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Copy pathBRCA_2way_varRes_init_null.R
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BRCA_2way_varRes_init_null.R
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args <- commandArgs(trailingOnly = TRUE)
beg <- as.numeric(args[1])
end <- as.numeric(args[2])
load("./essentials_BRCA.RData")
res_pr <- 50
res_gb <- 50
res_expr <- 50
smooth_1d <- 1
smooth_2d <- 1
if (smooth_1d > 0) library(aws)
if (smooth_2d > 0) library(smoothie)
integrand_e <- function(x,k) {dpois(k,x)}
integrand_m <- function(x,mean) {dnorm(x=mean,mean=x,sd=0.14)}
tensor_product <- function(matrix1,matrix2,smooth_h=0,normalize=c("row","column","no"),kernel=c("gauss","cauchy","minvar")) {
if (is.matrix(matrix1) && is.matrix(matrix2)) result <- matrix(ncol=ncol(matrix1),nrow=ncol(matrix2),data=rep(0,ncol(matrix1)*ncol(matrix2))) else result <- matrix(ncol=length(matrix1),nrow=length(matrix1),data=rep(0,length(matrix1)*length(matrix2)))
if (is.matrix(matrix1) && is.matrix(matrix2)) for (i in 1:nrow(matrix1)) {
result <- result + matrix(ncol=ncol(matrix1),nrow=ncol(matrix2),byrow=TRUE,data=apply(expand.grid(matrix1[i,],matrix2[i,]), 1, prod))
} else result <- result + matrix(nrow=length(matrix1),ncol=length(matrix2),byrow=TRUE,data=apply(expand.grid(matrix1,matrix2), 1, prod))
if (is.matrix(matrix1) && is.matrix(matrix2)) result <- result/nrow(matrix1)
if (!is.null(kernel) && smooth_h > 0) result <- kernel2dsmooth(result,kernel.type = kernel[1],sigma=smooth_h,nx=ncol(matrix2),ny=ncol(matrix1))
if (normalize[1] == "row") for (i in 1:nrow(result)) result[i,] <- result[i,]/sum(result[i,]) else if (normalize[1] == "column") for (i in 1:nrow(result)) result[,i] <- result[,i]/sum(result[,i])
return(result)
}
geo_mean <- function(data) {
log_data <- log(data)
gm <- exp(mean(log_data[is.finite(log_data)]))
return(gm)
}
for (i in beg:end){
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,'/AN_model/all',sep=""))
system(command=paste('mkdir ./',i,'/T_model',sep=""))
system(command=paste('mkdir ./',i,'/T_model/all',sep=""))
system(command=paste('mkdir ./',i,'/full_model',sep=""))
system(command=paste('mkdir ./',i,'/null',sep=""))
system(command=paste('mkdir ./',i,'/null/AN_model',sep=""))
system(command=paste('mkdir ./',i,'/null/T_model',sep=""))
# generate "missing" Var data
tempVar <- matrix(rep(".",length(ANs)*3),nrow=length(ANs),ncol=3)
colnames(tempVar) <- c("NAME:\tEXPR","M.GB","M.P")
rownames(tempVar) <- ANs
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/AN_model/all/AN_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/null/AN_model/AN_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
rownames(tempVar) <- Ts
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/T_model/all/T_VarData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
eval(parse(text = paste('write.table(', paste('tempVar,file = "./',i,'/null/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(Ts,ANs)
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 = ""))))
###########################################################
############### identify constitutive CpGs ################
IDs_promoter <- unique(c(eval(parse(text = paste('TSS1500Ind$SID$','"',workingList_BRCA[i],'"',sep=""))),eval(parse(text = paste('TSS200Ind$SID$','"',workingList_BRCA[i],'"',sep=""))), eval(parse(text = paste('UTR5Ind$SID$','"',workingList_BRCA[i],'"',sep="")))),eval(parse(text = paste('EXON1Ind$SID$','"',workingList_BRCA[i],'"',sep=""))))
promoterVars <- promoterVars_template[1:length(IDs_promoter)]
promoter_CpGs <- template_promoter_CpGs[1:length(IDs_promoter)]
IDs_body <- unique(c(eval(parse(text = paste('GENEBODYInd$SID$','"',workingList_BRCA[i],'"',sep=""))), eval(parse(text = paste('UTR3Ind$SID$','"',workingList_BRCA[i],'"',sep="")))))
geneBodyVars <- geneBodyVars_template[1:length(IDs_body)]
geneBody_CpGs <- template_body_CpGs[1:length(IDs_body)]
ncol = length(IDs_promoter) + length(IDs_body) + 1
############################################################
######### calculate epsilons and smoothing params ##########
epsilon_pr_t <- 1/(length(Ts)*length(IDs_promoter))/res_pr
epsilon_gb_t <- 1/(length(Ts)*length(IDs_body))/res_gb
epsilon_e_t <- 1/length(Ts)/res_expr
epsilon_pr_an <- 1/(length(ANs)*length(IDs_promoter))/res_pr
epsilon_gb_an <- 1/(length(ANs)*length(IDs_body))/res_gb
epsilon_e_an <- 1/length(ANs)/res_expr
smooth_e <- 10/(mean(length(ANs),length(Ts))/(res_expr*2))
smooth_pr <- trunc(10/(mean(length(ANs),length(Ts))*length(IDs_promoter)/(res_pr*res_expr)))
smooth_gb <- trunc(10/(mean(length(ANs),length(Ts))*length(IDs_body)/(res_gb*res_expr)))
###########################################################
############### define the binning scheme ################
all_labels_pr <- as.character(seq(1,res_pr,1))
all_labels_gb <- as.character(seq(1,res_gb,1))
all_labels_expr <- as.character(seq(1,res_expr,1))
promoter_t <- matrix(ncol=res_pr,nrow=length(Ts))
promoter_an <- matrix(ncol=res_pr,nrow=length(ANs))
body_t <- matrix(ncol=res_gb,nrow=length(Ts))
body_an <- matrix(ncol=res_gb,nrow=length(ANs))
expr_t <- matrix(ncol=res_expr,nrow=length(Ts))
expr_an <- matrix(ncol=res_expr,nrow=length(ANs))
pseudo_counts_pr <- matrix(ncol=res_pr,nrow=res_expr,data=rep(1,res_expr*res_pr))
pseudo_counts_gb <- matrix(ncol=res_gb,nrow=res_expr,data=rep(1,res_expr*res_gb))
# gene body
x <- mmatrix[IDs_body,c(Ts,ANs)]
density <- density(x,bw=0.14,from=-7,to=7,n=2801)
density$y <- density$y/sum(density$y)
density$y <- cumsum(density$y)
breaks <- NULL
noBreaks <- res_gb-1
for (j in 1:noBreaks) { breaks <- c (breaks, density$x[which(density$y >= j*(1/(1+noBreaks)))][1])}
breaksBODY <- sort(c(-7.01,breaks,7.01))
# promoter
x <- mmatrix[IDs_promoter,c(Ts,ANs)]
density <- density(x,bw=0.14,from=-7,to=7,n=2801)
density$y <- density$y/sum(density$y)
density$y <- cumsum(density$y)
breaks <- NULL
noBreaks <- res_pr-1
for (j in 1:noBreaks) { breaks <- c (breaks, density$x[which(density$y >= j*(1/(1+noBreaks)))][1])}
breaksPROMOTER <- sort(c(-7.01,breaks,7.01))
# expression
temp <- counts_BRCA_plusOne[workingList_BRCA[i],c(Ts,ANs)]
tempAN <- matrix(ncol=2)
colnames(tempAN) <- c("cpm","density")
for (j in 1:length(temp)) {
lambda <- temp[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]]
tempAN <- rbind(tempAN,cbind(X/current,dpois(X,lambda=lambda)*current))
}
tempAN <- as.data.frame(tempAN[-1,],)
tempAN <- tempAN[order(tempAN$cpm),]
tempAN[,3] <- cumsum(tempAN[,2])
tempAN[,3] <- tempAN[,3]/max(tempAN[,3])
breaks <- NULL
noBreaks <- res_expr-1
for (j in 1:noBreaks) { breaks <- c (breaks, tempAN[which(tempAN[,3] >= j*(1/(1+noBreaks))),1][1])}
max <- max(counts_BRCA_plusOne[workingList_BRCA[i],c(Ts,ANs)])
max_boundary <- 20+max+(4*max*max^(-1/2))
breaksEXPRESSION <- c(0,breaks,max_boundary)
########################################################
# dynamic generation of model specification files here #
########################################################
####################### state map ######################
stateMaps <- file(paste("./",i,"/stateMaps.txt",sep=""),"w")
exprMap <- paste("NAME:\texprMap\nSYMBOLS:\t",paste(seq(1,res_expr,1),collapse=" "),"\nMETA_SYMBOLS:\t.=",paste(seq(1,res_expr,1),collapse=" "),"; *=",paste(seq(1,res_expr,1),collapse=" "),";\n\n",collapse="",sep="")
prMap <- paste("NAME:\tprMap\nSYMBOLS:\t",paste(seq(1,res_pr,1),collapse=" "),"\nMETA_SYMBOLS:\t.=",paste(seq(1,res_pr,1),collapse=" "),"; *=",paste(seq(1,res_pr,1),collapse=" "),";\n\n",collapse="",sep="")
gbMap <- paste("NAME:\tgbMap\nSYMBOLS:\t",paste(seq(1,res_gb,1),collapse=" "),"\nMETA_SYMBOLS:\t.=",paste(seq(1,res_gb,1),collapse=" "),"; *=",paste(seq(1,res_gb,1),collapse=" "),";\n",collapse="",sep="")
cat(exprMap,prMap,gbMap,file=stateMaps)
close(stateMaps)
########################################################
####################### variables ######################
variables <- file(paste("./",i,"/variables.txt",sep=""),"w")
cat("STATE_MAP_NAME:\texprMap\nVAR_NAMES:\tEXPR\n\n",sep="",file=variables)
cat("STATE_MAP_NAME:\tprMap\nVAR_NAMES:\tM.P\n\n",sep="",file=variables)
cat("STATE_MAP_NAME:\tgbMap\nVAR_NAMES:\tM.GB\n",sep="",file=variables)
close(variables)
########################################################
##################### factor graph #####################
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.M.GB\nNB1:\tEXPR\nNB2:\tM.GB\nPOT:\tpot_EXPR.M.GB\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\tEXPR.M.P\nNB1:\tEXPR\nNB2:\tM.P\nPOT:\tpot_EXPR.M.P\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\t",promoter_CpGs,"\nNB1:\tM.P\nPOT:\tpot_",promoter_CpGs,"\n",sep="",collapse=""),file=factorGraph)
cat(paste("\nNAME:\t",geneBody_CpGs,"\nNB1:\tM.GB\nPOT:\tpot_",geneBody_CpGs,"\n",sep="",collapse=""),file=factorGraph)
close(factorGraph)
system(command=paste('cp ./',i,'/*.txt ./',i,'/AN_model/all',sep=""))
system(command=paste('cp ./',i,'/*.txt ./',i,'/T_model/all',sep=""))
system(command=paste('cp ./',i,'/*.txt ./',i,'/full_model',sep=""))
system(command=paste('cp ./',i,'/*.txt ./',i,'/null/AN_model',sep=""))
system(command=paste('cp ./',i,'/*.txt ./',i,'/null/T_model',sep=""))
###########################################################################
############################## AN model ###################################
## full AN model developed from here, to obtain likelihoods of ANs ########
# generate FacData for full set of ANs
tempS_AN <- matrix(ncol=ncol,nrow=length(ANs))
for (current_sample in 1:length(ANs)) {
# expression
read_count <- trunc(counts_BRCA_plusOne[workingList_BRCA[i],ANs[current_sample]])
lambdas <- breaksEXPRESSION * factors_ls[which_ANs[current_sample]]
frequencies_expr <- rep(0,length(breaksEXPRESSION)-1)
for (freq in 1:res_expr) {
frequencies_expr[freq] <- integrate(integrand_e, lower = lambdas[freq], upper = lambdas[freq+1], read_count,stop.on.error=FALSE)[1]
}
frequencies_expr <- unlist(frequencies_expr)
if (length(which(frequencies_expr==0))==res_expr) frequencies_expr[length(frequencies_expr)] <- 1
frequencies_expr <- frequencies_expr + epsilon_e_an
frequencies_expr <- frequencies_expr/sum(frequencies_expr)
# gene body
cpg_list_gb <- NULL
for (cpg in 1:length(geneBodyVars)) {
miu <- mmatrix[IDs_body[cpg],ANs[current_sample]]
frequencies_gb <- rep(0,res_gb)
for (freq in 1:res_gb) {
frequencies_gb[freq] <- integrate(integrand_m,lower=breaksBODY[freq],upper=breaksBODY[freq+1],mean=miu)$value
}
frequencies_gb <- unlist(frequencies_gb) + epsilon_gb_an
frequencies_gb <- frequencies_gb/sum(frequencies_gb)
cpg_list_gb[[cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL
for (cpg in 1:length(promoterVars)) {
miu <- mmatrix[IDs_promoter[cpg],ANs[current_sample]]
frequencies_pr <- rep(0,res_pr)
for (freq in 1:res_pr) {
frequencies_pr[freq] <- integrate(integrand_m,lower=breaksPROMOTER[freq],upper=breaksPROMOTER[freq+1],mean=miu)$value
}
frequencies_pr <- unlist(frequencies_pr) + epsilon_pr_an
frequencies_pr <- frequencies_pr/sum(frequencies_pr)
cpg_list_pr[[cpg]] <- frequencies_pr
}
tempS_formated <- matrix(ncol=ncol,nrow=1)
tempS_formated[1,1] <- paste('[1,',res_expr,']((',paste(frequencies_expr,sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- 1
for (element in 1:length(cpg_list_pr)) {
tempS_formated[1,cur_ncol+1] <- paste('[1,',res_pr,']((',paste(cpg_list_pr[[element]],sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- cur_ncol + 1
}
for (element in 1:length(cpg_list_gb)) {
tempS_formated[1,cur_ncol+1] <- paste('[1,',res_gb,']((',paste(cpg_list_gb[[element]],sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- cur_ncol + 1
}
tempS_AN[current_sample,] <- tempS_formated
#start precomputing correct initialization of parameters
promoter_an[current_sample,] <- apply(matrix(unlist(cpg_list_pr),ncol=res_pr,byrow=TRUE),2,geo_mean)/sum(apply(matrix(unlist(cpg_list_pr),ncol=res_pr,byrow=TRUE),2,geo_mean))
body_an[current_sample,] <- apply(matrix(unlist(cpg_list_gb),ncol=res_gb,byrow=TRUE),2,geo_mean)/sum(apply(matrix(unlist(cpg_list_gb),ncol=res_gb,byrow=TRUE),2,geo_mean))
expr_an[current_sample,] <- frequencies_expr
}
# build and the full model with AN samples
# precompute correct initialization of parameters for AN-only model
prior_pr <- apply(promoter_an,2,mean)
prior_gb <- apply(body_an,2,mean)
if (smooth_1d > 0) {
prior_expr <- kernsm(apply(expr_an,2,mean),h=smooth_e)
prior_expr <- prior_expr@yhat/sum(prior_expr@yhat)
} else prior_expr <- apply(promoter_an,2,mean)
string <- paste(prior_pr,collapse=",")
promoterPots <- paste("\nNAME:\t\tpot_",promoter_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_pr,"]((",string,"))\nPC_MAT:\t\t[1,",res_pr,"]((",paste(rep(1,res_pr),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_gb,collapse=",")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBody_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_gb,"]((",string,"))\nPC_MAT:\t\t[1,",res_gb,"]((",paste(rep(1,res_gb),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_expr,collapse=",")
expr.pots <- paste("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\n",sep="",collapse="")
result <- tensor_product(body_an,expr_an,smooth_h=smooth_gb)
expr.m <- paste("NAME:\t\tpot_EXPR.M.GB\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(pseudo_counts_gb,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse="")
result <- tensor_product(promoter_an,expr_an,smooth_h=smooth_pr)
expr.m <- c(expr.m,paste("NAME:\t\tpot_EXPR.M.P\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(pseudo_counts_pr,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse=""))
potentials <- file(paste("./",i,"/AN_model/all/factorPotentials.txt",sep=""),"w")
cat(expr.m,expr.pots,promoterPots,geneBodyPots,file=potentials)
close(potentials)
tempFac <- tempS_AN
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs,geneBody_CpGs)
rownames(tempFac) <- ANs
eval(parse(text = paste('write.table(', paste('tempFac,file ="./',i,'/AN_model/all/AN_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
# query the full model with AN samples
string<-system(intern=TRUE,command=paste('./dfgEval_static --dfgSpecPrefix=./',i,'/AN_model/all/ -l -n - ./',i,'/AN_model/all/AN_VarData.tab ./',i,'/AN_model/all/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 Ts #######
# generate FacData for full set of Ts
tempS_T <- matrix(ncol=ncol,nrow=length(Ts))
for (current_sample in 1:length(Ts)) {
# expression
read_count <- trunc(counts_BRCA_plusOne[workingList_BRCA[i],Ts[current_sample]])
lambdas <- breaksEXPRESSION * factors_ls[which_Ts[current_sample]]
frequencies_expr <- rep(0,length(breaksEXPRESSION)-1)
for (freq in 1:res_expr) {
frequencies_expr[freq] <- integrate(integrand_e, lower = lambdas[freq], upper = lambdas[freq+1], read_count,stop.on.error=FALSE)[1]
}
frequencies_expr <- unlist(frequencies_expr)
if (length(which(frequencies_expr==0))==res_expr) frequencies_expr[length(frequencies_expr)] <- 1
frequencies_expr <- frequencies_expr + epsilon_e_t
frequencies_expr <- frequencies_expr/sum(frequencies_expr)
# gene body
cpg_list_gb <- NULL
for (cpg in 1:length(geneBodyVars)) {
miu <- mmatrix[IDs_body[cpg],Ts[current_sample]]
frequencies_gb <- rep(0,res_gb)
for (freq in 1:res_gb) {
frequencies_gb[freq] <- integrate(integrand_m,lower=breaksBODY[freq],upper=breaksBODY[freq+1],mean=miu)$value
}
frequencies_gb <- unlist(frequencies_gb) + epsilon_gb_t
frequencies_gb <- frequencies_gb/sum(frequencies_gb)
cpg_list_gb[[cpg]] <- frequencies_gb
}
# promoter
cpg_list_pr <- NULL
for (cpg in 1:length(promoterVars)) {
miu <- mmatrix[IDs_promoter[cpg],Ts[current_sample]]
frequencies_pr <- rep(0,res_pr)
for (freq in 1:res_pr) {
frequencies_pr[freq] <- integrate(integrand_m,lower=breaksPROMOTER[freq],upper=breaksPROMOTER[freq+1],mean=miu)$value
}
frequencies_pr <- unlist(frequencies_pr) + epsilon_pr_t
frequencies_pr <- frequencies_pr/sum(frequencies_pr)
cpg_list_pr[[cpg]] <- frequencies_pr
}
tempS_formated <- matrix(ncol=ncol,nrow=1)
tempS_formated[1,1] <- paste('[1,',res_expr,']((',paste(frequencies_expr,sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- 1
for (element in 1:length(cpg_list_pr)) {
tempS_formated[1,cur_ncol+1] <- paste('[1,',res_pr,']((',paste(cpg_list_pr[[element]],sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- cur_ncol + 1
}
for (element in 1:length(cpg_list_gb)) {
tempS_formated[1,cur_ncol+1] <- paste('[1,',res_gb,']((',paste(cpg_list_gb[[element]],sep="",collapse=","),'))',sep="",collapse="")
cur_ncol <- cur_ncol + 1
}
tempS_T[current_sample,] <- tempS_formated
#start precomputing correct initialization of parameters
promoter_t[current_sample,] <- apply(matrix(unlist(cpg_list_pr),ncol=res_pr,byrow=TRUE),2,geo_mean)/sum(apply(matrix(unlist(cpg_list_pr),ncol=res_pr,byrow=TRUE),2,geo_mean))
body_t[current_sample,] <- apply(matrix(unlist(cpg_list_gb),ncol=res_gb,byrow=TRUE),2,geo_mean)/sum(apply(matrix(unlist(cpg_list_gb),ncol=res_gb,byrow=TRUE),2,geo_mean))
expr_t[current_sample,] <- frequencies_expr
}
# build and the full model with T samples
# precompute correct initialization of parameters for T-only model
prior_pr <- apply(promoter_t,2,mean)
prior_gb <- apply(body_t,2,mean)
if (smooth_1d > 0) {
prior_expr <- kernsm(apply(expr_t,2,mean),h=smooth_e)
prior_expr <- prior_expr@yhat/sum(prior_expr@yhat)
} else prior_expr <- apply(expr_t,2,mean)
string <- paste(prior_pr,collapse=",")
promoterPots <- paste("\nNAME:\t\tpot_",promoter_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_pr,"]((",string,"))\nPC_MAT:\t\t[1,",res_pr,"]((",paste(rep(1,res_pr),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_gb,collapse=",")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBody_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_gb,"]((",string,"))\nPC_MAT:\t\t[1,",res_gb,"]((",paste(rep(1,res_gb),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_expr,collapse=",")
expr.pots <- paste("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\n",sep="",collapse="")
result <- tensor_product(body_t,expr_t,smooth_h=smooth_gb)
expr.m <- paste("NAME:\t\tpot_EXPR.M.GB\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(pseudo_counts_gb,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse="")
result <- tensor_product(promoter_t,expr_t,smooth_h=smooth_pr)
expr.m <- c(expr.m,paste("NAME:\t\tpot_EXPR.M.P\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(pseudo_counts_pr,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse=""))
potentials <- file(paste("./",i,"/T_model/all/factorPotentials.txt",sep=""),"w")
cat(expr.m,expr.pots,promoterPots,geneBodyPots,file=potentials)
close(potentials)
tempFac <- tempS_T
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs,geneBody_CpGs)
rownames(tempFac) <- Ts
eval(parse(text = paste('write.table(', paste('tempFac,file ="./',i,'/T_model/all/T_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
# query the full model with T samples
string<-system(intern=TRUE,command=paste('./dfgEval_static --dfgSpecPrefix=./',i,'/T_model/all/ -l -n - ./',i,'/T_model/all/T_VarData.tab ./',i,'/T_model/all/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 ####
# precompute correct initialization of parameters for AN-only model
promoter_all <- rbind(promoter_t,promoter_an)
body_all <- rbind(body_t,body_an)
expr_all <- rbind(expr_t,expr_an)
prior_pr <- apply(promoter_all,2,mean)
prior_gb <- apply(body_all,2,mean)
if (smooth_1d > 0) {
prior_expr <- kernsm(apply(expr_all,2,mean),h=smooth_e)
prior_expr <- prior_expr@yhat/sum(prior_expr@yhat)
} else prior_expr <- apply(expr_all,2,mean)
string <- paste(prior_pr,collapse=",")
promoterPots <- paste("\nNAME:\t\tpot_",promoter_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_pr,"]((",string,"))\nPC_MAT:\t\t[1,",res_pr,"]((",paste(rep(1,res_pr),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_gb,collapse=",")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBody_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_gb,"]((",string,"))\nPC_MAT:\t\t[1,",res_gb,"]((",paste(rep(1,res_gb),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_expr,collapse=",")
expr.pots <- paste("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\n",sep="",collapse="")
result <- tensor_product(body_all,expr_all,smooth_h=smooth_gb)
expr.m <- paste("NAME:\t\tpot_EXPR.M.GB\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(pseudo_counts_gb,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse="")
result <- tensor_product(promoter_all,expr_all,smooth_h=smooth_pr)
expr.m <- c(expr.m,paste("NAME:\t\tpot_EXPR.M.P\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(pseudo_counts_pr,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse=""))
potentials <- file(paste("./",i,"/full_model/factorPotentials.txt",sep=""),"w")
cat(expr.m,expr.pots,promoterPots,geneBodyPots,file=potentials)
close(potentials)
tempFac <- rbind(tempS_T,tempS_AN)
colnames(tempFac) <- c("NAME:\tEXPR.likelihood",promoter_CpGs,geneBody_CpGs)
rownames(tempFac) <- c(Ts,ANs)
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 = ""))))
# query the full model with T and AN samples
string<-system(intern=TRUE,command=paste('./dfgEval_static --dfgSpecPrefix=./',i,'/full_model/ -l -n - ./',i,'/full_model/full_VarData.tab ./',i,'/full_model/full_FacData.tab',sep=""))
allData_full_likelihoods <- as.numeric(substring(string[-1],17))
###########################################################################################
###########################################################################
######################## D calculation ###################################
D <- 2*(sum(allData_full_likelihoods) - (sum(ANs_AN_likelihoods)+sum(Ts_T_likelihoods)))
###########################################################################
################# P val calculation using null distr. #####################
nruns <- 100
Ds <- vector(length=nruns,mode="numeric")
for (run in 1:nruns) {
cur <- sample(x=1:(length(Ts)+length(ANs)),size=length(Ts),replace=FALSE)
# Ts
tempFac_T <- tempFac[cur,]
rownames(tempFac_T) <- Ts
eval(parse(text = paste('write.table(', paste('tempFac_T,file = "./',i,'/null/T_model/T_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
prior_pr <- apply(promoter_all[cur,],2,mean)
prior_gb <- apply(body_all[cur,],2,mean)
if (smooth_1d > 0) {
prior_expr <- kernsm(apply(expr_all[cur,],2,mean),h=smooth_e)
prior_expr <- prior_expr@yhat/sum(prior_expr@yhat)
} else prior_expr <- apply(expr_all[cur,],2,mean)
string <- paste(prior_pr,collapse=",")
promoterPots <- paste("\nNAME:\t\tpot_",promoter_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_pr,"]((",string,"))\nPC_MAT:\t\t[1,",res_pr,"]((",paste(rep(1,res_pr),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_gb,collapse=",")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBody_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_gb,"]((",string,"))\nPC_MAT:\t\t[1,",res_gb,"]((",paste(rep(1,res_gb),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_expr,collapse=",")
expr.pots <- paste("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\n",sep="",collapse="")
result <- tensor_product(body_all[cur,],expr_all[cur,],smooth_h=smooth_gb)
expr.m <- paste("NAME:\t\tpot_EXPR.M.GB\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(pseudo_counts_gb,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse="")
result <- tensor_product(promoter_all[cur,],expr_all[cur,],smooth_h=smooth_pr)
expr.m <- c(expr.m,paste("NAME:\t\tpot_EXPR.M.P\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(pseudo_counts_pr,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse=""))
potentials <- file(paste("./",i,"/null/T_model/factorPotentials.txt",sep=""),"w")
cat(expr.m,expr.pots,promoterPots,geneBodyPots,file=potentials)
close(potentials)
# query
string<-system(intern=TRUE,command=paste('./dfgEval_static --dfgSpecPrefix=./',i,'/null/T_model/ -l -n - ./',i,'/T_model/all/T_VarData.tab ./',i,'/null/T_model/T_FacData.tab',sep=""))
Ts_T_likelihoods <- as.numeric(substring(string[-1],17))
# ANs
tempFac_AN <- tempFac[-cur,]
rownames(tempFac_AN) <- ANs
eval(parse(text = paste('write.table(', paste('tempFac_AN,file = "./',i,'/null/AN_model/AN_FacData.tab",row.names=TRUE,col.names=TRUE,quote=FALSE,sep="\t",append=FALSE)', sep = ""))))
prior_pr <- apply(promoter_all[-cur,],2,mean)
prior_gb <- apply(body_all[-cur,],2,mean)
if (smooth_1d > 0) {
prior_expr <- kernsm(apply(expr_all[-cur,],2,mean),h=smooth_e)
prior_expr <- prior_expr@yhat/sum(prior_expr@yhat)
} else prior_expr <- apply(expr_all[-cur,],2,mean)
string <- paste(prior_pr,collapse=",")
promoterPots <- paste("\nNAME:\t\tpot_",promoter_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_pr,"]((",string,"))\nPC_MAT:\t\t[1,",res_pr,"]((",paste(rep(1,res_pr),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_gb,collapse=",")
geneBodyPots <- paste("\nNAME:\t\tpot_",geneBody_CpGs,"\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_gb,"]((",string,"))\nPC_MAT:\t\t[1,",res_gb,"]((",paste(rep(1,res_gb),collapse=","),"))\n",sep="",collapse="")
string <- paste(prior_expr,collapse=",")
expr.pots <- paste("\nNAME:\t\tpot_EXPR.likelihood\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\nNAME:\t\tpot_EXPR.prior\nTYPE:\t\trowNorm\nPOT_MAT:\t[1,",res_expr,"]((",string,"))\nPC_MAT:\t\t[1,",res_expr,"]((",paste(rep(1,res_expr),collapse=","),"))\n\n",sep="",collapse="")
result <- tensor_product(body_all[-cur,],expr_all[-cur,],smooth_h=smooth_gb)
expr.m <- paste("NAME:\t\tpot_EXPR.M.GB\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_gb),collapse=","),"]((",paste(apply(pseudo_counts_gb,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse="")
result <- tensor_product(promoter_all[-cur,],expr_all[-cur,],smooth_h=smooth_pr)
expr.m <- c(expr.m,paste("NAME:\t\tpot_EXPR.M.P\nTYPE:\t\trowNorm\nPOT_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(result,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\nPC_MAT:\t\t[",paste(c(res_expr,res_pr),collapse=","),"]((",paste(apply(pseudo_counts_pr,1,paste,collapse=","),collapse="),\n\t\t\t("),"))\n\n",sep="",collapse=""))
potentials <- file(paste("./",i,"/null/AN_model/factorPotentials.txt",sep=""),"w")
cat(expr.m,expr.pots,promoterPots,geneBodyPots,file=potentials)
close(potentials)
# query
string<-system(intern=TRUE,command=paste('./dfgEval_static --dfgSpecPrefix=./',i,'/null/AN_model/ -l -n - ./',i,'/AN_model/all/AN_VarData.tab ./',i,'/null/AN_model/AN_FacData.tab',sep=""))
ANs_AN_likelihoods <- as.numeric(substring(string[-1],17))
Ds[run] <- 2*(sum(allData_full_likelihoods) - (sum(ANs_AN_likelihoods)+sum(Ts_T_likelihoods)))
}
if (sd(Ds) != 0) zscore <- (D - mean(Ds)) / sd(Ds) else zscore <- -6
pval_zscore <- pnorm(zscore,lower.tail=FALSE)
###########################################################################################
eval(parse(text=paste('write.table(x=t(c(pval_zscore,D,mean(Ds),sd(Ds),zscore)), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'.result")',sep="")))
eval(parse(text=paste('write.table(x=t(breaksEXPRESSION), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'.result")',sep="")))
eval(parse(text=paste('write.table(x=t(breaksBODY), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'.result")',sep="")))
eval(parse(text=paste('write.table(x=t(breaksPROMOTER), col.names=FALSE, row.names=FALSE, append=TRUE, file="./',i,'.result")',sep="")))
system(intern=TRUE,command=paste('tar cf ',i,'.tar ',i,sep=""))
cat(paste("done ",i," in ", sprintf("%.2f", (proc.time()[3]-ptm)/60)," minutes\n",sep=""))
}