RepOri_revision_VPCHiC.Rmd

# library(devtools)
# devtools::install_bitbucket("eraineri/chaser", build_opts=c(), force=T)

```{r}
library(chaser)
library(GenomicRanges)
library(igraph)

##Set datapath
datapath='/media/zeus/Verbatim HDD/RepOriKarolina2021/RepOriData' 


source(paste0(datapath,'/coloursdef.R'))
```
Now use Cavalli net to extract VPCHiC
```{r}


ppfiles=list.files(paste0(datapath,'/cav/'))
ppfiles=ppfiles[grep('TSS_scores', ppfiles)]
PPnets=as.list(paste0(datapath, '/cav/',ppfiles))
names(PPnets)=ppfiles


PPnetsprocl25=lapply(PPnets, function(x){
  x=get(load(x))
 # print(head(x))
    sel=which(x$maxScore>35)   ########attention
  return(x[sel,1:6])
})

PPnetsl25=PPnetsprocl25

win=c(10000, 25000, 5000) ##########################################attention PROBLEM!

PPnetsprocl25=PPnetsl25
for (w in c(1:3)){
PPnetsprocl25[[w]]$start1=sapply(PPnetsl25[[w]]$start1, function(x){return(max((x-win[w]),0))})
PPnetsprocl25[[w]]$start2=sapply(PPnetsl25[[w]]$start2, function(x){return(max((x-win[w]),0))})
PPnetsprocl25[[w]]$end1=sapply(PPnetsl25[[w]]$end1, function(x){return((x+win[w]))})
PPnetsprocl25[[w]]$end2=sapply(PPnetsl25[[w]]$end2, function(x){return((x+win[w]))})

}
###remove chromosome 18

PPnetsprocl25=lapply(PPnetsprocl25, function(x){
  return(x[which(x$chrom1!='chr18'),])
})

PPnetschroml25=lapply(PPnetsprocl25, make_chromnet)

PPnetefl25=PPnetschroml25


```

Now load efficiencies calculated for specific TSS regions
Load TSS effs
```{r}

pathftss=paste0(datapath,'/OriCavFeb2021/2021_02_16_extendedwindows_TSS_efficiencies/mm10/')

files=list.files(pathftss)

filesbed=files[grep('.bed', files)]
orisT=list()
for (f in filesbed){
oriname=sub('.bed|efficiencies_', '', f)
orisT[[oriname]] <- read.table(paste(pathftss,f, sep='/'), header = T, stringsAsFactors = F)
colnames(orisT[[oriname]])[c(4,5,6,7)]=paste(oriname, colnames(orisT[[oriname]][c(4,5,6,7)]), sep='_')
}


###start with 5kb ints
PPnetefl25=PPnetschroml25

net=PPnetefl25[['TSS_scores_5kb.df']]
res='5kb'

names(orisT)=gsub('mean_efficiency_', '', names(orisT))

for (o in names(orisT)[3]){ ## choose 5kb
net=load_features(net,orisT[[o]][,-c(4:6)],type='features_table',auxfun=mean, missingv=0, featname='')
}
colnames(net$features)=gsub('mean_efficiency_|_norm_background_corrected', '', colnames(net$features))

oribin=apply(net$features, c(1,2), function(x){
return(as.numeric(x>0))
})

colnames(oribin)=paste('bin',colnames(oribin))



net=load_features(net, oribin, type='features_on_nodes', missingv=0)

netbinchas=chas(net)
netbinfeat=export(net)

#rnet=chaser::randomize(net, nrandom=4, preserve.nodes = NULL,dist.match = T)

#save(rnet, file='/home/vera/dbpersonal/rnetefl25_25kb.Rdata')

#load(file='/home/vera/dbpersonal/rnetefl25_5kb.Rdata')
rnetfeat=lapply(rnet, export)

rnetchas=lapply(rnet, chas)

plot(colMeans(rnetfeat[[1]]),rnetchas[[1]], xlab='Mean efficiency at TSS  Bonev VPCHiC',     ylab='OriEfAs Bonev VPCHiC 5kb ', ylim=c(0,0.3))
for (i in 2:length(rnetchas)){
  points(colMeans(rnetfeat[[i]]),rnetchas[[i]])
}
points(colMeans(netbinfeat), netbinchas,  pch=20,col='red')
text(colMeans(netbinfeat), 0.1, labels=names(rnetchas[[1]]), pos=4, srt=90, cex=0.6)

abline(h=0)
```

Load original Ori bed files in mm10
```{r}
##For HiC OriNet need oris in mm10
files=list.files(paste0(datapath,'/mm10OrisOct19/means_mm10/'))

Pnet=PPnetefl25[['TSS_scores_5kb.df']]


filesbed=files[grep('.bed', files)]
oris=list()
for (f in filesbed){
oriname=sub('.bed', '', f)
oris[[oriname]] <- read.table(paste(datapath,'/mm10OrisOct19/means_mm10/',f, sep=''), header = T, stringsAsFactors = F)
colnames(oris[[oriname]])[c(4,5,6,7)]=paste(oriname, colnames(oris[[oriname]][c(4,5,6,7)]), sep='_')
}

orinames=lapply(oris, function(x){
  return(paste(x[,1], x[,2], x[,3], sep='_'))
  
})
###random
randwtmm10oris=list.files(paste0(datapath,'/mm10RandomTSSDistExp/random_efficiency_mm10/wt_all/'))



```

```{r}
orisl=list()
for (f in filesbed){
  oris <- read.table(paste0(datapath,'/mm10OrisOct19/means_mm10/',f, sep=''), header = T, stringsAsFactors = F)
oris=oris[!duplicated(paste(oris[,1], oris[,2], oris[,3])),]
 
    orisl[[f]]=oris
  
  Pnet <- load_features(Pnet, oris[-c(4:6)], type='features_table',missingv=0,featname=gsub('mean_efficiency','',f))
   
}

##calculate all oris
orisef=lapply(orisl, function(x){
  return(x[,-c(4:6)])
  
})
alloris=Reduce(rbind,orisef[c(2, 6, 12)])
colnames(alloris)=c('chr', 'start', 'end', 'All-Ori')
Pnet <- load_features(Pnet, alloris, type='features_table',missingv=0, auxfun=mean)

for (f in randwtmm10oris){
  print(f)
  oris <- read.table(paste0(datapath,'/mm10RandomTSSDistExp/random_efficiency_mm10/wt_all/',f), header = T, stringsAsFactors = F)
  
  Pnet <- load_features(Pnet, oris[-c(4:6)], type='features_table',missingv=0)
  
}

colnames(Pnet$features)=c(gsub('mean_efficiency_|_mm10.bed','',filesbed),'ALL-ORI', paste0('r', c(1:20)))

Pnetfeat=export(Pnet)

Pnetchas=chas(Pnet)

Pnetbin=Pnet
Pnetbin$features=apply(Pnetbin$features>0, c(1,2), as.numeric)

Pnetchr1=subset_chromnet(Pnet, chrom='chr1')

Pnetfeatchr1=export(Pnetchr1)
Pnetchaschr1=chas(Pnetchr1)

Pnetchr18=subset_chromnet(Pnet, chrom='chr18')

```

#####RANDOMIZATIONS VPCHiC
```{r}

########rnadomization Distance preserving netwt

###currently only chr1
nrand=50
#Pnet=subset_chromnet(Pnet, )

#drPnet=chaser::randomize(Pnet, nrandom=nrand, preserve.nodes = NULL,dist.match = T)  ##!!!!!!!!!!!!!!!!!!!!!!!!!

load('/media/zeus/Verbatim HDD/RepOriKarolina2021/RepOri3D/drPnet_rand50.Rdata')

drPnetfeat=lapply(drPnet, export)
drPnetchas=lapply(drPnet, chas)

###Calculate Zscore:

##flatten out random list
drPnetchasdf=as.data.frame(drPnetchas)
drPnetchasmean=rowMeans(drPnetchasdf)
drPnetchassd=apply(drPnetchasdf, 1, sd)


zsP=0
for (d in 1:length(Pnetchas)){
zsP[d]=round((Pnetchas[d]-drPnetchasmean[d])/drPnetchassd[d],2)
}
names(zsP)=names(Pnetchas)
```


PLOTS  VPCHiC
```{r}
#pdf('/home/vera/dbpersonal/Dropbox_work/RepOriKarolina/RevisionFigures/PCHiC_OriEfAsPP.pdf')

sel=colnames(Pnetfeat)[grep('exclusive|responsive|aph_const|cdc6_const|wt_const|both|r1|r2$|r3|r4|r5|r6|r7|r8|r9|r10',colnames(Pnetfeat), invert = T, ignore.case=T)]
labsel=sel
labsel=gsub('_MM10.BED','', toupper(labsel))
labsel[grep('^R', labsel)]<-''
labsel=gsub('CONSTITUTIVE', 'COMM', labsel)
print(sel)
colssel=cols[labsel]
colssel[6:length(colssel)]<-'purple'

pdf('/media/zeus/Verbatim HDD/RepOriKarolina2021/RepOri3D/VPCHiC_OriEfAs_cor.pdf')

plot(colMeans(Pnetfeat)[sel], Pnetchas[sel],  pch=21,cex=1.5, xlab='Efficiency averaged over network nodes', ylab='OriEfAs', cex.axis=1.5, cex.lab=1.5, ylim=c(-0.1,0.2), bg=colssel, col='black', main='VPCHiC')
#text(colMeans(Pnetfeat)[sel], Pnetchas[sel], labels=labsel, pos=4, srt=90, cex=1.5)

for (i in 1:nrand){
  points(jitter(colMeans(drPnetfeat[[i]])[sel], amount=1/60),drPnetchas[[i]][sel],pch=1, col=colssel, cex=3, lwd=0.5) 
}

text(colMeans(Pnetfeat)[sel[1:5]], rep(-0.1, 5), labels=round(zsP[sel[1:5]],0), pos=3, col=colssel[1:5])
text(colMeans(Pnetfeat)[sel[6]], rep(-0.1), labels=round(zsP[sel][6],0), pos=3)
#text(colMeans(Pnetfeat)[sel[16]], rep(-0.1), labels=round(mean(zsP[sel[17:25]]),0), pos=4, srt=90)

points( colMeans(Pnetfeat)[sel], Pnetchas[sel],  pch=21,cex=1.5)
abline(h=0)
dev.off()

```

```{r}
plot( ab[sel],chas[sel], pch=21, cex=1.5, xlab='Average Origin Efficiency', 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=2, lwd=0.5)
  }
 text(  abrand[sel],randchas[[i]][sel],chas[sel],labels='', pos=4,   srt=90)
##add Zscores
 print('nameszs')
 print(names(zs))
text(ab[sel][1:5], rep(-0.1, 5), labels=round(zs[sel][1:5],0), pos=4, srt=90, col=colssel[sel][1:5])
text(ab[sel][6], rep(-0.1), labels=round(mean(zs[sel][6:16]),0), pos=4, srt=90, col='black')
text(ab[sel][16], rep(-0.1), labels=round(mean(zs[sel][17:25]),0), pos=4, srt=90, col='black')
 points( ab[sel],chas[sel], pch=21, cex=1.5)


```

```{r}
##flatten out random list
drPnetbinchasdf=as.data.frame(drPnetbinchas)
drPnetbinchasmean=rowMeans(drPnetbinchasdf)
drPnetbinchassd=apply(drPnetbinchasdf, 1, sd)


zsPbin=0
for (d in 1:length(Pnetbinchas)){
zsPbin[d]=round((Pnetbinchas[d]-drPnetbinchasmean[d])/drPnetbinchassd[d],2)
}
names(zsPbin)=names(Pnetbinchas)

sel=colnames(Pnetfeat)[grep('exclusive|responsive|aph_const|cdc6_const|wt_const|both',colnames(Pnetfeat), invert = T, ignore.case=T)]
labsel=sel
labsel=gsub('_MM10.BED','', toupper(labsel))
labsel[grep('^R', labsel)]<-''
labsel=gsub('CONSTITUTIVE', 'COMM', labsel)

colssel=cols[labsel]
colssel[7:25]<-'yellow'

plot(colSums(drPnetbinfeat[[1]])[sel],drPnetbinchas[[1]][sel], xlab='Number of origins VPCHiC ',     ylab='OriAs VPCHiC ', ylim=c(-0.1,0.2), xlim=c(0,11500)) 
for (i in 2:nrand){
  points(colSums(drPnetbinfeat[[i]])[sel],drPnetbinchas[[i]][sel]) 
}
points(colSums(Pnetbinfeat)[sel], Pnetbinchas[sel],  pch=20,col=colssel)
text(colSums(Pnetbinfeat)[sel], Pnetbinchas[sel], labels=labsel, pos=4, srt=90)

text(colSums(Pnetbinfeat)[sel[1:5]], rep(-0.1, 5), labels=zsP[sel[1:5]], pos=4, srt=90)
text(colSums(Pnetbinfeat)[sel[6]], rep(-0.1), labels=round(mean(zsP[sel[6:16]]),2), pos=4, srt=90)
text(colSums(Pnetbinfeat)[sel[16]], rep(-0.1), labels=round(mean(zsP[sel[17:25]]),2), pos=4, srt=90)


abline(h=0)


```

```{r}
plotchas(Pnetfeat, Pnetchas, drPnetfeat,drPnetchas ,xmin=0, xmax=4.5, ymin=-0.1, ymax=0.2, labon='ON', title='VPCHiC', exclude='EXCL|RESP|APH_CONST|CDC6_CONST|WT_CONST|BOTH', 1)

```
```{r}

Pnetbiin=Pnet

Pnetbin$features=apply(Pnetbin$features>0, c(1,2), as.numeric)

Pnetbinchas=chas(Pnetbin)
Pnetbinfeat=export(Pnetbin)

drPnetbin=lapply(drPnet, function(x){
  x$features=apply(x$features>0, c(1,2), as.numeric)

return(x)  
})
                  
drPnetbinfeat=lapply(drPnetbin, export)

                         
drPnetbinchas=lapply(drPnetbin, chas)

plotchas(Pnetbinfeat, Pnetbinchas, drPnetbinfeat,drPnetbinchas ,xmin=0, xmax=4.5, ymin=-0.1, ymax=0.2, labon='ON', title='VPCHiC', exclude='EXCL|RESP|APH_CONST|CDC6_CONST|WT_CONST|BOTH', 1)

```


```{r}
##Define distance in VOCHIC nets

  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_PCHiC=distne(netwt)

dist_PCHiCPP=distne(netwt_bb)

##VPCHIC
dist_VPCHiC5kb=distne(PPnetefl25[[3]])
dist_VPCHiC25kb=distne(PPnetefl25[[2]])
dist_VPCHiC10kb=distne(PPnetefl25[[1]])

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(dist_VPCHiC10kb), na.rm=T), type='line', col='blue', lwd=3)
points(density(log10(dist_VPCHiC25kb), na.rm=T), type='line', col='purple', lwd=3)
#points(density(log10(dist_PCHiCPP), na.rm=T), type='line', col='green', lwd=3)
legend(3,0.8 ,lty=1, lwd=3,col=c('black', 'cyan', 'blue', 'purple', 'green'), legend=c('VPCHiC 5kb', 'PCHiC', 'VPCHiC10kb', 'VPCHiC25kb', 'PCHiC-PP'), bty="n", cex=1.5)


save(dist_VPCHiC5kb, dist_PCHiC, dist_PCHiCPP, file=paste0(datapath,'/distances.Rdata'))

```


Load TAD coordinates and map VPCHiC fragments to TADs
```{r Comparison with tads}
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', 'compart')

##annotate tad size
tadsize=tad
tadsize=cbind(tadsize, abs(tadsize$end-tadsize$start))
colnames(tadsize)=c('chr', 'start', 'end', 'compart', 'gensize')
write.table(cbind(rownames(tadsize), tadsize), 'TADsizeannot.txt', quote=F, sep='\t', row.names=F)

##subset PCHiC into orinet

VPCHiCoris=rownames(Pnet$features)[which(Pnet$features[,1]>0)]

VPCHiCorinet=subset_chromnet(Pnet, method="nodes", nodes1=VPCHiCoris)
## use chaser to assign features

orinet=VPCHiCorinet


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))
```

```{r}
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)



tadfrag[,1]=as.character(tadfrag[,1])



GVPCHiC=graph.data.frame(Pnet$edgesdf[,-c(1:6)], directed=F)


###study which promoters contain oris
boxplot(Pnetfeat[,'ALL-ORI']~ degree(GVPCHiC), outline=F)

 deg74high=names(which(degree(GVPCHiC)>74))
 deg74low=names(which(degree(GVPCHiC)<=74))
 
 noeff=names(which(Pnetfeat[,'ALL-ORI']==0))
 
 higheff=names(which(Pnetfeat[,'ALL-ORI']>4.5))
 
 summary(degree(GVPCHiC)[noeff])

summary(Pnetfeat[deg74high,'ALL-ORI'])
summary(Pnetfeat[setdiff(rownames(Pnetfeat),deg74high),'ALL-ORI'])

GVPCHiCorinet=graph.data.frame(VPCHiCorinet$edgesdf[,-c(1:6)], directed=F)

##INtratad info for cytoscape

```


Now study inter and intra-tad 

```{r}
#associate each int to intra or inter tad}

el=orinet$edgesdf[,c(7,8)]
rownames(el)=paste(el[,1], el[,2], sep='-')
#E(Gorinet)$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]),]


#GVPCHiC=graph.data.frame(netwt$edgesdf[,-c(1:6)], directed=F)

GVPCHiCorinet=graph.data.frame(VPCHiCorinet$edgesdf[,-c(1:6)], directed=F)

Gorinet=GVPCHiCorinet

E(Gorinet)$enames=rownames(el)

E(Gorinet)$intert=rep(0, length(E(Gorinet)))
E(Gorinet)$intert[which(E(Gorinet)$enames %in% rownames(intertads))]=1

intere=E(Gorinet)$enames[which(E(Gorinet)$enames %in% rownames(intertads))]
interchrom=E(Gorinet)$enames[which(is.na(E(Gorinet)$dist))]


```


Process data on interaction distance
```{r}

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)]

```

Plots about distance distributions
```{r}
pdf('DistVPCHiC.pdf')
plot(density(log10(distintertad[!is.na(distintertad)])), col='darkgreen',lwd=3,  xlab='Distance spanned (log10 bases)',  ylim=c(0,1), cex=1.5, cex.lab=1.5, cex.axis=1.5, main='')
points(density(log10(distintratad[!is.na(distintratad)])), type='l',lwd=3, col='cyan')
points(density(log10(distori[!is.na(dist)])), type='l',lwd=3, col='black')
legend(2,1 ,lty=1, lwd=3,col=c('black', 'cyan', 'darkgreen'), legend=c('All', 'Intra-TAD', 'Inter-TAD'), bty="n", cex=1.5)
dev.off()



```