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sanityCheck.R
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sanityCheck.R
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#args <- commandArgs(trailingOnly = TRUE)
#beg <- as.numeric(args[1])
#end <- as.numeric(args[2])
#load("./essentials_FG.RData")
t_pr <- -2
t_gb <- -2
an_pr <- -2
an_gb <- -2
t_lambda <- 80
an_lambda <- 70
libsize <- 50
IDs_promoter_all <- unique(c(unlist(TSS1500Ind$SID),unlist(TSS200Ind$SID),unlist(UTR5Ind$SID),unlist(EXON1Ind$SID)))
IDs_body_all <- unique(c(unlist(GENEBODYInd$SID),unlist(UTR3Ind$SID)))
# AN prior
d_promoter <- density(bmatrix[IDs_promoter_all,ANs2],bw=0.14,from=-7,to=7,n=1401)
d_promoter$y <- d_promoter$y/sum(d_promoter$y)
d_body <- density(bmatrix[IDs_body_all,ANs2],bw=0.14,from=-7,to=7,n=1401)
d_body$y <- d_body$y/sum(d_body$y)
# T prior
d_promoter_t <- density(bmatrix[IDs_promoter_all,Ts2],bw=0.14,from=-7,to=7,n=1401)
d_promoter_t$y <- d_promoter_t$y/sum(d_promoter_t$y)
d_body_t <- density(bmatrix[IDs_body_all,Ts2],bw=0.14,from=-7,to=7,n=1401)
d_body_t$y <- d_body_t$y/sum(d_body_t$y)
# all data prior
d_promoter_all <- density(bmatrix[IDs_promoter_all,c(Ts2,ANs2)],bw=0.14,from=-7,to=7,n=1401)
d_promoter_all$y <- d_promoter$y/sum(d_promoter$y)
d_body_all <- density(bmatrix[IDs_body_all,c(Ts2,ANs2)],bw=0.14,from=-7,to=7,n=1401)
d_body_all$y <- d_body$y/sum(d_body$y)
all_labels <- as.character(seq(1,5,1))
promoter_CpGs <- paste(promoterVars,".likelihood",sep="")
geneBody_CpGs <- paste(geneBodyVars,".likelihood",sep="")
for (arg in length(args)){
i <- args[arg]
cat(paste("doing ",i,"\n",sep=""))
ptm <- proc.time()[3]
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('TSS1500Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))),eval(parse(text = paste('TSS200Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))), eval(parse(text = paste('UTR5Ind$SID$','"',workingList_BRCA4[i],'"',sep="")))),eval(parse(text = paste('EXON1Ind$SID$','"',workingList_BRCA4[i],'"',sep=""))))
IDs_body <- unique(c(eval(parse(text = paste('GENEBODYInd$SID$','"',workingList_BRCA4[i],'"',sep=""))), eval(parse(text = paste('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]((0.001,0.001,0.001,0.001,0.001))\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]((0.001,0.001,0.001,0.001,0.001))\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]((0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001))\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]((0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001),\n\t\t\t(0.001,0.001,0.001,0.001,0.001))\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]((0.001,0.001,0.001,0.001,0.001))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,5]((0.2,0.2,0.2,0.2,0.2))\nPC_MAT:\t\t[1,5]((0.01,0.01,0.01,0.01,0.01))\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("\nNAME:\tEXPR.prior\nNB1:\tEXPR\nPOT:\tpot_EXPR.prior\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(0.1,0.3,0.5,0.7,0.9) # 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 <- 0
sd <- 0.5
breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
mean <- 0
sd <- 0.5
breaksPROMOTER <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# expression
mean <- 1.5
sd <- 0.25
breaksEXPRESSION <- qnorm(percentiles,mean=mean,sd=sd)
# generate FacData for full set of ANs
list <- NULL
for (j in 1:length(ANs2)) {
lambda <- an_lambda
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- libsize
list[[j]] <- cbind(X/current,dpois(X,lambda=lambda)*current)
}
tempS <- matrix(ncol=ncol)
for (current_sample in 1:length(ANs2)) {
# expression
#dens <- as.data.frame(list[[current_sample]])
#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$density)
lambda <- an_lambda
frequencies_expr <- dpois(lambda,libsize * breaksEXPRESSION)
#frequencies_expr <- rep(0,5) # EXPR.likelihood
#for (label in 1:5) {if (!is.na(match(all_labels[label], labels))) {
# frequencies_expr[label] <- frequencies[match(all_labels[label], labels)]
#}}
# gene body
cpg_list_gb <- NULL # CpG_GB_X.likelihood
for (gb_cpg in 1:length(IDs_body)) {
x <- an_gb
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$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$density
frequencies_gb <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_gb[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_gb[[gb_cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL # CpG_P_X.likelihood
for (pr_cpg in 1:length(IDs_promoter)) {
x <- an_pr
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$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 AN model with AN samples
system(command=paste('mkdir ./',i,'/AN_model/','all/',sep=""))
system(command=paste('./dfgTrain_static --emTrain --maxIter 10000 --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('./dfgEval_static --dfgSpecPrefix=./',i,'/AN_model/all/ -l -n - ./',i,'/AN_model/AN_VarData.tab ./',i,'/AN_model/AN_FacData.tab',sep=""))
ANs_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(bmatrix[IDs_body,Ts2])
#sd <- sd(bmatrix[IDs_body,Ts2])
#breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
#mean <- mean(bmatrix[IDs_promoter,Ts2])
#sd <- sd(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
list <- NULL
for (j in 1:length(Ts2)) {
lambda <- t_lambda
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- libsize
list[[j]] <- cbind(X/current,dpois(X,lambda=lambda)*current)
}
tempS <- matrix(ncol=ncol)
for (current_sample in 1:length(Ts2)) {
# expression
#dens <- as.data.frame(list[[current_sample]])
#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)
lambda <- t_lambda
frequencies_expr <- dpois(lambda,libsize * breaksEXPRESSION)
#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 <- t_gb
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$y * d_body_t$y # P(M|d)*P(d)
dens <- as.data.frame(cbind(dens$x,dens$y))
colnames(dens) <- c("M","density")
dens$interval <- findInterval(dens[,1],breaksBODY)
dens <- aggregate(cbind(M,density) ~ interval, data=dens, sum)
for (bin in 2:length(breaksBODY)) { # (P(M|d)*P(d))/P(M)
dens[bin-1,3] <- dens[bin-1,3] / sum(d_body_t$y[(intersect(which(d_body_t$x >= breaksBODY[bin-1]),which(d_body_t$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 <- t_pr
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 T model with T samples
system(command=paste('mkdir ./',i,'/T_model/','all/',sep=""))
system(command=paste('./dfgTrain_static --emTrain --maxIter 10000 --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('./dfgEval_static --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(bmatrix[IDs_body,])
#sd <- sd(bmatrix[IDs_body,])
#breaksBODY <- round(sort(c(-7.01,qnorm(percentiles,mean=mean,sd=sd),7.01)),digits=2)
# promoter
#mean <- mean(bmatrix[IDs_promoter,c(Ts2,ANs2)])
#sd <- sd(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 and ANs
list <- NULL
for (j in 1:length(c(Ts2,ANs2))) {
if (j <= 75) lambda <- t_lambda else lambda <- an_lambda
X <- seq(round(max(lambda-(4*lambda*lambda^(-1/2)),1)),round(lambda+(4*lambda*lambda^(-1/2))))
current <- libsize
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 <- as.data.frame(list[[current_sample]])
#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$density)
if (current_sample <= 75) lambda <- t_lambda else lambda <- an_lambda
#print(lambda)
frequencies_expr <- dpois(lambda,libsize * breaksEXPRESSION)
#frequencies_expr <- rep(0,5) # EXPR.likelihood
#for (label in 1:5) {if (!is.na(match(all_labels[label], labels))) {
# frequencies_expr[label] <- frequencies[match(all_labels[label], labels)]
#}}
# gene body
cpg_list_gb <- NULL # CpG_GB_X.likelihood
for (gb_cpg in 1:length(IDs_body)) {
if (current_sample <= 75) x <- t_gb else x <- an_gb
#x <- -2
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$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$density
frequencies_gb <- rep(0,5)
for (label in 1:5) {
if (!is.na(match(all_labels[label], labels)))
frequencies_gb[label] <- frequencies[match(all_labels[label], labels)]
}
cpg_list_gb[[gb_cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL # CpG_P_X.likelihood
for (pr_cpg in 1:length(IDs_promoter)) {
if (current_sample <= 75) x <- t_pr else x <- an_pr
#x <- -2
if (x < -7) x <- -7
dens <- density(x,bw=0.14,from=-7,to=7,n=1401)
dens$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('./dfgTrain_static --emTrain --maxIter 10000 --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('./dfgEval_static --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))
###########################################################################################
eval(parse(text=paste('write.table(x=t(ANs_AN_likelihoods), col.names=FALSE, row.names=FALSE, append=FALSE, file="./',i,'/',i,'.result")',sep="")))
eval(parse(text=paste('write.table(x=t(Ts_T_likelihoods), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'/',i,'.result")',sep="")))
eval(parse(text=paste('write.table(x=t(allData_full_likelihoods), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'/',i,'.result")',sep="")))
#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)
#eval(parse(text=paste('write.table(x=t(pval), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'/',i,'.result")',sep="")))
cat(paste("done ",i," in ", sprintf("%.2f", (proc.time()[3]-ptm)/60)," minutes\n",sep=""))
}