){
z <- i - m + 1
z <- ifelse(z > 0, z, 1)
w <- i + m + 1
w <- ifelse(w < length(x), w, length(x))
if(all(x[c(z : i, (i + 2) : w)] <= x[i + 1])) return(i + 1) else return(numeric(0))
})
pks <- unlist(pks)
pks
}
peakinter=find_peaks(density(log10(distintertad))$y, 0.1)
peakintra=find_peaks(density(log10(distintratad))$y, 0.1)
peakintraori=find_peaks(density(log10(distintratadori))$y, 0.1)
peakinterori=find_peaks(density(log10(distintertadori))$y, 0.1)
```
Plots about distance distributions
```{r}
#pdf(paste(datapath,'Figures/DistanceDep/Distance_distribution_TAD.pdf', sep='/'))
plot(density(log10(distintertad[!is.na(distintertad)])),lty=2, col='purple',lwd=2, xlab='Distance spanned (log10)', main='Distribution of distances spanned in network contacts', ylim=c(0,1))
points(density(log10(distintratad[!is.na(distintratad)])), lty=2,type='l',lwd=2, col='pink')
#abline(v=density(log10(distintertadori))$x[peakinter], col='purple')
#abline(v=density(log10(distintratadori))$x[peakintra], col='pink')
#abline(v=median(log10(distintertadori)), col='purple')
#abline(v=median(log10(distintratadori)), col='pink')
print ('intertad peaks')
density(log10(distintertad))$x[peakinter]
print ('intratad peaks')
density(log10(distintratad))$x[peakintra]
legend('topleft', legend=c('VOCHiC interTAD', 'VOCHiC intraTAD'), lty=c(2,2), col=c('purple', 'pink' ))
#dev.off()
#pdf(paste(datapath,'Figures/DistanceDep/Distance_distribution_allcombined.pdf', sep='/'))
#png('../RepOriCode/Figures/DistanceDep/Distance_distribution_allcombined.png')
#pdf('DistVOCHiCcor.pdf')
plot(density(log10(distintertadori[!is.na(distintertadori)])), col='darkgreen',lwd=3, xlab='Distance spanned (log10 bases)', ylim=c(0,1), cex=2, cex.lab=2, cex.axis=2, main='')
points(density(log10(distintratadori[!is.na(distintratadori)])), type='l',lwd=3, col='cyan')
points(density(log10(distori[!is.na(distori)])), type='l',lwd=3, col='black')
legend(3,1 ,lty=1, lwd=3,col=c('black', 'cyan', 'darkgreen'), legend=c('All', 'Intra-TAD', 'Inter-TAD'), bty="n", cex=2)
#dev.off()
```
```{r}
load(paste0(datapath,'/distances.Rdata'))
plot(density(log10(dist_VPCHiC5kb), na.rm=T), type='line', lwd=3, xlab='Distance in log10(bases)', main='VPCHIC networks 5kb', ylim=c(0,1))
points(density(log10(dist_PCHiC), na.rm=T), col='cyan', type='l', lwd=3)
points(density(log10(distori), na.rm=T), type='line', col='blue', lwd=3)
points(density(log10(dist_PCHiCPP), na.rm=T), type='line', col='green', lwd=3)
points(density(log10(distintratadori), na.rm=T), type='line', col='purple', lwd=3)
legend(3,0.8 ,lty=1, lwd=3,col=c('black', 'cyan', 'blue', 'green', 'purple'), legend=c('VPCHiC 5kb', 'PCHiC', 'VOCHiC', 'PCHiC-PP','VOCHiCintra') , bty="n", cex=1.5)
```
Check that when interactions are interTAD they unite TADs with similar RT (more similar than random interactions)
```{r}
#Load RT data
#load(paste(datapath,'Hiratani_fragrankmed.Rdata', sep='/'))
###now check if intertads interactions mean different RT
##calculate difference in RT rank in fragments united in network
#RTdifints=apply(el,1, function(x){
#x=unlist(strsplit(x, split='_'))
#return(fragrankmed[x[1],1]-fragrankmed[x[2],1])