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2way_param_xfold_general_conf.R
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2way_param_xfold_general_conf.R
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#################################################
# prepare workspace
#################################################
args <- commandArgs(trailingOnly = TRUE)
beg <- as.numeric(args[1]) # first ID to process
end <- as.numeric(args[2]) # last consequitve ID to process
load("./data_BRCA_progressing.RData") # data to work on, includes folds_g1 and folds_g2
#################################################
# configure the model a bit
#################################################
nbins_e <- 1000 # number of discrete bins to divide expression data into
nbins_meth <- 100 # number of discrete bins to divide methylation data types (promoter and gene body meth.) into
#################################################
# libs and common functions
#################################################
library(dgRaph)
library(dplyr)
library(VGAM)
library(bbmle)
library(pROC) # calculates AUC
loglik <- function(alpha, beta){-sum(dbetabinom.ab(xi, ni, alpha, beta, log = T))}
# data checks
if (length(folds_g1) != length(folds_g2)) stop("Uneven number of x-folds")
nfolds <- length(folds_g1)
length_g1 <- length(unlist(folds_g1))
length_g2 <- length(unlist(folds_g2))
for (i in beg:end) {
cat(paste("doing ",i,"\n",sep=""))
ptm <- proc.time()[3]
model <- list()
scores_xval <- vector(length=nrow(data_BRCA_progressing[[i]]))
# calculate common discretization upper and lower limits
expr <- as.data.frame(data_BRCA_progressing[[i]]) %>%
mutate(EXPR = (read_count + 1) / lib_size) %>%
select(EXPR)
pr <- as.data.frame(data_BRCA_progressing[[i]]) %>%
select(starts_with("pr", ignore.case = F))
n_pr_cpg <- ncol(pr)
gb <- as.data.frame(data_BRCA_progressing[[i]]) %>%
select(starts_with("gb", ignore.case = F))
n_gb_cpg <- ncol(gb)
# upper and lower limits
min_p <- min(pr)-0.1
max_p <- max(pr)+0.1
min_gb <- min(gb)-0.1
max_gb <- max(gb)+0.1
min_e <- max(0,min(expr)-0.001*min(expr))
max_e <- max(expr)+0.001*max(expr)
# common data frame with observed expression point estimate
df <- data_BRCA_progressing[[i]] %>%
as.data.frame() %>%
mutate(EXPR = (read_count+1) / lib_size) %>%
mutate(EXPR = as.integer(cut(EXPR, breaks = seq(min_e,max_e,length.out = nbins_e+1), labels = c(1:nbins_e)))) %>%
mutate(PR_overall = NA) %>%
mutate(GB_overall = NA) %>%
mutate_each(funs(bin = as.integer(cut(., breaks = seq(min_p,max_p,length.out = nbins_meth+1), labels = c(1:nbins_meth)))), PR = starts_with("pr", ignore.case = F)) %>%
mutate_each(funs(bin = as.integer(cut(., breaks = seq(min_gb,max_gb,length.out = nbins_meth+1), labels = c(1:nbins_meth)))), GB = starts_with("gb", ignore.case = F)) %>%
select(-starts_with("pr", ignore.case = F), -starts_with("gb", ignore.case = F), -lib_size, -read_count)
# common data frame with unobserved expression
df_wo_expr <- df %>%
mutate(EXPR = NA)
# Base models
varDim <- c(nbins_e,rep(nbins_meth, 2+n_pr_cpg+n_gb_cpg))
facPot <- list(linregPotential(dim = c(nbins_e, nbins_meth)), # PR_overall | EXPR
linregPotential(dim = c(nbins_e, nbins_meth)), # GB_overall | EXPR
linregPotential(dim = c(nbins_meth, nbins_meth), range1 = c(min_p, max_p), range2 = c(min_p,max_p), alpha = 1, beta = 0, var = (max_p-min_p)**2/(nbins_meth/4)), # PR_i | PR
linregPotential(dim = c(nbins_meth, nbins_meth), range1 = c(min_gb, max_gb), range2 = c(min_gb,max_gb), alpha = 1, beta = 0, var = (max_gb-min_gb)**2/(nbins_meth/4))) # GB_i | GB
facNbs <- c(list(c(1,2)), # PR_overall | EXPR
list(c(1,3)), # GB_overall | EXPR
lapply(4:(3+n_pr_cpg), FUN=function(i){c(2,i)}), # PR_i | PR_overall
lapply((1+3+n_pr_cpg):(3+n_pr_cpg+n_gb_cpg), FUN=function(i){c(3,i)})) # GB_i | GB_overall
potMap <- c(1, 2, rep(3, n_pr_cpg), rep(4, n_gb_cpg))
optimFun <- list(linreg1 = linregOptimize(range1 = c(min_e,max_e), range2 = c(min_p,max_p)),
linreg2 = linregOptimize(range1 = c(min_e,max_e), range2 = c(min_gb,max_gb)),
fixedLink1 = fixedlinkOptimize(range1 = c(min_p,max_p), range2 = c(min_p,max_p), alpha = 1, beta = 0),
fixedLink2 = fixedlinkOptimize(range1 = c(min_gb,max_gb), range2 = c(min_gb,max_gb), alpha = 1, beta = 0))
dfg_base <- dfg(varDim, facPot, facNbs, potMap, varNames = names(df))
dfg_g1 <- dfg_base
dfg_g2 <- dfg_base
# dfg with beta prior
cur_length <- length(varDim)
varDimPrior <- varDim
facPotPrior <- facPot
facPotPrior[[5]] <- matrix(1, 1,nbins_e)
facNbsPrior <- facNbs
facNbsPrior[[cur_length]] <- 1
potMapPrior <- potMap
potMapPrior[cur_length] <- 5
dfg_prior_base <- dfg(varDimPrior, facPotPrior, facNbsPrior, potMapPrior, varNames = names(df))
# begin x-fold here
for (fold in 1:nfolds) { # iterate through nfolds folds
# identify training and validation sets, assumes g1 and g2 sets are adjacent
g1_xfold_training <- setdiff((1:length_g1),unlist(folds_g1[[fold]]))
g2_xfold_training <- setdiff(length_g1+(1:length_g2),unlist(folds_g2[[fold]]))
#################################################
# G1 model
#################################################
#################################################
# Learn betabinomial
#################################################
xini <- as.data.frame(data_BRCA_progressing[[i]][g1_xfold_training,1:2])
ni <- as.integer(xini$lib_size)
xi <- as.integer(xini$read_count+1)
m0 <- mle2(minuslogl = loglik, start = list(alpha = 1, beta = 1), method = "L-BFGS-B", lower=c(alpha = 0.0001, beta = 0.0001))
alpha <- coef(m0)['alpha']
beta <- coef(m0)['beta']
# Posteriors
alphaPost <- alpha + xini$read_count
betaPost <- beta + xini$lib_size - xini$read_count
# Data list
dataList <- list()
dataList[[1]] <- lapply(1:length(g1_xfold_training), FUN=function(i){
breaks <- seq(min_e, max_e, length.out = nbins_e+1)
diff( pbeta(breaks, alphaPost[i], betaPost[i]))})
# Train
dfg_g1 <- train(data = df_wo_expr[g1_xfold_training,],
dataList = dataList,
dfg = dfg_g1,
optim = c("linreg1", "linreg2", "fixedLink1", "fixedLink2"),
optimFun = optimFun, iter.max = 5000, threshold = 1e-5)
# Model with beta prior
dfg_g1_prior <- dfg_prior_base
potentials(dfg_g1_prior) <- c(potentials(dfg_g1), list(betaPotential(dim=c(1,nbins_e), range=c(min_e,max_e),alphas=alpha, betas=beta)))
#################################################
# G2 model
#################################################
# Learn betabinomial
xini <- as.data.frame(data_BRCA_progressing[[i]][g2_xfold_training,1:2])
ni <- as.integer(xini$lib_size)
xi <- as.integer(xini$read_count+1)
m0 <- mle2(minuslogl = loglik, start = list(alpha = 1, beta = 1), method = "L-BFGS-B", lower=c(alpha = 0.0001, beta = 0.0001))
alpha <- coef(m0)['alpha']
beta <- coef(m0)['beta']
# Posteriors
alphaPost <- alpha + xini$read_count
betaPost <- beta + xini$lib_size - xini$read_count
# Data list
dataList <- list()
dataList[[1]] <- lapply(1:length(g2_xfold_training), FUN=function(i){
breaks <- seq(min_e, max_e, length.out = nbins_e+1)
diff( pbeta(breaks, alphaPost[i], betaPost[i]))})
# Train
dfg_g2 <- train(data = df_wo_expr[g2_xfold_training,],
dataList = dataList,
dfg = dfg_g2,
optim = c("linreg1", "linreg2", "fixedLink1", "fixedLink2"),
optimFun = optimFun, iter.max = 5000, threshold = 1e-5)
# Model with beta prior
dfg_g2_prior <- dfg_prior_base
potentials(dfg_g2_prior) <- c(potentials(dfg_g2), list(betaPotential(dim=c(1,nbins_e), range=c(min_e,max_e),alphas=alpha, betas=beta)))
#################################################
# Evaluation and performance
#################################################
# training data scores
df_train <- df_wo_expr[c(g1_xfold_training,g2_xfold_training),]
likelihood1 <- likelihood(dfg = dfg_g2_prior, data = df_train, log = T)
likelihood1[which(is.na(likelihood1))] <- vapply(likelihood1[which(is.na(likelihood1))], FUN= function(x) rnorm(n=1,mean=-500,sd=0.01), FUN.VALUE = 500)
likelihood2 <- likelihood(dfg = dfg_g1_prior, data = df_train, log = T)
likelihood2[which(is.na(likelihood2))] <- vapply(likelihood2[which(is.na(likelihood2))], FUN= function(x) rnorm(n=1,mean=-500,sd=0.01), FUN.VALUE = 500)
scores_train <- likelihood1 - likelihood2
# evaluation data scores
df_eval <- df_wo_expr[-c(g1_xfold_training,g2_xfold_training),]
likelihood1 <- likelihood(dfg = dfg_g2_prior, data = df_eval, log = T)
likelihood1[which(is.na(likelihood1))] <- vapply(likelihood1[which(is.na(likelihood1))], FUN= function(x) rnorm(n=1,mean=-500,sd=0.01), FUN.VALUE = 500)
likelihood2 <- likelihood(dfg = dfg_g1_prior, data = df_eval, log = T)
likelihood2[which(is.na(likelihood2))] <- vapply(likelihood2[which(is.na(likelihood2))], FUN= function(x) rnorm(n=1,mean=-500,sd=0.01), FUN.VALUE = 500)
scores_eval <- likelihood1 - likelihood2
# Metrics of performance
model$auc_training[fold] <- auc(predictor=scores_train,response=c(rep("G1",length(g1_xfold_training)),rep("G2",length(g2_xfold_training))))
model$auc_evaluation[fold] <- auc(predictor=scores_eval,response=c(rep("G1",(length_g1-length(g1_xfold_training))),rep("G2",(length_g2-length(g2_xfold_training)))))
model$kls[fold] <- kl(dfg_g1_prior,dfg_g2_prior)
scores_xval[c(folds_g1[[fold]],folds_g2[[fold]])] <- scores_eval
} # x-validation ends here
model$scores <- scores_xval
# save model results
eval(parse(text=paste('save(model, file="./',i,'_model.RData")',sep="")))
cat(paste("done evaluating for ",i," in ", sprintf("%.2f", (proc.time()[3]-ptm)/60)," minutes\n",sep=""))
}