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PaperFigures_revision_notebook_16jan.Rmd
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---
title: "Examples of ChAseR"
output: html_notebook
---
```{r}
## ----eval=FALSE----------------------------------------------------------
library(devtools)
devtools::install_bitbucket("eraineri/chaser", build_opts=c(), force=T)
library(chaser)
library(gplots)
library(ggplot2)
library(igraph)
library(pheatmap)
library(gtable)
library(vioplot)
##Define paths:
netpath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/BP/PCHiCdataBP'
featurepath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/BPfeatures'
figurespath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/Figures/revisionfigures'
nrand=30
```
```{r}
#######Set up PCHiC contact networks from Javierre et al. Cell 2016
bp=read.table(paste(netpath,'merged_samples_12Apr2015_full.txt', sep='/'), sep='\t', skip=4, header=T)
bp[,1]=paste('chr', bp[,1], sep='')
bp[,6]=paste('chr', bp[,6], sep='')
bpmono_forchaser=bp[which(bp$Monocytes>=5),c(1,2,3,6,7,8)]
bpneu_forchaser=bp[which(bp$Neutrophils>=5),c(1,2,3,6,7,8)]
bpt_forchaser=bp[which(rowMeans(bp[,c(21:26)])>=5),c(1,2,3,6,7,8)]
mononet=chaser::make_chromnet(bpmono_forchaser)
neunet=chaser::make_chromnet(bpneu_forchaser)
tnet=chaser::make_chromnet(bpt_forchaser)
```
Load histone modification data from Chen et al. 2016
```{r}
epivar_path='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Ngoc/Ngoc/epivar_data'
lf=list.files(epivar_path)
lf=lf[grep('bed', lf)]
lfpatients=sapply(lf, function(x){
return(unlist(strsplit(x, split='\\.'))[1])
})
info=read.table(paste(epivar_path,'Blueprint_Epivar_public_results_release.index', sep='/'), sep='\t', header=T)
neutros=as.vector(info$SAMPLE_NAME[which(info$CELL_TYPE=='mature neutrophil')])
monos=as.vector(info$SAMPLE_NAME[which(info$CELL_TYPE=='CD14-positive, CD16-negative classical monocyte')])
CD4T=as.vector(info$SAMPLE_NAME[which(info$CELL_TYPE=='CD4-positive, alpha-beta T cell')])
sampleslist=list(monos, neutros, CD4T)
names(sampleslist)=c('monos', 'neutros', 'Tcells')
colsc=c('red', 'blue', 'darkgreen')
names(colsc)=c('monos', 'neutros', 'Tcells')
###Load netbp with all features or load separately here
#j=0
#for (i in lf[sel]){
# print(paste('################################################',j))
#fi=paste(epivar_path,i, sep='/')
# namesvec=unlist(strsplit(i, split='\\.'))
# br=grep('broad',i)>0
# names=paste(namesvec[1],namesvec[2],as.numeric(br), sep='_')
# netbp <- load_features(netbp,fi,type='macs2',featname = names, missingv=0)
# j=j+1
#}
#saveRDS(netbp, file = "/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/BPfeatures/netbp_chenfeats.rds")
netbp=readRDS('/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/BPfeatures/netbp_chenfeats.rds')
netbpfeat=export(netbp)
samples=sapply(colnames(netbpfeat), function(x){
return(unlist(strsplit(x, split='_'))[1])
})
```
Make chromnets with histone modification features
```{r}
#########################
#########################
#########################
#########################
###Neutrophils
chromneunet=chaser::make_chromnet(bpneu_forchaser, features=netbpfeat)
#extract promoter bait nodes
baits <- export(chromneunet, 'baits')
# extract other ends
nodes=export(chromneunet, "nodes")$name
oes=nodes[-which(nodes %in% baits)]
chromneunet_bb<-chaser::subset_chromnet(chromneunet, method="nodes", nodes1=baits)
chromneunet_bbfeat=export(chromneunet_bb)
chromneunet_bbchas=chas(chromneunet_bb)
chromneunet_bo<-chaser::subset_chromnet(chromneunet, method="nodes", nodes1=baits, nodes2=oes)
chromneunet_bofeat=export(chromneunet_bo)
chromneunet_bochas=chas(chromneunet_bo)
```
```{r}
###Monocytes
chrommononet=chaser::make_chromnet(bpmono_forchaser, features=netbpfeat)
#extract promoter bait nodes
baits <- export(chrommononet, 'baits')
# extract other ends
nodes=export(chrommononet, "nodes")$name
oes=nodes[-which(nodes %in% baits)]
chrommononet_bb<-chaser::subset_chromnet(chrommononet, method="nodes", nodes1=baits)
chrommononet_bbfeat=export(chrommononet_bb)
chrommononet_bbchas=chas(chrommononet_bb)
chrommononet_bo<-chaser::subset_chromnet(chrommononet, method="nodes", nodes1=baits, nodes2=oes)
chrommononet_bofeat=export(chrommononet_bo)
chrommononet_bochas=chas(chrommononet_bo)
```
```{r}
###Tcells
chromtnet=chaser::make_chromnet(bpt_forchaser, features=netbpfeat)
#extract promoter bait nodes
baits <- export(chromtnet, 'baits')
# extract other ends
nodes=export(chromtnet, "nodes")$name
oes=nodes[-which(nodes %in% baits)]
chromtnet_bb<-chaser::subset_chromnet(chromtnet, method="nodes", nodes1=baits)
chromtnet_bbfeat=export(chromtnet_bb)
chromtnet_bbchas=chas(chromtnet_bb)
chromtnet_bo<-chaser::subset_chromnet(chromtnet, method="nodes", nodes1=baits, nodes2=oes)
chromtnet_bofeat=export(chromtnet_bo)
chromtnet_bochas=chas(chromtnet_bo)
```
Plots ChAs PO vs PP
```{r}
##Plot Neutrophils
#svg(paste(figurespath,'HistmodsNeutros_ChAsPO_vs_PP.svg', sep='/'), width=8, height= 8)
par(mfrow=c(2,2))
for (h in c( 'H3K27me3','H3K4me1','H3K4me3', 'H3K27ac')){
print(h)
sel=intersect( grep(h, colnames(chromneunet_bofeat)), which(samples %in% sampleslist[['neutros']]))
# sel1=sel[grep('S00H5QH1',sel)]
# print(names(neunet_bochas[sel[sel1]]))
print(length(sel))
if (length(sel)>0){
plot(chromneunet_bbchas[sel],chromneunet_bochas[sel], pch=21,bg='blue', xlab='ChAs PP', ylab='ChAs PO', main=h, xlim=c(0, 0.5), ylim=c(-0.3,0.6), cex.axis=1.2 )
abline(a=0, b=1)
abline(h=0)
}
}
#dev.off()
```
```{r}
#Plot Moncytes and Tcells
#svg(paste(figurespath,'Suppfigs/HistmodsMonoTcells_ChAsPO_vs_PP.svg', sep='/'), width=8, height= 8)
#png(paste(figurespath,'Suppfigs/HistmodsMonoTcells_ChAsPO_vs_PP.png', sep='/'))
par(mfrow=c(2,2))
for (h in c( 'H3K4me1', 'H3K27ac')){
print(h)
sel=intersect( grep(h, colnames(chrommononet_bofeat)), which(samples %in% sampleslist[['monos']]))
print(length(sel))
if (length(sel)>0){
plot(chrommononet_bbchas[sel],chrommononet_bochas[sel], pch=21,bg='red', xlab='ChAs PP', ylab='ChAs PO', main=paste('Monocytes ',h), xlim=c(0.1, 0.3), ylim=c(-0.1,0.3), cex.axis=1.2)
abline(a=0, b=1)
abline(h=0)
}
}
for (h in c( 'H3K4me1', 'H3K27ac')){
print(h)
sel=intersect( grep(h, colnames(chrommononet_bofeat)), which(samples %in% sampleslist[['Tcells']]))
print(length(sel))
if (length(sel)>0){
plot(chromtnet_bbchas[sel],chromtnet_bochas[sel], pch=21,bg='darkgreen', xlab='ChAs PP', ylab='ChAs PO', main=paste('Tcells ',h), xlim=c(0, 0.3), ylim=c(-0.1,0.3), cex.axis=1.2)
abline(a=0, b=1)
abline(h=0)
}
}
#dev.off()
```
Plots ChAs vs abundance
```{r}
#svg(paste(figurespath,'SuppFigs/HistmodsNeutros_ChAsvsAb_PP.svg', sep='/'), width=8, height= 8)
#png(paste(figurespath,'SuppFigs/HistmodsNeutros_ChAsvsAb_PP.png', sep='/'))
par(mfrow=c(2,2))
for (h in c( 'H3K27me3','H3K4me1','H3K4me3', 'H3K27ac')){
print(h)
sel=intersect( grep(h, colnames(chromneunet_bofeat)), which(samples %in% sampleslist[['neutros']]))
# sel1=sel[grep('S00H5QH1',sel)]
# print(names(neunet_bochas[sel[sel1]]))
print(length(sel))
if (length(sel)>0){
plot(chromneunet_bbfeat[sel],chromneunet_bbchas[sel], pch=21,bg='blue', xlab='Abundance PP', ylab='ChAs PP', main=h, xlim=c(0,0.6), cex.axis=1.2 )
}
}
#dev.off()
```
Randomizations of histone mods
```{r}
###Randomization
rchromneunet_bb <- chaser::randomize(chromneunet_bb, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rchromneunet_bbchas <-lapply(rchromneunet_bb, chas)
rchromneunet_bbfeat <-lapply(rchromneunet_bb, export)
```
```{r}
#svg(paste(figurespath,'Histmods_NeuRandDist_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Histmods_NeuRandDist_abchas.png', sep='/'))
par(mfrow=c(2,2))
for (h in c( 'H3K27me3','H3K4me1','H3K4me3', 'H3K27ac')){
print(h)
sel=intersect( grep(h, colnames(chromneunet_bofeat)), which(samples %in% sampleslist[['neutros']]))
# sel1=sel[grep('S00H5QH1',sel)]
# print(names(neunet_bochas[sel[sel1]]))
print(length(sel))
mycols=rainbow(length(sel))
if (length(sel)>0){
plot(colMeans(rchromneunet_bbfeat[[1]])[sel],rchromneunet_bbchas[[1]][sel], pch='.', xlim=c(0,1), ylim=c(-0.1,0.5), col=mycols, xlab='Mean Abundance PP',
ylab='ChAs PP ', main=paste('Neutrophils ',h))
for (i in 2:nrand){
points(colMeans(rchromneunet_bbfeat[[i]])[sel],rchromneunet_bbchas[[i]][sel], pch='.',col=mycols)
}
points(colMeans(chromneunet_bbfeat)[sel], chromneunet_bbchas[sel], pch=20, col=mycols)
}
}
#dev.off()
#svg(paste(figurespath,'H3K27ac_NeuRandDist_abchas.svg', sep='/'))
plot(colMeans(rchromneunet_bbfeat[[1]])[sel],rchromneunet_bbchas[[1]][sel], pch='.', xlim=c(0.1,0.4), ylim=c(-0.05,0.35), col=mycols, xlab='Mean Abundance PP',
ylab='ChAs PP ', main=paste('Neutrophils ',h))
for (i in 2:nrand){
points(colMeans(rchromneunet_bbfeat[[i]])[sel],rchromneunet_bbchas[[i]][sel], pch='.',col=mycols)
}
points(colMeans(chromneunet_bbfeat)[sel], chromneunet_bbchas[sel], pch=20, col=mycols)
#dev.off()
```
```{r}
###Single individual plot
#S010B0H1
selind=c('S010B0H1_NS1110_H3K4me1_1', 'S010B0H1_NS1111_H3K27ac_', 'S010B0H1_NS1112_H3K4me3_', 'S010B0H1_NS1113_H3K27me3_1')
lab=sapply(selind, function(x){
return(unlist(strsplit(x, split='_'))[3])
})
#svg(paste(figurespath,'Individual_abchasPP.svg', sep='/'))
plot(colMeans(chromneunet_bbfeat[,selind]),chromneunet_bbchas[selind], pch=21,bg='blue', xlab='Abundance PP', ylab='ChAs PP', main='Individual', xlim=c(0.1, 0.5), ylim=c(-0.05,0.4), cex.axis=1.2 )
text(colMeans(chromneunet_bbfeat[,selind]),chromneunet_bbchas[selind],labels=lab, cex=1, pos=2)
for (i in 2:nrand){
points(colMeans(rchromneunet_bbfeat[[i]])[selind],rchromneunet_bbchas[[i]][selind], col='grey')
}
#dev.off()
#svg(paste(figurespath,'Individual_abchasPOvsPP.svg', sep='/'))
plot(chromneunet_bbchas[selind],chromneunet_bochas[selind], pch=21,bg='blue', xlab='ChAs PP', ylab='ChAs PO', main=h, xlim=c(0.15, 0.35), ylim=c(-0.2,0.3), cex.axis=1.2 )
text(chromneunet_bbchas[selind],chromneunet_bochas[selind],labels=lab, cex=1, pos=2)
abline(a=0, b=1)
abline(h=0)
#dev.off()
```
Histmods Figure
```{r}
#svg(paste(figurespath,'Histmodsneutro.svg', sep='/'))
par(mfrow=c(2,2))
plot(colMeans(chromneunet_bbfeat[,selind]),chromneunet_bbchas[selind], pch=21,bg='blue', xlab='Abundance PP', ylab='ChAs PP', , xlim=c(0.1, 0.5), ylim=c(-0.05,0.4), cex.axis=1.2 )
text(colMeans(chromneunet_bbfeat[,selind]),chromneunet_bbchas[selind],labels=lab, cex=1, pos=2)
for (i in 2:nrand){
points(colMeans(rchromneunet_bbfeat[[i]])[selind],rchromneunet_bbchas[[i]][selind], col='grey')
}
plot(chromneunet_bbchas[selind],chromneunet_bochas[selind], pch=21,bg='blue', xlab='ChAs PP', ylab='ChAs PO', xlim=c(0, 0.35), ylim=c(-0.2,0.3), cex.axis=1.2 )
text(chromneunet_bbchas[selind],chromneunet_bochas[selind],labels=lab, cex=1, pos=2)
abline(a=0, b=1)
abline(h=0)
sel=intersect( grep('H3K27ac', colnames(chromneunet_bofeat)), which(samples %in% sampleslist[['neutros']]))
# sel1=sel[grep('S00H5QH1',sel)]
# print(names(neunet_bochas[sel[sel1]]))
print(length(sel))
if (length(sel)>0){
plot(chromneunet_bbchas[sel],chromneunet_bochas[sel], pch=21,bg='blue', xlab='ChAs PP', ylab='ChAs PO', xlim=c(0, 0.35), ylim=c(-0.3,0.6), cex.axis=1.2 )
abline(a=0, b=1)
abline(h=0)
}
plot(colMeans(rchromneunet_bbfeat[[1]])[sel],rchromneunet_bbchas[[1]][sel], pch='.', xlim=c(0.1,0.5), ylim=c(-0.05,0.35), col=mycols, xlab='Mean Abundance PP',
ylab='ChAs PP ')
for (i in 2:nrand){
points(colMeans(rchromneunet_bbfeat[[i]])[sel],rchromneunet_bbchas[[i]][sel], pch='.',col=mycols)
}
points(colMeans(chromneunet_bbfeat)[sel], chromneunet_bbchas[sel], pch=20, col=mycols)
#dev.off()
```
Load methylation features from EPIVAR, Chen et al. Cell 2016
```{r}
metdatapath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/BPfeatures/'
###Monocytes
metmono<-readRDS(paste(metdatapath,'monocytes-meth-feature-table.rds', sep='/'))
netmetmono<-chaser::make_chromnet(bpmono_forchaser)
netmetmono<-chaser::load_features(netmetmono, metmono,auxfun='mean', type='features_table', missingv=NA, featname=colnames(metmono))
netmetmonocomp<-subset_chromnet(netmetmono, 'complete')
netmetmonocomp_chas=chas(netmetmonocomp)
#extract promoter bait nodes
baits <- export(netmetmono, 'baits')
# extract other ends
nodes=export(netmetmono, "nodes")$name
oes=nodes[!which(nodes %in% baits)]
#write.table(rowMeans(metemamono),paste(featurepath, 'EPIVARMethylationMono.txt', sep='/'), quote=F, sep='\t')
netmetmono_bb<- chaser::subset_chromnet(netmetmono, method="nodes", nodes1=baits)
##Remove from network the nodes that do not have features
netmetmono_bbcomp<-subset_chromnet(netmetmono_bb, 'complete')
netmetmono_bbchas=chas(netmetmono_bbcomp)
netmetmono_bbfeat=export(netmetmono_bbcomp)
###Neutrophils
metneu<-readRDS(paste(metdatapath,'neutrophils-meth-feature-table.rds', sep='/'))
netmetneu<-chaser::make_chromnet(bpneu_forchaser)
netmetneu<-chaser::load_features(netmetneu, metneu, auxfun='mean', type='features_table', missingv=NA, featname=colnames(metneu))
#extract promoter bait nodes
baits <- export(netmetneu, 'baits')
# extract other ends
nodes=export(netmetneu, "nodes")$name
oes=nodes[-which(nodes %in% baits)]
#write.table(rowMeans(metemaneu), paste(featurepath, 'EPIVARMethylationneu.txt', sep='/'), quote=F, sep='\t')
netmetneu_bb<- chaser::subset_chromnet(netmetneu, method="nodes", nodes1=baits)
netmetneu_bbcomp=subset_chromnet(netmetneu_bb, 'complete')
netmetneu_bbchas=chas(netmetneu_bbcomp)
netmetneu_bbfeat=export(netmetneu_bbcomp)
```
Randomization of methylation values preserving distance (Figure 2E)
```{r}
##Randomization on neutrophil network
rnetmetneu_bbcomp <- chaser::randomize(netmetneu_bbcomp, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rnetmetneu_bbchas <-lapply(rnetmetneu_bbcomp, chas)
rnetmetneu_bbfeat=lapply(rnetmetneu_bbcomp, export)
```
```{r}
mycols=rainbow(ncol(netmetneu_bbfeat))
#svg(paste(figurespath, 'Epivar_Met_NeuRandDist_abchas.svg', sep='/'))
plot(colMeans(rnetmetneu_bbfeat[[1]]),rnetmetneu_bbchas[[1]], pch='.', ylim=c(0.0,0.23),xlim=c(0.34, 0.37), col=mycols, xlab='Mean methylation Neutrophils', ylab='ChAs on PP network')
for (i in 2:nrand){
points(colMeans(rnetmetneu_bbfeat[[i]]),rnetmetneu_bbchas[[i]], pch='.',col=mycols)
}
points(colMeans(netmetneu_bbfeat), netmetneu_bbchas, pch=20, col=mycols)
#dev.off()
```
More detailed analyses on a single individual (Figures 2F-G)
```{r}
library(ggplot2)
library(ggExtra)
##Neutrophils
#export scatterplot for 1 individual
netmetneu_bbscatter=export(netmetneu_bbcomp, type='scatterplot', featname='S000GZ')
p<-ggplot(data = netmetneu_bbscatter, aes(fi, fj)) + stat_density2d(aes(fill = ..density..^0.25), geom = "tile", contour = FALSE, n = 200) + scale_fill_continuous(low = "white", high = "dodgerblue4") +theme_classic() +geom_density_2d(colour='black')+ xlab('Methylation on node i')+ylab('Methylation on node j')
plot(p)
ggsave(paste(figurespath, 'SuppFigs/Methylation_neutrophil_onesubject.pdf', sep='/'), height=5, width=6)
##Produce violin plot for 1 individual
netmetneubbcorr <- chaser::chas(netmetneu_bbcomp, method="corr_fun")
netmetneubballcorr <- chaser::chas(netmetneu_bb, method="corr_fun")
netmetneu_bballfeat=export(netmetneu_bb)
cf<- cut(netmetneu_bbfeat[,'S000GZ'], seq(0,1,by=0.25), include.lowest=T)
y <- netmetneubbcorr[,'S000GZ']
daf <- data.frame(y=y, Methylation=cf)
library(Hmisc)
p <- ggplot2::ggplot(daf, aes(Methylation, y))
p <- p + ggplot2::geom_violin()
p <- p + theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position='none',
axis.text.x=element_text(size=16),
axis.text.y=element_text(size=16),
axis.title.x=element_text(size=16),
axis.title.y=element_text(size=16))
p <- p + xlab("Methylation at a promoter bait")
p <- p + ylab("Average methylation at neighbouring promoters")
p<-p + stat_summary(fun.data=mean_sdl, geom="pointrange", color="black")
plot(p)
ggplot2::ggsave(paste(figurespath, 'Methylation_Neutrophil_1subject_correVioplot.pdf', sep='/'))
```
```{r}
##Neutrophils
#export scatterplot for 1 individual
chromneunet_bbscatter=export(chromneunet_bb, type='scatterplot', featname='S00H5QH1_NS502_H3K4me3_')
colnames(chromneunet_bbscatter)[9]='node1'
colnames(chromneunet_bbscatter)[10]='node2'
smoothScatter(chromneunet_bbscatter[,'node1'], chromneunet_bbscatter[,'node2'])
#ggsave(paste(figurespath, 'Methylation_neutrophil_onesubject.svg', sep='/'), height=5, width=6)
##Produce violin plot for 1 individual
netmetneubbcorr <- chaser::chas(netmetneu_bbcomp, method="corr_fun")
netmetneubballcorr <- chaser::chas(netmetneu_bb, method="corr_fun")
netmetneu_bballfeat=export(netmetneu_bb)
cf<- cut(netmetneu_bbfeat[,'S000GZ'], seq(0,1,by=0.25), include.lowest=T)
y <- netmetneubbcorr[,'S000GZ']
daf <- data.frame(y=y, Methylation=cf)
library(Hmisc)
p <- ggplot2::ggplot(daf, aes(Methylation, y, fill='blue'))
p <- p + ggplot2::geom_violin()
p <- p + theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position='none',
axis.text.x=element_text(size=12),
axis.text.y=element_text(size=12),
axis.title.x=element_text(size=14),
axis.title.y=element_text(size=14))
p <- p + xlab("H3K4me1 at promoter bait")
p <- p + ylab("Average H3K4me1 at neighbouring promoters")
p<-p + stat_summary(fun.data=mean_sdl, geom="pointrange", color="black")
plot(p)
```
Expression Data
```{r}
expdatapath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Ngoc/Ngoc/epivar_data/Expression/'
biomartpath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/common/forGARDEN-NET/biomart/'
biomartpath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/common/forGARDEN-NET/biomart/'
monoexp=read.table(paste(expdatapath,'mono_gene_nor_combat_20151109.txt_sort', sep='/'), sep='\t', header=T)
neutroexp=read.table(paste(expdatapath,'neut_gene_nor_combat_20151109.txt_sort', sep='/'), sep='\t', header=T)
texp=read.table(paste(expdatapath,'tcel_gene_nor_combat_20151109.txt_sort',sep='/'), sep='\t', header=T)
## Read expression data and process it to assign expression to all bait fragments
conv=read.delim(paste(biomartpath,'BLUEPRINT_fragments_good.tsv', sep='/'), sep='\t')
monoexp_pro=monoexp
monoexp_pro[,1]=sapply(as.vector(monoexp_pro[,1]),function(x){
res=x
if (grep('\\.', x)){
vec=unlist(strsplit(x, split='\\.'))
res=vec[1]
}
return(res)
})
neuexp_pro=neutroexp
neuexp_pro[,1]=sapply(as.vector(neuexp_pro[,1]),function(x){
res=x
if (grep('\\.', x)){
vec=unlist(strsplit(x, split='\\.'))
res=vec[1]
}
return(res)
})
texp_pro=texp
texp_pro[,1]=sapply(as.vector(texp_pro[,1]),function(x){
res=x
if (grep('\\.', x)){
vec=unlist(strsplit(x, split='\\.'))
res=vec[1]
}
return(res)
})
rownames(monoexp_pro)=monoexp_pro[,1]
rownames(neuexp_pro)=neuexp_pro[,1]
rownames(texp_pro)=texp_pro[,1]
convpro=data.frame(paste('chr',conv$chr, ':', conv$start, '-', conv$end, sep=''), conv$ensembl)
rownames(convpro)=convpro[,1]
colnames(convpro)[2]='ens.id'
m=merge( convpro,monoexp_pro, by='ens.id', all.x=F, all.y=F)
rownames(m)=m[,2]
m=m[,-c(1,2)]
m=data.frame(m, rowMeans(m))
n=merge( convpro,neuexp_pro, by='ens.id', all.x=F, all.y=F)
rownames(n)=n[,2]
n=n[,-c(1,2)]
n=data.frame(n, rowMeans(n))
t=merge( convpro,texp_pro, by='ens.id', all.x=F, all.y=F)
rownames(t)=t[,2]
t=t[,-c(1,2)]
t=data.frame(t, rowMeans(t))
##Extract PP subnetworks
mononetbb <- chaser::subset_chromnet(mononet, method="nodes", nodes1=baits)
neunetbb <- chaser::subset_chromnet(neunet, method="nodes", nodes1=baits)
tnetbb <- chaser::subset_chromnet(tnet, method="nodes", nodes1=baits)
##Add features
mononetbbexp <- load_features(mononetbb,m,type='features_on_nodes',featname = colnames(m), missingv=0)
mononetbbexpchas=chas(mononetbbexp)
mononetbbexpfeat=export(mononetbbexp)
neunetbbexp <- load_features(neunetbb,m,type='features_on_nodes',featname = colnames(m), missingv=0)
neunetbbexpchas=chas(neunetbbexp)
neunetbbexpfeat=export(neunetbbexp)
tnetbbexp <- load_features(tnetbb,m,type='features_on_nodes',featname = colnames(m), missingv=0)
tnetbbexpchas=chas(tnetbbexp)
tnetbbexpfeat=export(tnetbbexp)
Gneunetbb=export(neunetbb, 'igraph')
#write.table(get.edgelist(Gneunetbb), paste(figurespath, 'neunet.txt',sep='/'), quote=F, sep='\t', row.names=F)
#write.table(data.frame(rownames(neunetbbexpfeat),neunetbbexpfeat[,'rowMeans.m.']),paste(figurespath, 'NeutrophilPP_Epivar.txt', sep='/'), row.names=F, quote=F)
```
```{r}
##Produce violin plot for 1 individual expression
neunetexpbbcorr <- chaser::chas(neunetbbexp, method="corr_fun")
neunetexpbbfeat=export(neunetbbexp)
expvals=neunetexpbbfeat[complete.cases(neunetexpbbfeat[,'S000GZ']),'S000GZ']
cf=cut(expvals, seq(min(expvals),max(expvals),by=4), include.lowest=T)
#cf<- cut(neunetexpbbfeat[,'S000GZ'], seq(min(neunetexpbbfeat[,'S000GZ']),17,by=3), include.lowest=T)
y <- neunetexpbbcorr[complete.cases(neunetexpbbcorr),'S000GZ']
daf <- data.frame(y=y,Expression=cf)
p <- ggplot2::ggplot(daf, aes(Expression, y))
p <- p + ggplot2::geom_violin()
p <- p + theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position='none',
axis.text.x=element_text(size=16),
axis.text.y=element_text(size=16),
axis.title.x=element_text(size=16),
axis.title.y=element_text(size=16))
p <- p + xlab("Expression")
p <- p + ylab("Average expression at neighbouring promoters")
p<-p + stat_summary(fun.data=mean_sdl, geom="pointrange", color="black")
plot(p)
ggplot2::ggsave(paste(figurespath, 'Expression_Neutrophil_1subject_correVioplot.pdf', sep='/'))
```
Expression supplementary figures showing ChAs vs abundance in 3 cell types
```{r}
#svg(paste(figurespath,'SuppFigs/Epivar_Monoexp_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_Monoexp_abchas.png', sep='/'))
plot(colMeans(mononetbbexpfeat), mononetbbexpchas, xlab="exp ab",
ylab='chas PP', axes=FALSE, pch=20, main='Monocytes', col='red')
axis(1)
axis(2)
#dev.off()
#svg(paste(figurespath,'SuppFigs/Epivar_Neuexp_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_Neuexp_abchas.png', sep='/'))
plot(colMeans(neunetbbexpfeat), neunetbbexpchas, xlab="exp ab",
ylab='chas PP', pch=20, col='blue', main='Neutrophils')
axis(1)
axis(2)
#dev.off()
#svg(paste(figurespath,'SuppFigs/Epivar_Tcellexp_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_Tcellexp_abchas.png', sep='/'))
plot(colMeans(tnetbbexpfeat), tnetbbexpchas, xlab="exp ab",
ylab='chas PP', pch=20, col='darkgreen', main='Tcells')
axis(1)
axis(2)
#dev.off()
```
Randomizations preserving distance Figure 2H
```{r}
###Neutrophils
rneuexp <- chaser::randomize(neunetbbexp, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rneuexp_chas <-lapply(rneuexp, chas)
rneuexp_feat=lapply(rneuexp, export)
```
```{r}
mycols=rainbow(ncol(neunetbbexpfeat))
#svg(paste(figurespath,'Epivar_Neuexp_RandDist_abchas.svg', sep='/'))
plot(colMeans(rneuexp_feat[[1]]),rneuexp_chas[[1]], pch='.', ylim=c(0.02,0.13), xlim=c(2.9,3),col=mycols, xlab='Mean expression Neutrophils', ylab='ChAs on PP network')
for (i in 2:nrand){
points(colMeans(rneuexp_feat[[i]]),rneuexp_chas[[i]], pch='.',col=mycols)
}
points(colMeans(neunetbbexpfeat), neunetbbexpchas, pch=20, col=mycols)
#dev.off()
```
Randomization of expression for monocytes and tcells preserving distances (Supp Figures S3)
```{r}
##Monocytes
rmonoexp <- chaser::randomize(mononetbbexp, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rmonoexp_chas <-lapply(rmonoexp, chas)
rmonoexp_feat=lapply(rmonoexp, export)
```
```{r}
mycols=rainbow(ncol(mononetbbexpfeat))
#svg(paste(figurespath,'SuppFigs/Epivar_exp_MonoRandDist_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_exp_MonoRandDist_abchas.png', sep='/'))
plot(colMeans(rmonoexp_feat[[1]]),rmonoexp_chas[[1]], pch='.', ylim=c(0.02,0.13),xlim=c(3.,3.2), col=mycols, xlab='Mean expression Monocytes', ylab='ChAs on PP network')
for (i in 2:nrand){
points(colMeans(rmonoexp_feat[[i]]),rmonoexp_chas[[i]], pch='.',col=mycols)
}
points(colMeans(mononetbbexpfeat), mononetbbexpchas, pch=20, col=mycols)
#dev.off()
```
```{r}
###Tcells
rtexp <- chaser::randomize(tnetbbexp, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rtexp_chas <-lapply(rtexp, chas)
rtexp_feat=lapply(rtexp, export)
```
```{r}
mycols=rainbow(ncol(tnetbbexpfeat))
#svg(paste(figurespath,'SuppFigs/Epivar_exp_TcellRandDist_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_exp_TcellRandDist_abchas.png', sep='/'))
plot(colMeans(rtexp_feat[[1]]),rtexp_chas[[1]], pch='.', ylim=c(0.02,0.13),xlim=c(3., 3.2), col=mycols, xlab='Mean expression Tcells', ylab='ChAs on PP network')
for (i in 2:nrand){
points(colMeans(rtexp_feat[[i]]),rtexp_chas[[i]], pch='.',col=mycols)
}
points(colMeans(tnetbbexpfeat), tnetbbexpchas, pch=20, col=mycols)
#dev.off()
```
Randomization not preserving distance
```{r}
###Randomization not preserving distance
###Now do randomization with permutations, not preserving ditance
rpneuexp <- chaser::randomize(neunetbbexp, nrandom=100, preserve.nodes = NULL,dist.match = F)
rpneuexp_chas <-lapply(rpneuexp, chas)
rpneuexp_feat=lapply(rpneuexp, export)
mycols=rainbow(ncol(neunetbbexpfeat))
```
```{r}
#svg(paste(figurespath,'SuppFigs/Epivar_exp_neuRand_abchas.svg', sep='/'))
#png(paste(figurespath,'SuppFigs/Epivar_exp_neuRand_abchas.png', sep='/'))
plot(colMeans(rpneuexp_feat[[1]]),rpneuexp_chas[[1]], pch='.', col=mycols, ylim=c(-0.05,0.15),xlim=c(2.9, 3), xlab='Mean expression neutrophils', ylab='ChAs on PP network', axes=F)
for (i in 2:nrand){
points(colMeans(rpneuexp_feat[[i]]),rpneuexp_chas[[i]], pch='.', col=mycols)
}
points(colMeans(neunetbbexpfeat), neunetbbexpchas, pch=21, bg=mycols)
axis(1)
axis(2)
#dev.off()
```
Chipseq data from Beekman et al. on BP Bcells net
```{r}
bpB=bp[c(which(bp$Naive_B>5),which(bp$Total_B>5)), ]
netbpB=chaser::make_chromnet(bpB[,c(1,2,3,6,7,8)])
GbpB=export(netbpB, 'igraph')
#write.table(get.edgelist(GbpB), paste(figurespath, 'Bnet.txt',sep='/'), quote=F, sep='\t', row.names=F)
Beekpath='/mnt/SERVER-CRCT-STORAGE/CRCT21/Private/Common/forGARDEN-NET/Beekman2018_BcellsEpigenome/converted_to_hg19'
fs=list.files(Beekpath)
for (f in fs ){
featn=gsub('.vst.txt', '',f)
beek=read.table(paste(Beekpath,f,sep='/'), header=T)
rownames(beek)=beek[,1]
beek=beek[,-1]
if( length(grep('pval|FDR|CLL',colnames(beek)))>0){
beek=beek[grep('pval|FDR|CLL', colnames(beek), invert = T)]
}
beekprocnew=data.frame(beek[,c(1,2,3)], rowMeans(beek[,c(4:ncol(beek))]))
rownames(beekprocnew)=NULL
colnames(beekprocnew)[4]=featn
netbpB=load_features(netbpB, beekprocnew, type='features_table',featnames=featn,auxfun=mean, missingv=0)
}
netbpB_chas=chas(netbpB)
###split PP and PO
baitsB <- unique(chaser::export(netbpB, "edges")$node_from)
##extract other ends
tmp <- unique(chaser::export(netbpB, "edges")$node_to)
oesB <- tmp[!(tmp %in% baitsB)]
netbpB_bb<- chaser::subset_chromnet(netbpB, method="nodes", nodes1=baitsB)
##Remove from network the nodes that do not have features
netbpB_bbchas=chas(netbpB_bb)
netbpB_bbfeat=export(netbpB_bb)
netbpB_bo<- chaser::subset_chromnet(netbpB, method="nodes", nodes1=baitsB, nodes2=oesB)
netbpB_bochas=chas(netbpB_bo)
netbpB_bofeat=export(netbpB_bo)
netbpB_feat=export(netbpB)
colnames(netbpB_feat)=gsub('.vst.txt', '',fs)
netbpB_chas=chas(netbpB)
names(netbpB_chas)=gsub('.vst.txt', '',fs)
```
```{r}
rnetbpB=randomize(netbpB,nrandom=nrand, preserve.nodes=NULL, dist.match=T)
rnetbpB_feat=lapply(rnetbpB, export)
rnetbpB_chas=lapply(rnetbpB, chas)
rnetbpB_bb=chaser::randomize(netbpB_bb, nrandom=nrand, preserve.nodes = NULL,dist.match = T)
rnetbpB_bbfeat=lapply(rnetbpB_bb, export)
rnetbpB_bbchas=lapply(rnetbpB_bb, chas)
```
```{r}
netbpB_crosschas=chas(netbpB, method='crosschas')
colnames(netbpB_crosschas)=gsub('.vst.txt', '',fs)
rownames(netbpB_crosschas)=colnames(netbpB_crosschas)
netbpB_bbcrosschas=chas(netbpB_bb, method='crosschas')
netbpB_bocrosschas=chas(netbpB_bo, method='crosschas')
netbpB_bbandchas=chaser::chas(netbpB_bb, method="and")
netbpB_boandchas=chaser::chas(netbpB_bo, method="and")
```
```{r}
rnetbpB_bbcrosschas=lapply(rnetbpB_bb, chas, method='crosschas')
rnetbpB_bbcrosschasfin=Reduce('+', rnetbpB_bbcrosschas)/nrand
rnetbpB_bbandchas=lapply(rnetbpB_bb, chas, method='and')
rnetbpB_bbandchasfin=Reduce('+', rnetbpB_bbandchas)/nrand
```
```{r}
library(RColorBrewer)
# Sets the minimum (0), the maximum (15), and the increasing steps (+1) for the color scale
# Note: if some of your genes are outside of this range, they will appear white on the heatmap
breaksList = seq(-0.3, 0.4, by = 0.1)
par(mfrow=c(2,4))
#pdf(paste(figurespath,'BeekBcellPCHiC_crosschasPP.pdf', sep='/'))
pheatmap(netbpB_bbcrosschas,display_numbers = T, main='PP cross', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_andchasPP.pdf', sep='/'))
pheatmap(netbpB_bbandchas,display_numbers = T, main='PP AND', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_crosschasPO.pdf', sep='/'))
pheatmap(netbpB_bocrosschas,display_numbers = T, main='PO cross', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_andchasPO.pdf', sep='/'))
pheatmap(netbpB_boandchas,display_numbers = T, main='PO AND', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_crosschasPPrandom.pdf', sep='/'))
pheatmap(rnetbpB_bbcrosschasfin,display_numbers = T, main='randomPP cross', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_andchasPPrandom.pdf', sep='/'))
pheatmap(rnetbpB_bbandchasfin,display_numbers = T, main='randomPP AND', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
###try with z score
#pdf(paste(figurespath,'BeekBcellPCHiC_crosschasPPZscore.pdf', sep='/'))
pheatmap((rnetbpB_bbcrosschasfin-netbpB_bbcrosschas),display_numbers = T, main='Zscore PP cross', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
#pdf(paste(figurespath,'BeekBcellPCHiC_andchasPPZscore.pdf', sep='/'))
pheatmap((rnetbpB_bbandchasfin-netbpB_bbandchas),display_numbers = T, main='Zscore PP AND', cluster_rows=F, cluster_cols=F, fontsize_number=14,fontsize=14, number_color = 'black',color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(length(breaksList)), breaks=breaksList)
#dev.off()
```
```{r}
newmt=netbpB_bbcrosschas
newmt[lower.tri(newmt)]<-0
crosschasg=graph.adjacency(newmt, weighted=T)
crosschasmat=data.frame(get.edgelist(crosschasg), E(crosschasg)$weight)
crosschasmatsel=crosschasmat[-which(crosschasmat[,1]==crosschasmat[,2]),]
rownames(crosschasmatsel)=paste(crosschasmatsel[,1], crosschasmatsel[,2],sep='_')
newandt=netbpB_bbandchas
newandt[lower.tri(newandt)]<-0
andchasg=graph.adjacency(newandt, weighted=T)
andchasmat=data.frame(get.edgelist(andchasg), E(andchasg)$weight)
andchasmatsel=andchasmat[-which(andchasmat[,1]==andchasmat[,2]),]
rownames(andchasmatsel)=paste(andchasmatsel[,1], andchasmatsel[,2],sep='_')
rcrosschasag=lapply(rnetbpB_bbcrosschas, function(x){
x[lower.tri(x)]<-0
return(graph.adjacency(x, weighted=T))
})
rcrosschasmat=lapply(rcrosschasag, function(x){
res=data.frame(get.edgelist(x), E(x)$weight)
res=res[-which(res[,1]==res[,2]),]
rownames(res)=paste(res[,1], res[,2],sep='_')
return(res)
})
randchasag=lapply(rnetbpB_bbandchas, function(x){
x[lower.tri(x)]<-0
return(graph.adjacency(x, weighted=T))
})
randchasmat=lapply(randchasag, function(x){
res=data.frame(get.edgelist(x), E(x)$weight)
res=res[-which(res[,1]==res[,2]),]
rownames(res)=paste(res[,1], res[,2],sep='_')
return(res)
})
```
```{r}
#svg(paste(figurespath, 'Figure3ABCD.svg', sep='/'), height=5, width=9)
par(mfrow=c(1,2))
plot(colMeans(netbpB_bbfeat),netbpB_bbchas, ylab='ChAs PP', xlab='Abundance PP', ylim=c(0,0.43),xlim=c(0,7), pch=21, bg='red')
text(colMeans(netbpB_bbfeat),netbpB_bbchas, label=colnames(netbpB_feat), cex=0.8, pos=1)
for (i in 2:nrand){
points(colMeans(rnetbpB_bbfeat[[i]]),rnetbpB_bbchas[[i]], col='grey')
}
points(colMeans(rnetbpB_bbfeat[[i]]),rnetbpB_bbchas[[i]], col='grey')
abline(h=0)
plot(netbpB_bbchas,netbpB_bochas, ylab='ChAs PO', xlab='ChAs PP', pch=21, bg='red', ylim=c(-0.25,0.32), xlim=c(0,0.5))
text(netbpB_bbchas,netbpB_bochas, label=colnames(netbpB_feat), cex=0.8, pos=3)
abline(a=0, b=1)
abline(h=0)
#dev.off()
par(mfrow=c(1,1))
cols=rainbow(nrow(crosschasmatsel))
#svg(paste(figurespath,'BeekAndvsCross.svg',sep='/'), height=5, width=8)
plot(crosschasmatsel[,3], andchasmatsel[,3], xlab='Cross ChAs of f1,f2', ylab='Chas of f1 AND f2', xlim=c(-0.2,0.4), ylim=c(0, 0.4), bg=cols, pch=21)
abline(a=0, b=1)
for (i in 2:nrand){
points(rcrosschasmat[[i]][,3], randchasmat[[i]][,3], pch='.', col=cols)
}
text(crosschasmatsel[,3], andchasmatsel[,3], labels=gsub('H3K','',rownames(crosschasmatsel)), cex=0.7, pos=3)
abline(h=0)
abline(v=0)
#dev.off()
```