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function.R
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processI < -function (i)
{
system(command=paste('mkdir',i,sep=" "))
system(command=paste('mkdir ./',i,'/AN_model',sep=""))
system(command=paste('mkdir ./',i,'/T_model',sep=""))
system(command=paste('mkdir ./',i,'/full_model',sep=""))
IDs_promoter <- unique(c(eval(parse(text = paste('dataf_BRCA_logit_PC@TSS1500Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))),eval(parse(text = paste('dataf_BRCA_logit_PC@TSS200Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))), eval(parse(text = paste('dataf_BRCA_logit_PC@UTR5Ind$SID$','"',workingList_BRCA4[i],'"',sep="")))),eval(parse(text = paste('dataf_BRCA_logit_PC@EXON1Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))))
IDs_body <- unique(c(eval(parse(text = paste('dataf_BRCA_logit_PC@GENEBODYInd$SID$','"',workingList_BRCA4[i],'"',sep=""))), eval(parse(text = paste('dataf_BRCA_logit_PC@UTR3Ind$SID$','"',workingList_BRCA4[i],'"',sep="")))))
# generate "missing" Var data
ncol = length(IDs_promoter)+length(IDs_body)+1
tempVar <- matrix(rep(".",75*ncol),nrow=75,ncol=ncol)
colnames(tempVar) <- c("NAME:\tEXPR",promoterVars[1:length(IDs_promoter)],geneBodyVars[1:length(IDs_body)])
rownames(tempVar) <- ANs2
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/AN_model/AN_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
rownames(tempVar) <- Ts2
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/T_model/T_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
tempVar <- rbind(tempVar,tempVar)
rownames(tempVar) <- c(Ts2,ANs2)
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/full_model/full_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
########################################################
# dynamic generation of model specification files here #
########################################################
stateMaps <- file(paste("./",i,"/stateMaps.txt",sep=""),"w")
cat("NAME:\tfiveState\nSYMBOLS:\t1 2 3 4 5\nMETA_SYMBOLS:\t.=1 2 3 4 5; *=1 2 3 4 5;\n",file=stateMaps)
close(stateMaps)
variables <- file(paste("./",i,"/variables.txt",sep=""),"w")
cat("STATE_MAP_NAME:\tfiveState\nVAR_NAMES:\tEXPR",promoterVars[1:length(IDs_promoter)],geneBodyVars[1:length(IDs_body)],sep=" ",file=variables)
cat("\n",file=variables)
close(variables)
promoterPots <- paste("\nNAME:\t\tpot_",promoterVars,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,5]((1,1,1,1,1))\nPC_MAT:\t\t[1,5]((0.1,0.1,0.1,0.1,0.1))\n",sep="")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBodyVars,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,5]((1,1,1,1,1))\nPC_MAT:\t\t[1,5]((0.1,0.1,0.1,0.1,0.1))\n",sep="")
potentials <- file(paste("./",i,"/factorPotentials.txt",sep=""),"w")
cat("NAME:\t\tpot_EXPR.CpG_P\nTYPE:\t\trowNorm\nPOT_MAT:\t[5,5]((1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1))\nPC_MAT:\t\t[5,5]((0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1))\n\nNAME:\t\tpot_EXPR.CpG_GB\nTYPE:\t\trowNorm\nPOT_MAT:\t[5,5]((1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1),\n\t\t\t(1,1,1,1,1))\nPC_MAT:\t\t[5,5]((0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1),\n\t\t\t(0.1,0.1,0.1,0.1,0.1))\n",
promoterPots[1:length(IDs_promoter)],geneBodyPots[1:length(IDs_body)],sep="",file=potentials)
cat("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,5]((1,1,1,1,1))\nPC_MAT:\t\t[1,5]((0.1,0.1,0.1,0.1,0.1))\n",file=potentials)
close(potentials)
factorGraph <- file(paste("./",i,"/factorGraph.txt",sep=""),"w")
cat("NAME:\tEXPR.likelihood\nNB1:\tEXPR\nPOT:\tpot_EXPR.likelihood\n",file=factorGraph)
cat(paste("\nNAME:\tEXPR.",promoterVars[1:length(IDs_promoter)],"\nNB1:\tEXPR\nNB2:\t",promoterVars[1:length(IDs_promoter)],"\nPOT:\tpot_EXPR.CpG_P\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\tEXPR.",geneBodyVars[1:length(IDs_body)],"\nNB1:\tEXPR\nNB2:\t",geneBodyVars[1:length(IDs_body)],"\nPOT:\tpot_EXPR.CpG_GB\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\t",promoterVars[1:length(IDs_promoter)],".likelihood\nNB1:\t",promoterVars[1:length(IDs_promoter)],"\nPOT:\tpot_",promoterVars[1:length(IDs_promoter)],"\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\t",geneBodyVars[1:length(IDs_body)],".likelihood\nNB1:\t",geneBodyVars[1:length(IDs_body)],"\nPOT:\tpot_",geneBodyVars[1:length(IDs_body)],"\n",sep="",collapse=""),file=factorGraph)
close(factorGraph)
########################################################
nsim <- 1 # partial expression evidence will sum up to this value
percentiles <- c(0.2,0.4,0.6,0.8) # according to which the binning scheme will be defined
###########################################################################
############################## AN model ###################################
## full AN model developed from here, to obtain likelihoods of Ts and ANs#
ANs_AN_likelihoods <- NULL
# gene body
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_body,ANs2])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_body,ANs2])
breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,ANs2])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,ANs2])
breaksPROMOTER <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# expression
mean <- mean(cpm_BRCA_plusOne[workingList_BRCA4[i],ANs2])
sd <- sd(cpm_BRCA_plusOne[workingList_BRCA4[i],ANs2])
breaksEXPRESSION <- sort(c(0,qnorm(percentiles,mean=mean,sd=sd),1000000))
# generate FacData for full set of ANs
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],ANs2[1]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
list <- NULL
list[[1]] <- cbind(X/factors_ls[which_ANs[1]],dpois(X,lambda=lambda)*factors_ls[which_ANs[1]])
for (j in 2:length(ANs2)) {
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],ANs2[j]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- factors_ls[which_ANs[j]]
list[[j]] <- cbind(X/current,dpois(X,lambda=lambda)*current)
}
tempS <- matrix(ncol=ncol)
for (current_sample in 1:length(ANs2)) {
# expression
AN <- list[[current_sample]]
AN <- as.data.frame(AN)
colnames(AN) <- c("cpm","density")
AN$interval <- findInterval(AN[,1],breaksEXPRESSION)
AN <- aggregate(cbind(cpm,density) ~ interval, data=AN, sum) #sum up the repetitive intervals' densities
AN$interval <- as.factor(AN$interval)
labels <- colnames(t(summary(AN$interval)))
frequencies <- AN$density
frequencies <- AN$density * nsim/sum(AN$density)
frequencies_expr <- rep(0,5) # EXPR.likelihood
for (label in 1:5) {if (!is.na(match(all_labels[label], labels))) {
frequencies_expr[label] <- frequencies[match(all_labels[label], labels)]
}}
# gene body
cpg_list_gb <- NULL # CpG_GB_X.likelihood
for (gb_cpg in 1:length(IDs_body)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_body[gb_cpg],ANs2[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_body$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksBODY)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksBODY)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_body$y[(intersect(which(d_body$x >= breaksBODY[bin-1]),which(d_body$x < breaksBODY[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_gb <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_gb[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_gb[[gb_cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL # CpG_P_X.likelihood
for (pr_cpg in 1:length(IDs_promoter)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_promoter[pr_cpg],ANs2[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_promoter$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksPROMOTER)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksPROMOTER)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_promoter$y[(intersect(which(d_promoter$x >= breaksPROMOTER[bin-1]),which(d_promoter$x < breaksPROMOTER[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_pr <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_pr[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_pr[[pr_cpg]] <- frequencies_pr
}
tempS_formated <- matrix()
tempS_formated[1,1] <- paste('[1,5]((',paste(frequencies_expr,sep="",collapse=","),'))',sep="")
for (element in 1:length(cpg_list_pr)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_pr[[element]],sep="",collapse=","),'))',sep=""))
}
for (element in 1:length(cpg_list_gb)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_gb[[element]],sep="",collapse=","),'))',sep=""))
}
tempS <- rbind(tempS,tempS_formated)
}
tempFac <- tempS[2:76,]
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs[1:length(IDs_promoter)],geneBody_CpGs[1:length(IDs_body)])
rownames(tempFac) <- ANs2
eval(parse(text = paste('write.table(', paste('tempFac,file ="./',i,'/AN_model/AN_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
# build and query the full model with T and AN samples
system(command=paste('mkdir ./',i,'/AN_model/','all/',sep=""))
system(command=paste('../phy/src/dfgTrain --emTrain --maxIter 1000 --dfgSpecPrefix=./',i,'/ --outSpecPrefix=./',i,'/AN_model/all/ ./',i,'/AN_model/AN_VarData.tab ./',i,'/AN_model/AN_FacData.tab',sep=""))
string<-system(intern=TRUE,command=paste('../phy/src/dfgEval --dfgSpecPrefix=./',i,'/AN_model/all/ -l -n - ./',i,'/AN_model/AN_VarData.tab ./',i,'/AN_model/AN_FacData.tab',sep=""))
ANs_AN_likelihoods <- Ts_AN_likelihoods <- as.numeric(substring(string[-1],17))
##########################################################################
############################## T model ###################################
### full T model developed from here, to obtain likelihoods of ANs #######
# gene body
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_body,Ts2])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_body,Ts2])
breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,Ts2])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,Ts2])
breaksPROMOTER <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# expression
mean <- mean(cpm_BRCA_plusOne[workingList_BRCA4[i],Ts2])
sd <- sd(cpm_BRCA_plusOne[workingList_BRCA4[i],Ts2])
breaksEXPRESSION <- sort(c(0,qnorm(percentiles,mean=mean,sd=sd),1000000))
# generate FacData for full set of Ts
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],Ts2[1]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
list <- NULL
list[[1]] <- cbind(X/factors_ls[which_Ts[1]],dpois(X,lambda=lambda)*factors_ls[which_Ts[1]])
for (j in 2:length(Ts2)) {
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],Ts2[j]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- factors_ls[which_Ts[j]]
list[[j]] <- cbind(X/current,dpois(X,lambda=lambda)*current)
}
tempS <- matrix(ncol=ncol)
for (current_sample in 1:length(Ts2)) {
# expression
AN <- list[[current_sample]]
AN <- as.data.frame(AN)
colnames(AN) <- c("cpm","density")
AN$interval <- findInterval(AN[,1],breaksEXPRESSION)
AN <- aggregate(cbind(cpm,density) ~ interval, data=AN, sum) #sum up the repetitive intervals' densities
AN$interval <- as.factor(AN$interval)
labels <- colnames(t(summary(AN$interval)))
frequencies <- AN$density
frequencies <- AN$density * nsim/sum(AN$density)
frequencies_expr <- rep(0,5) # EXPR.likelihood
for (label in 1:5) {if (!is.na(match(all_labels[label], labels))) {
frequencies_expr[label] <- frequencies[match(all_labels[label], labels)]
}}
# gene body
cpg_list_gb <- NULL # CpG_GB_X.likelihood
for (gb_cpg in 1:length(IDs_body)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_body[gb_cpg],Ts2[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_body$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksBODY)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksBODY)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_body$y[(intersect(which(d_body$x >= breaksBODY[bin-1]),which(d_body$x < breaksBODY[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_gb <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_gb[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_gb[[gb_cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL # CpG_P_X.likelihood
for (pr_cpg in 1:length(IDs_promoter)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_promoter[pr_cpg],Ts2[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_promoter$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksPROMOTER)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksPROMOTER)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_promoter$y[(intersect(which(d_promoter$x >= breaksPROMOTER[bin-1]),which(d_promoter$x < breaksPROMOTER[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_pr <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_pr[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_pr[[pr_cpg]] <- frequencies_pr
}
tempS_formated <- matrix()
tempS_formated[1,1] <- paste('[1,5]((',paste(frequencies_expr,sep="",collapse=","),'))',sep="")
for (element in 1:length(cpg_list_pr)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_pr[[element]],sep="",collapse=","),'))',sep=""))
}
for (element in 1:length(cpg_list_gb)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_gb[[element]],sep="",collapse=","),'))',sep=""))
}
tempS <- rbind(tempS,tempS_formated)
}
tempFac <- tempS[2:76,]
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs[1:length(IDs_promoter)],geneBody_CpGs[1:length(IDs_body)])
rownames(tempFac) <- Ts2
eval(parse(text = paste('write.table(', paste('tempFac,file ="./',i,'/T_model/T_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
# build and query the full model with AN samples
system(command=paste('mkdir ./',i,'/T_model/','all/',sep=""))
system(command=paste('../phy/src/dfgTrain --emTrain --maxIter 1000 --dfgSpecPrefix=./',i,'/ --outSpecPrefix=./',i,'/T_model/all/ ./',i,'/T_model/T_VarData.tab ./',i,'/T_model/T_FacData.tab',sep=""))
string<-system(intern=TRUE,command=paste('../phy/src/dfgEval --dfgSpecPrefix=./',i,'/T_model/all/ -l -n - ./',i,'/T_model/T_VarData.tab ./',i,'/T_model/T_FacData.tab',sep=""))
Ts_T_likelihoods <- as.numeric(substring(string[-1],17))
##########################################################################
###########################################################################
######################## All data model ###################################
## Full model developed from here, to obtain likelihoods of Ts and ANs ####
# gene body
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_body,])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_body,])
breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
mean <- mean(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,c(Ts2,ANs2)])
sd <- sd(dataf_BRCA_logit_PC@bmatrix[IDs_promoter,c(Ts2,ANs2)])
breaksPROMOTER <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# expression
mean <- mean(cpm_BRCA_plusOne[workingList_BRCA4[i],c(Ts2,ANs2)])
sd <- sd(cpm_BRCA_plusOne[workingList_BRCA4[i],c(Ts2,ANs2)])
breaksEXPRESSION <- sort(c(0,qnorm(percentiles,mean=mean,sd=sd),1000000))
# generate FacData for Ts
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],c(Ts2,ANs2)[1]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
list <- NULL
list[[1]] <- cbind(X/factors_ls[c(which_Ts,which_ANs)[1]],dpois(X,lambda=lambda)*factors_ls[c(which_Ts,which_ANs)[1]])
for (j in 2:length(c(Ts2,ANs2))) {
lambda <- counts_BRCA_plusOne[workingList_BRCA4[i],c(Ts2,ANs2)[j]]
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- factors_ls[c(which_Ts,which_ANs)[j]]
list[[j]] <- cbind(X/current,dpois(X,lambda=lambda)*current)
}
tempS <- matrix(ncol=ncol)
for (current_sample in 1:length(c(Ts2,ANs2))) {
# expression
dens <- list[[current_sample]]
dens <- as.data.frame(dens)
colnames(dens) <- c("cpm","density")
dens$interval <- findInterval(dens[,1],breaksEXPRESSION)
dens <- aggregate(cbind(cpm,density) ~ interval, data=dens, sum) #sum up the repetitive intervals' densities
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies <- dens$density * nsim/sum(dens$density)
frequencies_expr <- rep(0,5) # EXPR.likelihood
for (label in 1:5) {if (!is.na(match(all_labels[label], labels))) {
frequencies_expr[label] <- frequencies[match(all_labels[label], labels)]
}}
# gene body
cpg_list_gb <- NULL # CpG_GB_X.likelihood
for (gb_cpg in 1:length(IDs_body)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_body[gb_cpg],c(Ts2,ANs2)[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_body_all$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksBODY)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksBODY)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_body_all$y[(intersect(which(d_body_all$x >= breaksBODY[bin-1]),which(d_body_all$x < breaksBODY[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_gb <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_gb[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_gb[[gb_cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL # CpG_P_X.likelihood
for (pr_cpg in 1:length(IDs_promoter)) {
x <- dataf_BRCA_logit_PC@bmatrix[IDs_promoter[pr_cpg],c(Ts2,ANs2)[current_sample]]
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$y <- dens$y/sum(dens$y)
dens$y <- dens$y * d_promoter_all$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksPROMOTER)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksPROMOTER)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_promoter_all$y[(intersect(which(d_promoter_all$x >= breaksPROMOTER[bin-1]),which(d_promoter_all$x < breaksPROMOTER[bin])))])
}
dens$interval <- as.factor(dens$interval)
labels <- colnames(t(summary(dens$interval)))
frequencies <- dens$density
frequencies_pr <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_pr[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_pr[[pr_cpg]] <- frequencies_pr
}
tempS_formated <- matrix()
tempS_formated[1,1] <- paste('[1,5]((',paste(frequencies_expr,sep="",collapse=","),'))',sep="")
for (element in 1:length(cpg_list_pr)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_pr[[element]],sep="",collapse=","),'))',sep=""))
}
for (element in 1:length(cpg_list_gb)) {
tempS_formated <- cbind(tempS_formated,paste('[1,5]((',paste(cpg_list_gb[[element]],sep="",collapse=","),'))',sep=""))
}
tempS <- rbind(tempS,tempS_formated)
}
tempFac <- tempS[2:151,]
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs[1:length(IDs_promoter)],geneBody_CpGs[1:length(IDs_body)])
rownames(tempFac) <- c(Ts2,ANs2)
eval(parse(text = paste('write.table(', paste('tempFac,file = "./',i,'/full_model/full_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
# build and query the full model with T and AN samples
system(command=paste('mkdir ./',i,'/full_model/','all/',sep=""))
system(command=paste('../phy/src/dfgTrain --emTrain --maxIter 1000 --dfgSpecPrefix=./',i,'/ --outSpecPrefix=./',i,'/full_model/all/ ./',i,'/full_model/full_VarData.tab ./',i,'/full_model/full_FacData.tab',sep=""))
string<-system(intern=TRUE,command=paste('../phy/src/dfgEval --dfgSpecPrefix=./',i,'/full_model/all/ -l -n - ./',i,'/full_model/full_VarData.tab ./',i,'/full_model/full_FacData.tab',sep=""))
allData_full_likelihoods <- as.numeric(substring(string[-1],17))
###########################################################################################
ANs_AN_likelihoods <- as.brob(exp(1)^-ANs_AN_likelihoods)
Ts_T_likelihoods <- as.brob(exp(1)^-Ts_T_likelihoods)
allData_full_likelihoods <- as.brob(exp(1)^-allData_full_likelihoods)
D <- -2*log(sum(ANs_AN_likelihoods,Ts_T_likelihoods)/sum(allData_full_likelihoods))
pval <- pchisq(D,df=52)
return(pval)
}