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PearsonVsBioGrid.R
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PearsonVsBioGrid.R
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require(data.table)
require(stringr)
require(reshape2)
require(igraph)
require(doMC)
require(ggplot2)
registerDoMC(35)
#install.packages("beanplot")
require(beanplot)
getMTX = function(tpCor, tpSRC){
if(tpSRC == "RNA"){
load("../DATA/RNA.Rdata")
rownames(dtRNA) = dtRNA$geneName
dtRNA$geneName = NULL
mtx=t(dtRNA)
}
if(tpSRC == "RNA45"){
load("../DATA/Prot.Rdata")
dtProt$Gene.Symbol = NULL
load("../DATA/RNA.Rdata")
rownames(dtRNA) = dtRNA$geneName
dtRNA$geneName = NULL
dtRNA = dtRNA[,colnames(dtProt)[]]
mtx=t(dtRNA)
}
if(tpSRC == "prot"){
load("../DATA/Prot.Rdata")
rownames(dtProt) = dtProt$Gene.Symbol
dtProt$Gene.Symbol = NULL
mtx=t(log2(dtProt))
}
mtx
}
getCorum = function(gns){
dtCorum = read.table("../DATA/CORUM_30102016_coreComplexes.txt",sep="\t",
comment.char = '?',quote = '?', header = T, stringsAsFactors = F)
dtx = foreach (x = dtCorum$subunits.Gene.name., .combine=rbind) %do% {
x=unlist(str_split(x,";"))
x=x[x %in% gns]
if (length(x) > 1){
t(combn(x,2))
}else{
NULL
}
}
colnames(dtx) = c("node1","node2")
dtx=data.frame(dtx,stringsAsFactors = F)
idx = dtx$node1 > dtx$node2
x = dtx$node1[idx]
dtx$node1[idx] = dtx$node2[idx]
dtx$node2[idx] = x
dtx$X = T
unique(dtx)
}
getBioGrid = function(gns){
dtBioGrid = read.table("/Z/aleferna/GeneLists/BioGrid/BIOGRID-ALL-3.4.137.GenePairs",header = T,comment.char = "#",sep = "\t",stringsAsFactors = F)
idx = dtBioGrid$Entrez %in% gns & dtBioGrid$Gene %in% gns
dtBioGrid = dtBioGrid[idx,]
colnames(dtBioGrid) = c("node1","node2")
#remove duplicates
dtBioGrid = dtBioGrid[!duplicated(dtBioGrid),]
#sort the rows
idx = dtBioGrid$node1 > dtBioGrid$node2
x = dtBioGrid$node1[idx]
dtBioGrid$node1[idx] = dtBioGrid$node2[idx]
dtBioGrid$node2[idx] = x
#remove self loops
idx = dtBioGrid$node1 != dtBioGrid$node2
dtBioGrid = dtBioGrid[idx,]
dtBioGrid = data.table(dtBioGrid)
dtBioGrid$X = T
dtBioGrid
}
############################################################################################################################################################
################################################ Genome Wide Correlation ############################################################
############################################################################################################################################################
doViolinALL = function(tpSRC,tpCor, mtx, dtKnown, srcKnown){
mtxCor=cor( mtx, method = tpCor)
edges=melt(mtxCor)
colnames(edges) = c("node1", "node2","cor")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges=merge(edges,dtKnown, all=T, by=c("node1","node2"))
edges[is.na(edges$X), X := F]
xedges=data.frame(edges)
pdf(file=paste0("Figures/CorrelationVsBioGrid/",tpSRC,".",tpCor,".",srcKnown,".Violin-ALL.pdf"), height = 8, width=8)
beanplot(xedges$cor ~ xedges$X, ll = 0.04,
main = paste("Correlation of All Genes: ",srcKnown) , side = "both", xlab=paste(tpSRC, tpCor),
col = list("#be1200", c("#006eff", "#FFFF00")),
axes=T,bw="nrd",what = c(1,1,1,0), ylim = c(-0.5,1.2))
dev.off()
}
############################################################################################################################################################
################################################ SubGroup Correlation ###############################################################
############################################################################################################################################################
doSubGroup = function(tpSRC,tpCor, mtx, dtKnown, srcKnown){
load("../DATA/geneListsHJ.RData")
geneLists$Nik.Zainal_Drivers =NULL
geneLists$FDATargets = NULL
geneLists$CancerSensus = NULL
geneLists$PAM50 = NULL
geneLists$DrugableGenome = NULL
rownames(geneLists) = geneLists$GeneName
geneLists$GeneName = NULL
geneLists$No.enrichment = NULL
geneLists$FredlundNet = NULL
geneLists$TumorGenes9506 = NULL
xedges=foreach (ListName = colnames(geneLists), .combine = rbind ) %do% {
#ListName = colnames(geneLists)[1]
gns = rownames( geneLists)[geneLists[,ListName]]
#
gns = gns[gns %in% colnames(mtx)]
print(c(ListName, length(gns)))
mtxCor=cor(mtx[,gns],method = tpCor)
edges=melt(mtxCor)
colnames(edges) = c("node1", "node2","cor")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges=merge(edges,dtKnown, all.x=T, by=c("node1","node2"))
edges[is.na(edges$X), X := F]
edges$ListName = ListName
edges
}
pdf(file=paste0("Figures/CorrelationVsBioGrid/",tpSRC,".",tpCor,".",srcKnown,".Violin.pdf"), height = 8, width=12)
idx = xedges$ListName %in% c("Erythrocyte" , "Plasma", "Adipose" , "ECM.1" , "ECM.2" , "Luminal" )
beanplot(xedges$cor[idx] ~ xedges$X[idx]*xedges$ListName[idx], ll = 0.04,
main = paste0(srcKnown," Correlation by Gene Cluster"), side = "both", xlab=paste(tpSRC, tpCor),
col = list("#be1200", c("#006eff", "#FFFF00")),
axes=T,bw="nrd",what = c(1,1,1,0), ylim = c(-0.5,1.2))
idx = xedges$ListName %in% c("Immune" , "Golgi" , "Basal" , "Mitochondrion" ,"Proliferation" , "Transcription")
beanplot(xedges$cor[idx] ~ xedges$X[idx]*xedges$ListName[idx], ll = 0.04,
main = paste0(srcKnown," Correlation by Gene Cluster"), side = "both", xlab=paste(tpSRC, tpCor),
col = list("#be1200", c("#006eff", "#FFFF00")),
axes=T,bw="nrd",what = c(1,1,1,0), ylim = c(-0.5,1.2))
dev.off()
}
############################################################################################################################################################
################################################ RNA vs Protein @ protein Complx ###############################################################
############################################################################################################################################################
doKnown.RNAvPROT.ByComplex = function(mtxR, mtxP, tpCor){
geneLists=list()
geneLists$GINS = c("GINS1","GINS2","GINS3","GINS4")
geneLists$MCM = c("MCM2","MCM3","MCM4","MCM5","MCM6","MCM7")
geneLists$Condensing_I = c("NCAPD2","NCAPG","NCAPH","SMC2","SMC4")
geneLists$Condensing_II = c("NCAPD3","NCAPG2","NCAPH2","SMC2","SMC4")
geneLists$Mitotic_14s_cohesin_1 = c("RAD21","SMC1A","SMC3","STAG1")
geneLists$DNA_PolAlpha = c("CHEK1","PRIM1","PRIM2","POLA1","POLA2")
xedges=foreach (ListName = names(geneLists), .combine = rbind ) %do% {
#ListName = names(geneLists)[1]
gns = geneLists[[ListName]]
gns = gns[gns %in% colnames(mtxP)]
gns = gns[gns %in% colnames(mtxR)]
mtxCor=cor(mtxR[,gns],method = tpCor)
edgesR=melt(mtxCor)
edgesR$Src = "RNA"
mtxCor=cor(mtxP[,gns],method = tpCor)
edgesP=melt(mtxCor)
edgesP$Src = "Protein"
edges = rbind(edgesR,edgesP)
colnames(edges) = c("node1", "node2","cor","Src")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges$ListName = ListName
edges
}
pdf(file=paste0("Figures/CorrelationVsBioGrid/Complex.",tpCor,".RNAvsProtein.pdf"), height = 8, width=12)
beanplot(show.names = T, xedges$cor ~ xedges$Src * xedges$ListName, ll = 0.04,
main = "RNA vs Protein Correlation of Complexes", side = "both", xlab=paste( tpCor),
col = list("red", "orange"),
axes=T,bw="nrd",what = c(1,1,1,0), ylim = c(0,1.2) )
dev.off()
}
############################################################################################################################################################
################################################ Correlation of BioGrid Genes RNA vs Prot ###############################################################
############################################################################################################################################################
doKnown.RNAvsProt.All = function(mtxR, mtxP, tpCor, dtKnown, srcKnown){
mtxCor=cor(mtxR,method = tpCor)
edgesR=melt(mtxCor)
edgesR$Src = "RNA"
mtxCor=cor(mtxP,method = tpCor)
edgesP=melt(mtxCor)
edgesP$Src = "Protein"
edges = rbind(edgesR,edgesP)
colnames(edges) = c("node1", "node2","cor","Src")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges=merge(edges,dtKnown, all.x=T, by=c("node1","node2"))
edges[is.na(edges$X), X := F]
pdf(file=paste0("Figures/CorrelationVsBioGrid/",srcKnown,".Known.",tpCor,".RNAvsProtein.pdf"), height = 8, width=12)
beanplot(show.names = T, edges$cor ~ edges$X * edges$Src, ll = 0.04,
main = paste("RNA vs Protein Correlation:",srcKnown), side = "both", xlab=paste(srcKnown, tpCor),
col = list("red", "orange", "blue", "lightblue"),
axes=T,bw="nrd",what = c(1,1,1,0), ylim = c(-0.5,1.2) )
dev.off()
}
############################################################################################################################################################
################################################ p-value calculation ###############################################################
############################################################################################################################################################
doPValCalc = function(tpSRC, tpCor, mtx, dtKnown, srcKnown){
mtxCor=cor( mtx, method = tpCor)
edges=melt(mtxCor)
colnames(edges) = c("node1", "node2", "cor")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges=merge(edges, dtKnown, all=T, by=c("node1","node2"))
edges[is.na(edges$X), X := F]
xedges=data.frame(edges)
load("../DATA/geneListsHJ.RData")
rownames(geneLists) = geneLists$GeneName
geneLists$GeneName = NULL
gns=colnames(mtx)
geneLists$All =F
geneLists[gns,"All"] = T
geneLists[is.na(geneLists)] = F
xstats=foreach (ListName = colnames(geneLists), .combine=rbind, .errorhandling = "remove") %dopar% {
gns = rownames( geneLists)[geneLists[,ListName]]
gns = gns[gns %in% colnames(mtx)]
mtxCor=cor(mtx[,gns],method = tpCor)
edges=melt(mtxCor)
colnames(edges) = c("node1", "node2","cor")
edges$node1 = paste(edges$node1)
edges$node2 = paste(edges$node2)
idx = edges$node1 < edges$node2
edges=edges[idx,]
edges=data.table(edges)
idx=NULL
edges=merge(edges,dtKnown, all.x=T, by=c("node1","node2"))
edges[is.na(edges$X), X := F]
edges$ListName = ListName
fg=edges$cor[edges$X]
bg=sample(edges$cor[!edges$X], sum(edges$X))
p=wilcox.test(fg,bg, alternative = "greater")
print(ListName)
data.frame(correlType=tpCor, dataSource=tpSRC, knownSrc=srcKnown, test=ListName,
pval=p$p.value, tstat=p$statistic,
alternative=p$alternative ,
length(fg), fgAvg = mean(fg), fgStd = sd(fg),
length(bg), bgAvg = mean(bg), bgStd = sd(bg),
delta = mean(fg) - mean(bg)
)
}
xstats
}
############################################################################################################################################################
################################################ Main Flow ###############################################################
############################################################################################################################################################
tpCor="pearson"
load("../DATA/Prot.Rdata")
rownames(dtProt) = dtProt$Gene.Symbol
dtProt$Gene.Symbol = NULL
dtProt = log2(dtProt)
load("../DATA/RNA.Rdata")
rownames(dtRNA) = dtRNA$geneName
dtRNA$geneName = NULL
gns=intersect(rownames(dtRNA),rownames(dtProt) )
dtBioGrid = getBioGrid(gns)
dtCorum = getCorum(gns)
gns=intersect(gns, unique(c(dtBioGrid$node1, dtBioGrid$node2)) )
smps=intersect(colnames(dtProt),colnames(dtRNA))
mtxP = t(dtProt[gns,smps])
mtxR = t( dtRNA[gns,smps])
doKnown.RNAvPROT.ByComplex(mtxR, mtxP, tpCor)
doKnown.RNAvsProt.All(mtxR, mtxP, tpCor, dtCorum, "CORUM")
doKnown.RNAvsProt.All(mtxR, mtxP, tpCor, dtBioGrid, "BioGrid" )
doSubGroup("RNA", tpCor, mtxR, dtBioGrid, "BioGrid" )
doSubGroup("RNA", tpCor, mtxR, dtCorum, "CORUM" )
doSubGroup("Prot", tpCor, mtxP, dtBioGrid, "BioGrid" )
doSubGroup("Prot", tpCor, mtxP, dtCorum, "CORUM" )
stats = foreach (srcKnown = c("BioGrid","CORUM"), .combine=rbind) %dopar% {
if (srcKnown == "BioGrid"){
dtKnown = dtBioGrid
}else{
dtKnown = dtCorum
}
foreach (tpSRC = c("RNA","Prot"), .combine=rbind) %dopar% {
if (tpSRC == "RNA"){
doPValCalc(tpSRC, tpCor, mtxR, dtKnown, srcKnown )
}else{
doPValCalc(tpSRC, tpCor, mtxP, dtKnown, srcKnown )
}
}
}
write.table(stats, file=paste0("Figures/CorrelationVsBioGrid/Stats.tab"), sep="\t", row.names = F, quote = F)