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05_1_Generate_disease_networks_Genes.R
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05_1_Generate_disease_networks_Genes.R
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## CODE FOR THE GENERATION OF THE DISEASE NETWORKS FOR MEN, WOMEN, AND ADJUSTING BY SEX BASED ON GENES ##
## Developed by Jon Sanchez-Valle & Beatriz Urda-Garcia
## Barcelona Supercomputing Center
## Life Science Department
## Computational Biology Group
## Email: jon.sanchez@bsc.es @ beatriz.urda@bsc.es
#### Function for calculating similarities between diseases, adjusting by sex, separately for men and women, and between diseases and healthy differences women vs. men ####
nameout<-"Adjusted"
classification<-"ICD10"
experiment<-"Microarrays"
build_network<-function(nameout,classification,experiment){
if("Generated_Networks"%in%list.files(experiment)==FALSE){dir.create(paste(experiment,"/Generated_Networks",sep=""))}
## Start the analysis!! ##
## @@ @@ @@ @@ @@ @@ @@ ##
files<-list.files(paste(experiment,"/",classification,"/Comparisons/",nameout,"/DifferentialExpressions/",sep=""))
## Run the loop ##
sdegs<-list() ; vsdegs<-list() ; vnum<-c() ; vname<-c()
allgenes<-list()
for(a in 1:length(files)){
# a<-1
tabla<-read.csv2(paste(experiment,"/",classification,"/Comparisons/",nameout,"/DifferentialExpressions/",files[a],sep=""),stringsAsFactors = F,sep="\t")
ups<-rownames(tabla)[intersect(which(as.numeric(as.character(tabla$adj.P.Val))<=0.05),which(as.numeric(as.character(tabla$logFC))>0))]
upslogs<-as.numeric(as.character(tabla$logFC[intersect(which(as.numeric(as.character(tabla$adj.P.Val))<=0.05),which(as.numeric(as.character(tabla$logFC))>0))]))
downs<-rownames(tabla)[intersect(which(as.numeric(as.character(tabla$adj.P.Val))<=0.05),which(as.numeric(as.character(tabla$logFC))<0))]
downslogs<-as.numeric(as.character(tabla$logFC[intersect(which(as.numeric(as.character(tabla$adj.P.Val))<=0.05),which(as.numeric(as.character(tabla$logFC))<0))]))
names(downslogs)<-downs
names(upslogs)<-ups
sdegs[[gsub(".txt","",files[a])]]$Up<-ups
sdegs[[gsub(".txt","",files[a])]]$Down<-downs
vsdegs[[gsub(".txt","",files[a])]]$Up<-upslogs
vsdegs[[gsub(".txt","",files[a])]]$Down<-downslogs
vnum<-rbind(vnum,c(length(ups),length(downs)))
vname<-c(vname,gsub(".+_","",gsub(".txt","",files[a])))
todosgenes<-tabla$logFC ; names(todosgenes)<-rownames(tabla)
allgenes[[gsub(".txt","",files[a])]]<-todosgenes
print(paste(round((a/length(files))*100,2),"%",sep=""))
}
## Similarities between pairs of diseases ##
netall<-c() ; fishtab<-c()
for(a in 1:(length(names(vsdegs))-1)){
for(b in (a+1):length(names(vsdegs))){
## Over all the genes ##
## @@ @@ @@ @@ @@ @@ ##
commongenes<-intersect(names(allgenes[[a]]),names(allgenes[[b]]))
femcorrelp<-cor.test(as.numeric(as.character(allgenes[[a]][commongenes])),as.numeric(as.character(allgenes[[b]][commongenes])),method = "pearson")
femcorrels<-cor.test(as.numeric(as.character(allgenes[[a]][commongenes])),as.numeric(as.character(allgenes[[b]][commongenes])),method = "spearman")
## Over the intersection of the sDEGs ##
## @@ @@ @@ @@ @@ @@@@ @@ @@ @@ @@ @@ ##
unof<-vsdegs[[a]] ; dosf<-vsdegs[[b]]
allunof<-c(unof$Up,unof$Down) ; alldosf<-c(dosf$Up,dosf$Down)
fintersection<-intersect(names(allunof),names(alldosf))
if(length(fintersection)>3){
ifemcorrelp<-cor.test(as.numeric(as.character(allunof[fintersection])),as.numeric(as.character(alldosf[fintersection])),method = "pearson")
ifemcorrels<-cor.test(as.numeric(as.character(allunof[fintersection])),as.numeric(as.character(alldosf[fintersection])),method = "spearman")
}
if(length(fintersection)<=3){
ifemcorrelp<-list("estimate"=NA,"p.value"=NA)
ifemcorrels<-list("estimate"=NA,"p.value"=NA)
}
## Over the union of sDEGs ##
## @@ @@ @@ @@ @@ @@ @@ @@ ##
funion<-unique(c(names(allunof),names(alldosf)))
numberifunion<-length(intersect(names(which(is.na(allgenes[[a]][funion])==FALSE)),names(which(is.na(allgenes[[b]][funion])==FALSE))))
if(numberifunion<=3){
ufemcorrelp<-list("estimate"=NA,"p.value"=NA)
ufemcorrels<-list("estimate"=NA,"p.value"=NA)
}
if(numberifunion>3){
ufemcorrelp<-cor.test(as.numeric(as.character(allgenes[[a]][funion])),as.numeric(as.character(allgenes[[b]][funion])),method = "pearson")
ufemcorrels<-cor.test(as.numeric(as.character(allgenes[[a]][funion])),as.numeric(as.character(allgenes[[b]][funion])),method = "spearman")
}
## Calculating Fisher's overlaps ##
## @@ @@ @@ @@ @@ @@ @@ @@ @@ @@ ##
background<-length(commongenes)
positi1<-intersect(names(vsdegs[[a]]$Up),commongenes) ; negati1<-intersect(names(vsdegs[[a]]$Down),commongenes) ; pega<-c() ; pegados<-c()
positi2<-intersect(names(vsdegs[[b]]$Up),commongenes) ; negati2<-intersect(names(vsdegs[[b]]$Down),commongenes)
interspd <- length(intersect(positi1,positi2))
kkpd <- matrix(c(interspd,length(positi1)-interspd,length(positi2)-interspd,background+interspd-length(positi1)-length(positi2)),nrow=2,ncol=2)
fispd<-fisher.test(kkpd,alternative="greater") $p.value
intersnd <- length(intersect(negati1,negati2))
kknd <- matrix(c(intersnd,length(negati1)-intersnd,length(negati2)-intersnd,background+intersnd-length(negati1)-length(negati2)),nrow=2,ncol=2)
fisnd<-fisher.test(kknd,alternative="greater") $p.value
interspi <- length(intersect(positi1,negati2))
kkpi <- matrix(c(interspi,length(positi1)-interspi,length(negati2)-interspi,background+interspi-length(positi1)-length(negati2)),nrow=2,ncol=2)
fispi<-fisher.test(kkpi,alternative="greater") $p.value
intersni <- length(intersect(negati1,positi2))
kkni <- matrix(c(intersni,length(negati1)-intersni,length(positi2)-intersni,background+intersni-length(negati1)-length(positi2)),nrow=2,ncol=2)
fisni<-fisher.test(kkni,alternative="greater") $p.value
fjunt<-c(fispi,fispd,fisni,fisnd)
## Put all the correlations together ##
## @@ @@ @@ @@ @@ @ @ @@ @@ @@ @@ @@ ##
netall<-rbind(netall,c(names(allgenes)[c(a,b)],as.numeric(femcorrelp$estimate),as.numeric(femcorrelp$p.value),
as.numeric(femcorrels$estimate),as.numeric(femcorrels$p.value),
as.numeric(ifemcorrelp$estimate),as.numeric(ifemcorrelp$p.value),
as.numeric(ifemcorrels$estimate),as.numeric(ifemcorrels$p.value),
as.numeric(ufemcorrelp$estimate),as.numeric(ufemcorrelp$p.value),
as.numeric(ufemcorrels$estimate),as.numeric(ufemcorrels$p.value)))
fishtab<-rbind(fishtab,fjunt)
}
print(paste(round((a/length(names(allgenes)))*100,2),"%",sep=""))
}
fishtab2<-fishtab
## Correct Fisher's Exact test p-values for multiple testing (number of disease comparisons * 4 sDEGs comparisons (up-up, down-down, up-down, down-up))
for(a in 1:4){fishtab[,a]<-fishtab[,a]*length(fishtab[,1])*4}
for(a in 1:4){fishtab[which(fishtab[,a]>1),a]<-1}
## Transform the fishtab into a 1, 0, and -1 column
threshold<-0.05
negs<-list("Pos_Neg"=which(fishtab[,1]<=threshold),"Neg_Pos"=which(fishtab[,3]<=threshold),"Pos_Pos"=which(fishtab[,2]>threshold),"Neg_Neg"=which(fishtab[,4]>threshold))
poss<-list("Pos_Neg"=which(fishtab[,1]>threshold),"Neg_Pos"=which(fishtab[,3]>threshold),"Pos_Pos"=which(fishtab[,2]<=threshold),"Neg_Neg"=which(fishtab[,4]<=threshold))
ffishint<-rep(0,length(fishtab[,1])) ; ffishint[Reduce(intersect,negs)]<--1 ; ffishint[Reduce(intersect,poss)]<-1
## Add the fisher's test columns ##
if(length(netall[1,])==14){netall<-cbind(netall,ffishint)}
## Add column names ##
colnames(netall)<-c("Disease1","Disease2","AllPearson","AllPvalPearson","AllSpearman","AllPvalSpearman",
"IntPearson","IntPvalPearson","IntSpearman","IntPvalSpearman",
"UniPearson","UniPvalPearson","UniSpearman","UniPvalSpearman","FisherTests")
## Write the tables ##
write.table(netall,paste(experiment,"/Generated_Networks/",classification,"_",nameout,"_pvals_network.txt",sep=""),quote=F,sep="\t",row.names=F)
## Correct for multipletesting ##
if(length(grep("Pval",colnames(netall)))>0){
netall[,4]<-as.numeric(netall[,4])*length(netall[,1])
netall[,6]<-as.numeric(netall[,6])*length(netall[,1])
netall[,8]<-as.numeric(netall[,8])*length(netall[,1])
netall[,10]<-as.numeric(netall[,10])*length(netall[,1])
netall[,12]<-as.numeric(netall[,12])*length(netall[,1])
netall[,14]<-as.numeric(netall[,14])*length(netall[,1])
colnames(netall)<-c("Disease1","Disease2","AllPearson","AllFDRPearson","AllSpearman","AllFDRSpearman",
"IntPearson","IntFDRPearson","IntSpearman","IntFDRSpearman",
"UniPearson","UniFDRPearson","UniSpearman","UniFDRSpearman","FisherTests")
netall[which(as.numeric(netall[,4])>1),4]<-1
netall[which(as.numeric(netall[,6])>1),6]<-1
netall[which(as.numeric(netall[,8])>1),8]<-1
netall[which(as.numeric(netall[,10])>1),10]<-1
netall[which(as.numeric(netall[,12])>1),12]<-1
netall[which(as.numeric(netall[,14])>1),14]<-1
}
## Write the tables ##
write.table(netall,paste(experiment,"/Generated_Networks/",classification,"_",nameout,"_fdr_network.txt",sep=""),quote=F,sep="\t",row.names=F)
## Convert the correlations and FDRs into 1s, 0s, and -1s ##
## @@ @@ @@ @@ @@ @@ @@ @@ @ @ @@ @@ @@ @@ @@ @@ @@ @@ @@ ##
palldir<-rep(0,length(netall[,1]))
palldir[intersect(which(as.numeric(netall[,4])<=0.05),which(as.numeric(netall[,3])<0))]<-(-1)
palldir[intersect(which(as.numeric(netall[,4])<=0.05),which(as.numeric(netall[,3])>0))]<-1
salldir<-rep(0,length(netall[,1]))
salldir[intersect(which(as.numeric(netall[,6])<=0.05),which(as.numeric(netall[,5])<0))]<-(-1)
salldir[intersect(which(as.numeric(netall[,6])<=0.05),which(as.numeric(netall[,5])>0))]<-1
pintdir<-rep(0,length(netall[,1]))
pintdir[intersect(which(as.numeric(netall[,8])<=0.05),which(as.numeric(netall[,7])<0))]<-(-1)
pintdir[intersect(which(as.numeric(netall[,8])<=0.05),which(as.numeric(netall[,7])>0))]<-1
sintdir<-rep(0,length(netall[,1]))
sintdir[intersect(which(as.numeric(netall[,10])<=0.05),which(as.numeric(netall[,9])<0))]<-(-1)
sintdir[intersect(which(as.numeric(netall[,10])<=0.05),which(as.numeric(netall[,9])>0))]<-1
punidir<-rep(0,length(netall[,1]))
punidir[intersect(which(as.numeric(netall[,12])<=0.05),which(as.numeric(netall[,11])<0))]<-(-1)
punidir[intersect(which(as.numeric(netall[,12])<=0.05),which(as.numeric(netall[,11])>0))]<-1
sunidir<-rep(0,length(netall[,1]))
sunidir[intersect(which(as.numeric(netall[,14])<=0.05),which(as.numeric(netall[,13])<0))]<-(-1)
sunidir[intersect(which(as.numeric(netall[,14])<=0.05),which(as.numeric(netall[,13])>0))]<-1
binary<-cbind(netall[,1:2],palldir,salldir,pintdir,sintdir,punidir,sunidir,netall[,15])
## Write both tables ##
colnames(binary)<-c("Disease1","Disease2","PearsonAll","SpearmanAll","PearsonInt","SpearmanInt","PearsonUni","SpearmanUni","Fisher")
write.table(binary,paste(experiment,"/Generated_Networks/",classification,"_",nameout,"_binarized_network.txt",sep=""),quote=F,sep="\t",row.names=F)
## Which interactions are different?
resultstab<-rbind(cbind(c(length(which(binary[,3]!=0))),
c(length(which(binary[,4]!=0))),
c(length(which(binary[,5]!=0))),
c(length(which(binary[,6]!=0))),
c(length(which(binary[,7]!=0))),
c(length(which(binary[,8]!=0))),
c(length(which(binary[,9]!=0)))))
resultstab<-cbind(resultstab,length(binary[,1]),length(names(allgenes)))
colnames(resultstab)<-c("PearsonAll","SpearmanAll","PearsonInt","SpearmanInt","PearsonUni","SpearmanUni","Fisher","PotentialInteractions","Diseases")
resultstab<-rbind(resultstab,round(100*(resultstab[1,]/resultstab[1,8]),2))
rownames(resultstab)<-c("All","Percentage_All")
write.table(resultstab,paste(experiment,"/Generated_Networks/",classification,"_",nameout,"_number_interactions.txt",sep=""),quote=F,sep="\t")
resultados<-list("resultstab"=resultstab,"binarynet"=binary,"fdrnet"=netall)
print("Finished!")
return(resultados)
}
#### Microarrays ####
## @@ @@ @ @ @@ @@ ##
## Adjusting for sex ##
adj9net<-build_network("Adjusted","ICD9","Microarrays")
adj10net<-build_network("Adjusted","ICD10","Microarrays")
print("Networks generated when adjusting for sex")
## Men and Women separately ##
women9net<-build_network("Women","ICD9","Microarrays")
men9net<-build_network("Men","ICD9","Microarrays")
women10net<-build_network("Women","ICD10","Microarrays")
men10net<-build_network("Men","ICD10","Microarrays")
print("Networks generated separately for men and women")
## Women vs. Men in cases and controls ##
cases9net<-build_network("Cases","ICD9","Microarrays")
controls9net<-build_network("Controls","ICD9","Microarrays")
cases10net<-build_network("Cases","ICD10","Microarrays")
controls10net<-build_network("Controls","ICD10","Microarrays")
print("Networks generated comparing men and women in cases and controls")
#### Do comorbidities go in opposite directions based on sex? ####
epi<-read.csv("Epidemiology/41467_2019_8475_MOESM6_ESM.txt",stringsAsFactors = F,sep="\t")
epis<-epi[,c(1:4,13,15,19,21)]
siwomen_nomen<-epis[intersect(which(as.numeric(epis$womenRR2.5)>1.01),which(as.numeric(epis$menRR97.5)<1)),]
nowomen_simen<-epis[intersect(which(as.numeric(epis$womenRR97.5)<1),which(as.numeric(epis$menRR2.5)>1.01)),]
siwomen_nomen[which(siwomen_nomen$NameB=="Gout"),]