scripts/cnpbayes.trio.comparison.batch.dup.R

In CNPBayes: Bayesian mixture models for copy number polymorphisms

#! /usr/bin/env Rscript

library(devtools)
#library(Rcpp)
library(tibble)
library(magrittr)
setwd("/dcs01/chaklab/chaklab1/users/mchou")
load_all("CNPBayes_trios")
setwd("/dcs01/chaklab/chaklab1/users/mchou/batchrun2")
ab <- commandArgs(trailingOnly=TRUE)  %>%
  as.integer
params.all <- readRDS(file.path("./params_dup", paste0("params_", ab, ".rds")))
seeds <- readRDS(file.path("./params_dup", "params_seeds.rds"))
seed <- seeds[ab]
set.seed(seed)
##--------------------------------------------------
##
message("cnpbayes_trios")
##
##--------------------------------------------------

# note maplabel and hp defined manually here for now
maplabel <- c(2,3,4)
hp <- HyperparametersTrios(k = 3)
params <- params.all[,1:3]
N <- params.all[1,4]

# sim truth
nbatch <- 1
mprob <- mprob.matrix(tau=c(0.5, 0.5, 0.5), maplabel, error=0.001)
truth <- simulate_data_multi2(params, N=N,
                              batches = rep(c(1:nbatch),
                                            length.out = 3*N),
                              error=0.001, mprob, maplabel)

is_offspring <- truth$data$family_member=="o"
cn_offspring <- truth$data$copy_number[is_offspring]

if (length(table(cn_offspring))!=hp@k) {
  truth <- simulate_data_multi2(params, N=N,
                                batches = rep(c(1:nbatch),
                                              length.out = 3*N),
                                error=0.001, mprob, maplabel)
}

# module for turning off ParamUpdates
#up <- rep(1L, 10)
#names(up) <- c("theta", "sigma2",
#              "pi", "mu",
#               "tau2",
#              "nu.0",
#               "sigma2.0",
#               "z.parents",
#               "z.offspring",
#               "pi.parents")
#up["z.parents"] <- 0L
#up["z.offspring"] <- 1L

#initialise model and mp
#mp <- McmcParams(iter=1000, burnin=1000, thin=1, param_updates=up)
mp <- McmcParams(iter=3000, burnin=2000, thin=1)
#model <- TBM(triodata=truth$data,
#             hp=hp,
#             mp=mp,
#             mprob=mprob,
#             maplabel=maplabel)


for(i in 1:500){
  set.seed(123456)
  mp <- McmcParams(iter=1, burnin=i)
  model <- gibbs_trios(model="TBM", dat=as.tibble(truth$data),
                       batches=truth$data$batches,
                       mp=mp, k_range=c(3, 3), max_burnin=i)[[1]]
  setwd("/dcs01/chaklab/chaklab1/users/mchou/batchrun2/resultsdup")
  saveRDS(model, file=paste0("throwaway_model.",ab,".rds"))
}

#mp2 <- McmcParams(iter=4000, burnin=2000, thin=1)
mb2 <- gibbs(model="MB", dat=truth$data$log_ratio,
             batches=truth$data$batches,
             mp=mp, k_range=c(3, 3), max_burnin=8000)

#mcmcParams(model) <- mp
#model <- posteriorSimulation(model)
snr <- snr.calc(model[[1]])

# this is for the trio model
results <- z2cn(model[[1]], maplabel)
is_offspring <- model[[1]]@triodata$family_member=="o"
cn.truth <- truth$data$copy_number
cn.truth.parent <- cn.truth[!is_offspring]
cn.truth.child <- cn.truth[is_offspring]
results.parent <- results@z[!is_offspring]
results.child <- results@z[is_offspring]
#cn.truth.parent2 <- as.factor(cn.truth.parent)
#cn.truth.child2 <- as.factor(cn.truth.child)
#results.parent2 <- as.factor(results.parent)
#results.child2 <- as.factor(results.child)
#cn.compare.parent <- table(results.parent2, cn.truth.parent2)
#cn.compare.child <- table(results.child2, cn.truth.child2)

prop.true.overall <- sum(cn.truth == results@z) / length(cn.truth)
prop.true.parent <- sum(cn.truth.parent == results.parent) / length(cn.truth.parent)
prop.true.child <- sum(cn.truth.child == results.child) / length(cn.truth.child)

# these lines manually modified based on maplabel(del/dup)
#sens.spec.par <- modified.sens.spec.calc(cn.compare.parent,cn.type="DEL")
#sens.par <- sens.spec.par$sensitivity
#spec.par <- sens.spec.par$specificity
#sens.spec.chd <- modified.sens.spec.calc(cn.compare.child,cn.type="DEL")
#sens.chd <- sens.spec.chd$sensitivity
#spec.chd <- sens.spec.chd$specificity

# this is for the MB model comparison
results.mb <- z2cn(mb2[[1]], maplabel)
#cn.truth2 <- as.factor(cn.truth)
#results.mb2 <- as.factor(results.mb@z)
#cn.compare.overall.mb <- table(results.mb2, cn.truth2)

prop.true.overall.mb <- sum(cn.truth == results.mb@z) / length(cn.truth)

#sens.spec.mb <- modified.sens.spec.calc(cn.compare.overall.mb,cn.type="DEL")
#sens.mb <- sens.spec.mb$sensitivity
#spec.mb <- sens.spec.mb$specificity

true.stats <- component_stats(truth$data)
truth.child.pi <- table(cn.truth.child)/length(cn.truth.child)
child.pi <- table(results.child)/length(results.child)

# input all the info
summaryResults <- list(params = params.all,
                       SimTruth = true.stats,
                       SimChildPi = truth.child.pi,
                       TrioCNcall = results@z,
                       MBCNcall = results.mb@z,
                       SNR = snr, Accuracy = prop.true.overall,
                       AccuracyParents = prop.true.parent,
                       AccuracyChild = prop.true.child,
                       AccuracyMB = prop.true.overall.mb,
                       ModelPi = model[[1]]@pi,
                       ModelChildPi = child.pi,
                       ModelTheta = model[[1]]@theta,
                       ModelSigma2 = model[[1]]@sigma2
)

## save to some output directory
pathout <- file.path("/dcs01/chaklab/chaklab1/users/mchou/batchrun2/resultsdup")
#pathout <- file.path("~/Desktop/Chakravarti_Lab/git")
results.out <- paste0("params_", ab, ".txt")
#saveRDS(summary.results, file=file.path(pathout, params.rds))
setwd("/dcs01/chaklab/chaklab1/users/mchou/batchrun2/resultsdup")
write.table(as.data.frame(summaryResults),file=file.path(pathout, results.out), quote=F,sep="\t",row.names=F)