This vignette includes the code to perform the global sensitivity analysis described in the paper.
library(httk)
library(data.table)
library(parallel)Basically, the idea of the Sobol method is to apportion variance: to estimate how much of the variance in a model’s output is contributed by variance in each of the model parameters. The fraction of output variance attributable to each parameter is called the sensitivity index for that parameter.
The sensitivity indexes can be estimated using the following method. Generate two parameter matrices, independently of one another. Call them Matrix A and Matrix B. Evaluate the model using A and B, to get two vectors of output, fA and fB. Then, swap one column at a time between A and B. If you swap column i, the resulting matrices can be called ABi (for matrix A, with column i from matrix B) and BAi (for matrix B, with column i from matrix A). Evaluate the model for the column-swapped matrices to get another two vectors of output, fABi and fBAi. Then first-order and total sensitivity indices can be estimated from the correlation coefficients between output vectors (Glen & Isaacs 2012).
With HTTK-Pop, this means we need to generate two virtual populations with the same specifications. For each chemical, we then need to add Funbound.plasma and Clint columns to each population matrix, and convert it into a matrix of HTTK model parameters. That will give matrices A and B, for each chemical. Then, we need to swap columns, evaluate the model, and compute the sensitivity indices from the correlations.
Let’s get started. First, let’s generate the two virtual populations using HTTK-Pop. These are strictly physiological parameters – no chemical dependence – so we can just do this once and re-use the two virtual populations for each chemical.
We’ll generate virtual populations for each of the ten ExpoCast subpopulations of interest, to see if and how global sensitivity changes by subpopulation. In fact, we’ve already generated one set of virtual populations in the “Generating subpopulations” vignette. Let’s just re-use those as the first virtual population, and repeat the same procedure to generate a second virtual population, for each subpopulation.
We’ll use the same seed as before (based on my name), but increment it by 1, so that the two virtual populations are not identical.
seed.int <- TeachingDemos::char2seed('Caroline Ring', set=FALSE)+1We set up the subpopulation specifications in the same way as before.
nsamp<-1000
ExpoCast.group<-list("Total",
"Age.6.11",
"Age.12.19",
"Age.20.65",
"Age.GT65",
"BMIgt30",
"BMIle30",
"Females",
"Males",
"ReproAgeFemale",
"Age.20.50.nonobese")
gendernum <- c(rep(list(NULL),7),
list(list(Male=0, Female=1000)),
list(list(Male=1000, Female=0)),
list(list(Male=0, Female=1000)),
list(NULL))
agelim<-c(list(c(0,79),
c(6,11),
c(12,19),
c(20,65),
c(66,79)),
rep(list(c(0,79)),4),
list(c(16,49)),
list(c(20,50)))
bmi_category <- c(rep(list(c('Underweight',
'Normal',
'Overweight',
'Obese')),
5),
list('Obese',
c('Underweight','Normal', 'Overweight')),
rep(list(c('Underweight',
'Normal',
'Overweight',
'Obese')),
3),
list(c('Underweight', 'Normal', 'Overweight')))Then we just evaluate another set of virtual populations, using the direct resampling method.
tmpfun <- function(gendernum, agelim, bmi_category, ExpoCast_grp,
nsamp, method){
result <- tryCatch({
pops <- httk::httkpop_generate(
method=method,
nsamp=nsamp,
gendernum = gendernum,
agelim_years = agelim,
weight_category = bmi_category)
filepart <- switch(method,
'virtual individuals' = 'vi',
'direct resampling' = 'dr')
saveRDS(object=pops,
file=paste0('data/httkpop_',
filepart,
'_',
ExpoCast_grp,
'2.Rdata')) #Note we've added a 2 to the file name!
return(0)
}, error = function(err){
print(paste('Error occurred:', err))
return(1)
})
}
cluster <- parallel::makeCluster(40,
outfile='subpopulations_parallel_out2.txt')
evalout <- parallel::clusterEvalQ(cl=cluster,
{library(data.table)
library(httk)})
parallel::clusterExport(cl = cluster,
varlist = 'tmpfun')
#Set seeds on all workers for reproducibility
parallel::clusterSetRNGStream(cluster,
seed.int)
out_dr <- parallel::clusterMap(cl=cluster,
fun = tmpfun,
gendernum=gendernum,
agelim=agelim,
bmi_category=bmi_category,
ExpoCast_grp = ExpoCast.group,
MoreArgs = list(nsamp = nsamp,
method = 'direct resampling')
)
parallel::stopCluster(cluster)Great. Now, let’s think of the procedure as looping first over the ten subpopulations of interest, and then over the chemicals in the HTTK data set. So first, let’s think about what we need to do for each chemical, given a subpopulation.
We need to draw Funbound.plasma and Clint samples for the two virtual populations and convert into HTTK parameter matrices. That will give us matrix A and matrix B. Then, we need to evaluate yA and yB. Then, we need to loop over the HTTK model parameters – the columns of A and B – and swap one column at a time between A and B. For parameter i, we then need to evaluate yABi and yBAi. Then, we need to compute the correlations, and from them the sensitivity indices for parameter i.
So we can write some pseudocode like this:
Let’s start with the innermost loop: what we want to do for each parameter i.
doforeachparam <- function(i,
indiv.A,
indiv.B,
this.chemcas,
model,
species='Human',
css.method,
f.A, #already evaluated model for A
f.B, #already evaluated model for B
g0, #normalized f.A
g0prime, #normalized f.B
p0){ #spurious correlation betweenf.A and f.B
#make copies so as not to change these data.tables outside the function
indiv.A <- copy(indiv.A)
indiv.B <- copy(indiv.B)
#First, let's swap column i.
indiv.ABi <- copy(indiv.A)
indiv.ABi[, (i):= indiv.B[, i, with=FALSE]]
indiv.BAi <- copy(indiv.B)
indiv.BAi[, (i):=indiv.A[, i, with=FALSE]]
#Convert to HTTK parameters for matrix ABi and matrix BAi
indiv.ABi.httk <- httk::convert_httk(indiv.model.bio = indiv.ABi,
model = model,
this.chem = this.chemcas)
indiv.BAi.httk <- httk::convert_httk(indiv.model.bio = indiv.BAi,
model = model,
this.chem = this.chemcas)
#If model is 3compartmentss, convert Funbound.plasma to Funbound.blood
if (model=="3compartmentss"){
#First, get the default parameters used for the Schmitt method of estimating
#partition coefficients.
pschmitt <- httk::parameterize_schmitt(chem.cas=this.chemcas,
species='Human')
convert_Fup <- function(DT.bio, DT.httk, pschmitt, this.chemcas){
DT.bio <- copy(DT.bio)
DT.httk <- copy(DT.httk)
#next, replace the single default value for Funbound.plasma with the vector
#of Funbound.plasma values from the virtual population data.table.
pschmitt$Funbound.plasma<-DT.httk[, Funbound.plasma]
#Now, predict the partitioning coefficients using Schmitt's method. The
#result will be a list of numerical vectors, one vector for each
#tissue-to-plasma partitioning coefficient, and one element of each vector
#for each individual. The list element names specify which partition
#coefficient it is, e.g. Kliver2plasma, Kgut2plasma, etc.
PCs <- httk::predict_partitioning_schmitt(parameters=pschmitt,
chem.cas=this.chemcas,
species='Human')
#Compute predicted Rblood2plasma (amount in blood/amount in plasma)
Rb2p <- 1 - DT.bio$hematocrit + DT.bio$hematocrit *
PCs[["Krbc2pu"]] *
DT.httk$Funbound.plasma
Funbound.blood <- DT.httk$Funbound.plasma/Rb2p
return(list(Rb2p=Rb2p,
Funbound.blood=Funbound.blood))
}
Fconvert.ABi <- convert_Fup(DT.bio=indiv.ABi,
DT.httk=indiv.ABi.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
Fconvert.BAi <- convert_Fup(DT.bio=indiv.BAi,
DT.httk=indiv.BAi.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
indiv.ABi.httk[, Funbound.plasma:=Fconvert.ABi$Funbound.blood]
indiv.BAi.httk[, Funbound.plasma:=Fconvert.BAi$Funbound.blood]
}
#Next, let's evaluate the model.
if (tolower(css.method)=='analytic') {
f.ABi <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.ABi.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
f.BAi <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.BAi.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
if (model=="3compartmentss"){
f.ABi <- f.ABi/Fconvert.ABi$Rb2p
f.BAi <- f.BAi/Fconvert.BAi$Rb2p
}
}
else if (tolower(css.method)=='full'){
f.ABi <- apply(X=indiv.ABi.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
f.BAi <- apply(X=indiv.BAi.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
}
#Finally, let's estimate the correlations and the sensitivity indexes. This code uses the same notation as Glen and Isaacs, so you can follow along from their paper.
gj <- (f.ABi-mean(f.ABi))/sqrt(var(f.ABi) * (length(f.ABi)-1) / length(f.ABi))
gjprime<-(f.BAi-mean(f.BAi))/sqrt(var(f.BAi) * (length(f.BAi)-1) / length(f.BAi))
csj <- mean(g0prime*gj)
csminusj <- mean(g0*gj)
cdj <- sum(g0prime*gj + g0*gjprime)/(2*length(g0))
cdminusj <- sum(g0*gj + g0prime*gjprime)/(2*length(g0))
pj <- sum(g0*g0prime + gj*gjprime)/(2*length(g0))
caj <- (cdj-pj*cdminusj)/(1-pj^2)
caminusj <- (cdminusj - pj*cdj)/(1-pj^2)
Si <- cdj - pj*(caminusj)/(1-caj*caminusj)
Ti <- 1 - cdminusj + pj*caj/(1-caj*caminusj)
return(list(Ti=Ti,
Si=Si,
p0=p0,
pj=pj,
csj=csj,
csminusj=csminusj,
cdj=cdj,
cdminusj=cdminusj,
caj=caj,
caminusj=caminusj))
}Great. Now take one step out, and define a function for what we need to do for each chemical.
doforeachchem <- function(this.chemcas, #CAS for one chemical
model, #HTTK model to use
species='Human', #Species for HTTK
nsamp, #Number of people in virtual population
css.method, #'analytic' or 'full'
sigma.factor=0.3, #coefficient of variation
indiv.model.bio1, #output of httkbio()
indiv.model.bio2, #output of httkbio()
ExpoCast.group, #subpopulation of interest
poormetab, #TRUE or FALSE
fup.censor){ #TRUE or FALSE
#To avoid making changes outside the function
indiv.model.bio1 <- copy(indiv.model.bio1)
indiv.model.bio2 <- copy(indiv.model.bio2)
#First draw Funbound.plasma and Clint
indiv.A <- cbind(indiv.model.bio1,
httk::draw_fup_clint(this.chem=this.chemcas,
nsamp=nrow(indiv.model.bio1),
sigma.factor=sigma.factor,
poormetab=poormetab,
fup.censor=fup.censor))
indiv.B <- cbind(indiv.model.bio2,
httk::draw_fup_clint(this.chem=this.chemcas,
nsamp=nrow(indiv.model.bio2),
sigma.factor=sigma.factor,
poormetab=poormetab,
fup.censor=fup.censor))
#Convert to HTTK parameters for matrix A and matrix B
indiv.A.httk <- httk::convert_httk(indiv.model.bio = indiv.A,
model = model,
this.chem = this.chemcas)
indiv.B.httk <- httk::convert_httk(indiv.model.bio = indiv.B,
model = model,
this.chem = this.chemcas)
#If model is 3compartmentss, convert Funbound.plasma to Funbound.blood
if (model=="3compartmentss"){
#First, get the default parameters used for the Schmitt method of estimating
#partition coefficients.
pschmitt <- httk::parameterize_schmitt(chem.cas=this.chemcas,
species='Human')
convert_Fup <- function(DT.bio, DT.httk, pschmitt, this.chemcas){
DT.bio <- copy(DT.bio)
DT.httk <- copy(DT.httk)
#next, replace the single default value for Funbound.plasma with the vector
#of Funbound.plasma values from the virtual population data.table.
pschmitt$Funbound.plasma<-DT.httk[, Funbound.plasma]
#Now, predict the partitioning coefficients using Schmitt's method. The
#result will be a list of numerical vectors, one vector for each
#tissue-to-plasma partitioning coefficient, and one element of each vector
#for each individual. The list element names specify which partition
#coefficient it is, e.g. Kliver2plasma, Kgut2plasma, etc.
PCs <- httk::predict_partitioning_schmitt(parameters=pschmitt,
chem.cas=this.chemcas,
species='Human')
Rb2p <- 1 - DT.bio$hematocrit + DT.bio$hematocrit *
PCs[["Krbc2pu"]] *
DT.httk$Funbound.plasma
Funbound.blood <- DT.httk$Funbound.plasma/Rb2p
return(list(Rb2p=Rb2p,
Funbound.blood=Funbound.blood))
}
Fconvert.A <- convert_Fup(DT.bio=indiv.A,
DT.httk=indiv.A.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
Fconvert.B <- convert_Fup(DT.bio=indiv.B,
DT.httk=indiv.B.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
indiv.A.httk[, Funbound.plasma:=Fconvert.A$Funbound.blood]
indiv.B.httk[, Funbound.plasma:=Fconvert.B$Funbound.blood]
}
#Evaluate model
if (tolower(css.method)=='analytic') {
f.A <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.A.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
f.B <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.B.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
if (model=="3compartmentss"){
f.A <- f.A/Fconvert.A$Rb2p
f.B <- f.B/Fconvert.B$Rb2p
}
}
else if (tolower(css.method)=='full'){
f.A <- apply(X=indiv.A.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
f.B <- apply(X=indiv.B.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
}
#Compute normalized data (number of standard deviations from the mean)
g0<-(f.A-mean(f.A))/sqrt(var(f.A) * (length(f.A)-1) / length(f.A))
g0prime <- (f.B-mean(f.B))/sqrt(var(f.B) * (length(f.B)-1) / length(f.B))
#Compute spurious correlation term between f.A and f.B
p0 <- mean(g0*g0prime)
#Next, define the list of HTTK parameters that constitute the physiological parameters for this model.
#Strictly speaking this should be all of the columns in indiv.model.bio1, except for liver.density, which is a fixed value.
phys.par <- names(indiv.model.bio1)[names(indiv.model.bio1)!=
'liver.density']
#Add Funbound.plasma
parlist <- list('Funbound.plasma',
'Clint',
phys.par)
#Note: this is a list with three members!
#Sensitivity is calculated to the *group* of physiological parameters *as a whole*, not to each one individually.
#If Clint is fixed at zero,
#don't test sensitivity to it
if (all(indiv.A[, Clint]==0)){
parlist <- parlist[parlist!='Clint']
}
#Now loop over parameters (and groups thereof)
eff.list <- lapply(parlist,
doforeachparam,
indiv.A=indiv.A,
indiv.B=indiv.B,
this.chemcas=this.chemcas,
model=model,
species=species,
css.method=css.method,
f.A=f.A,
f.B=f.B,
g0=g0,
g0prime=g0prime,
p0=p0)
parnames <- parlist
#Name the sensitivity indexes by their parameters
#For the group of physiological parameters, just name it "phys.par"
parnames[sapply(parnames, function(x) length(x)>1)] <- 'phys.par'
names(eff.list) <- parnames
#If Clint was zero, then sensitivity to it wasn't calculated.
#Set all the sensitivity quantities to NA to indicate this.
if (all(indiv.A[, Clint]==0)){
eff.list <- c(eff.list,
list(Clint=list(Ti=NA,
Si=NA,
p0=NA,
pj=NA,
csj=NA,
csminusj=NA,
cdj=NA,
cdminusj=NA,
caj=NA,
caminusj=NA)))
}
#Construct a data.table to return
eff.dt <- as.data.table(do.call(rbind.data.frame,
eff.list),
keep.rownames=TRUE)
eff.dt[, chemcas:=this.chemcas]
return(eff.dt)
}Now take one more step out and define a function to do for each subpopulation.
doforeachsubpop <- function(ecg, #Short for "ExpoCast group"
nsamp,
chemlist, #list of chemicals to loop over
model, #HTTK model to use
species = 'Human', #HTTK species to use
sigma.factor = 0.3, #Coefficient of variation
css.method = 'analytic', #or 'full'
poormetab, #TRUE or FALSE
fup.censor, #TRUE or FALSE
cluster){ #The parallel cluster to use (we'll parallelize the loop over chemicals)
#First read in the virtual population data.
indiv1.dt<-readRDS(paste0('data/httkpop_',
'dr',
'_',
ecg,
'.Rdata'))
indiv2.dt<-readRDS(paste0('data/httkpop_',
'dr',
'_',
ecg,
'2.Rdata'))
#Convert the biological parameters first
indiv.model.bio1 <- httk::httkpop_bio(indiv_dt=indiv1.dt)
indiv.model.bio2 <- httk::httkpop_bio(indiv_dt=indiv2.dt)
#Now loop over the chemicals
eff.allchems <- rbindlist(
parLapplyLB(cl=cluster,
chemlist,
doforeachchem,
model=model,
species=species,
sigma.factor=sigma.factor,
css.method=css.method,
indiv.model.bio1=indiv.model.bio1,
indiv.model.bio2=indiv.model.bio2,
ExpoCast.group=ecg,
poormetab=poormetab,
fup.censor=fup.censor,
nsamp=nsamp))
#Add a column to the data.table denoting the subpopulation
eff.allchems[,ExpoCast.group:=ecg]
#And return the output
return(eff.allchems)
}Excellent! All that remains is to write the loop over the subpopulations that will call all of these functions in turn. We’ll also add loops over the values of poormetab and Funbound.plasma, to see if and how global sensitivity changes under different conditions on the distributions of those variables.
WARNING: this code may take a LONG time to run! 4 model evaluations per individual times 1000 individuals times 3 parameters times about 500 chemicals times 10 subpopulations = 6e7 model evaluations, plus overhead. While the model evaluations are vectorized for the analytic steady-state models – so 1000 model evaluations take very little time – this still can add up. On my machine, it took about 7 minutes for each combination of CLint and Funbound conditions, and about half an hour total – and that is parallelized with 10 cores.
model <- "3compartmentss"
species <- "Human"
sigma.factor <- 0.3
css.method <- "analytic"
chemlist <- httk::get_cheminfo(info="CAS",
exclude.fub.zero=FALSE)
cluster <- parallel::makeCluster(40,
outfile='globalsens_parallel_out.txt')
evalout <- parallel::clusterEvalQ(cl=cluster,
{library(data.table)
library(httk)})
parallel::clusterExport(cl = cluster,
varlist = c('doforeachparam',
'doforeachchem',
'doforeachsubpop'))
#Set seeds on all workers for reproducibility
parallel::clusterSetRNGStream(cluster,
TeachingDemos::char2seed("Caroline Ring"))
system.time({for (poormetab in c(TRUE, FALSE)){
for (fup.censor in c(TRUE, FALSE)) {
eff.all<-rbindlist(lapply(ExpoCast.group,
doforeachsubpop,
nsamp=nsamp,
chemlist=chemlist,
model=model,
species=species,
sigma.factor=sigma.factor,
css.method=css.method,
poormetab=poormetab,
fup.censor=fup.censor,
cluster=cluster))
saveRDS(eff.all,
paste0('data/',
'sens_glenisaacs_nhanes_',
'allchems_',
'allgroups_',
model,
'_',
css.method,
'_fup_censor_',
fup.censor,
'_poormetab_',
poormetab,
"_FuptoFub",
'.Rdata'))
}
}
})
parallel::stopCluster(cluster)Finally, we want to do sensitivity analysis for independent Monte Carlo, because that lets us estimate sensitivity to each physiological parameter independently.
The first thing we need to do is generate two separate populations using independent Monte Carlo. To save space, we’ll set this up as a function that takes the seed as an argument.
indep_gen <- function(nsamp=1000, sigma.factor=0.3){
COmean <- physiology.data[physiology.data$Parameter=='Cardiac Output',
'Human']
indep.bio <- data.table(Qcardiacc=truncnorm::rtruncnorm(n=nsamp,
mean=COmean,
sd=sigma.factor*COmean,
a=0)/1000*60)
indep.bio[, BW:=truncnorm::rtruncnorm(n=nsamp,
mean=physiology.data[physiology.data$Parameter=='Average BW',
'Human'],
sd=sigma.factor*physiology.data[physiology.data$Parameter=='Average BW',
'Human'],
a=0)]
indep.bio[, plasma.vol:=truncnorm::rtruncnorm(n=nsamp,
mean=physiology.data[physiology.data$Parameter=='Plasma Volume',
'Human'],
sd=sigma.factor*physiology.data[physiology.data$Parameter=='Plasma Volume',
'Human'],
a=0)/1000] #convert mL/kg to L/kg
indep.bio[, hematocrit:=truncnorm::rtruncnorm(n=nsamp,
mean=physiology.data[physiology.data$Parameter=='Hematocrit',
'Human'],
sd=sigma.factor*physiology.data[physiology.data$Parameter=='Hematocrit',
'Human'],
a=0,
b=1)]
indep.bio[, million.cells.per.gliver:=truncnorm::rtruncnorm(n=nsamp,
mean=110,
sd=sigma.factor*110,
a=0)]
all.tissues <- tissue.data$Tissue[tissue.data$Tissue!='red blood cells']
for (tissue in all.tissues){
vol.mean <- tissue.data[tissue.data$Tissue==tissue,
'Human Vol (L/kg)']
flow.mean <- tissue.data[tissue.data$Tissue==tissue,
'Human Flow (mL/min/kg^(3/4))']/
1000*60
if (tissue=='liver'){ #subtract gut flow from portal vein flow
#to get arterial flow
flow.mean <- (tissue.data[tissue.data$Tissue=='liver',
'Human Flow (mL/min/kg^(3/4))'] -
tissue.data[tissue.data$Tissue=='gut',
'Human Flow (mL/min/kg^(3/4))'])/
1000*60
}
indep.bio[, paste0('V',
tissue,
'c'):=truncnorm::rtruncnorm(n=nsamp,
mean=vol.mean,
sd=sigma.factor*vol.mean,
a=0)]
indep.bio[, paste0('Q',
tissue,
'f'):=truncnorm::rtruncnorm(n=nsamp,
mean=flow.mean,
sd=sigma.factor*flow.mean,
a=0)/Qcardiacc]
}
indep.bio[, Qtotal.liverc:=(Qliverf+Qgutf)*Qcardiacc]
indep.bio[, liver.density:=1.05]
gfr.mean<-physiology.data[physiology.data$Parameter=='GFR',
'Human']*60/1000 #convert from ml/min/kg^(3/4) to L/hr/kg(3/4)
indep.bio[, Qgfrc:=truncnorm::rtruncnorm(n=nsamp,
mean=gfr.mean,
sd=sigma.factor*gfr.mean,
a=0)]
indep.bio[,
Vartc:= plasma.vol/
(1-hematocrit)/2] #L/kgBW
indep.bio[,
Vvenc:= plasma.vol/
(1-hematocrit)/2] #L/kgBW
return(indep.bio)
}And then we call it twice to get two different populations.
TeachingDemos::char2seed("Caroline Ring")
indep.bio1 <- indep_gen()
indep.bio2 <- indep_gen()The innermost function (over parameters) will be the same, but we need to modify the function of things to do for each chemical, because we need to do sensitivity analysis for the physiological parameters independently, not as a group.
doforeachchem_indep <- function(this.chemcas, #CAS for one chemical
model, #HTTK model to use
species='Human', #Species for HTTK
nsamp, #Number of people in virtual population
css.method, #'analytic' or 'full'
sigma.factor=0.3, #coefficient of variation
indep.model.bio1, #output of indep_gen()
indep.model.bio2, #output of indep_gen()
poormetab, #TRUE or FALSE
fup.censor){ #TRUE or FALSE
#To avoid making changes outside the function
indiv.model.bio1 <- copy(indep.model.bio1)
indiv.model.bio2 <- copy(indep.model.bio2)
#First draw Funbound.plasma and Clint
indiv.A <- httk::draw_fup_clint(this.chem=this.chemcas,
indiv.bio.tmp=indiv.model.bio1,
sigma.factor=sigma.factor,
poormetab=poormetab,
fup.censor=fup.censor)
indiv.B <- httk::draw_fup_clint(this.chem=this.chemcas,
indiv.bio.tmp=indiv.model.bio2,
sigma.factor=sigma.factor,
poormetab=poormetab,
fup.censor=fup.censor)
#Convert to HTTK parameters for matrix A and matrix B
indiv.A.httk <- httk::convert_httk(indiv.model.bio = indiv.A,
model = model,
this.chem = this.chemcas)
indiv.B.httk <- httk::convert_httk(indiv.model.bio = indiv.B,
model = model,
this.chem = this.chemcas)
#If model is 3compartmentss, convert Funbound.plasma to Funbound.blood
if (model=="3compartmentss"){
#First, get the default parameters used for the Schmitt method of estimating
#partition coefficients.
pschmitt <- httk::parameterize_schmitt(chem.cas=this.chemcas,
species='Human')
convert_Fup <- function(DT.bio, DT.httk, pschmitt, this.chemcas){
DT.bio <- copy(DT.bio)
DT.httk <- copy(DT.httk)
#next, replace the single default value for Funbound.plasma with the vector
#of Funbound.plasma values from the virtual population data.table.
pschmitt$Funbound.plasma<-DT.httk[, Funbound.plasma]
#Now, predict the partitioning coefficients using Schmitt's method. The
#result will be a list of numerical vectors, one vector for each
#tissue-to-plasma partitioning coefficient, and one element of each vector
#for each individual. The list element names specify which partition
#coefficient it is, e.g. Kliver2plasma, Kgut2plasma, etc.
PCs <- httk::predict_partitioning_schmitt(parameters=pschmitt,
chem.cas=this.chemcas,
species='Human')
Rb2p <- 1 - DT.bio$hematocrit + DT.bio$hematocrit *
PCs[["Krbc2pu"]] *
DT.httk$Funbound.plasma
Funbound.blood <- DT.httk$Funbound.plasma/Rb2p
return(list(Rb2p=Rb2p,
Funbound.blood=Funbound.blood))
}
Fconvert.A <- convert_Fup(DT.bio=indiv.A,
DT.httk=indiv.A.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
Fconvert.B <- convert_Fup(DT.bio=indiv.B,
DT.httk=indiv.B.httk,
pschmitt=pschmitt,
this.chemcas=this.chemcas)
indiv.A.httk[, Funbound.plasma:=Fconvert.A$Funbound.blood]
indiv.B.httk[, Funbound.plasma:=Fconvert.B$Funbound.blood]
}
#Evaluate model
if (tolower(css.method)=='analytic') {
f.A <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.A.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
f.B <- httk::calc_analytic_css(chem.cas=this.chemcas,
parameters=indiv.B.httk,
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
recalc.blood2plasma=TRUE)
if (model=="3compartmentss"){
f.A <- f.A/Fconvert.A$Rb2p
f.B <- f.B/Fconvert.B$Rb2p
}
}
else if (tolower(css.method)=='full'){
f.A <- apply(X=indiv.A.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
f.B <- apply(X=indiv.B.httk,
MARGIN=1,
FUN=function(x) httk::calc_css(chem.cas=this.chemcas,
parameters=as.list(x),
daily.dose=1,
output.units="uM",
model=model,
species=species,
suppress.messages=TRUE,
f.change=1e-5)[['avg']])
}
#Compute normalized data (number of standard deviations from the mean)
g0<-(f.A-mean(f.A))/sqrt(var(f.A) * (length(f.A)-1) / length(f.A))
g0prime <- (f.B-mean(f.B))/sqrt(var(f.B) * (length(f.B)-1) / length(f.B))
#Compute spurious correlation term between f.A and f.B
p0 <- mean(g0*g0prime)
#Next, define the list of HTTK parameters that constitute the physiological parameters for this model.
#Strictly speaking this should be all of the columns in indiv.model.bio1, except for liver.density, which is a fixed value.
# #From previous runs, these are the only parameters that the models are sensitive to
phys.par <- switch(model,
'pbtk'=c('BW',
'hematocrit',
'million.cells.per.gliver',
'Qcardiacc',
'Qgfrc',
'Qgutf',
'Qkidneyf',
'Qliverf',
'Vliverc'),
'3compartment'=c('BW',
'hematocrit',
'million.cells.per.gliver',
'Qgfrc',
'Qgutf',
'Qliverf',
'Vliverc'),
'3compartmentss'=c('BW',
'million.cells.per.gliver',
'Qgfrc',
'Qtotal.liverc',
'Vliverc'),
'1compartment'=c('BW',
'million.cells.per.gliver',
'Qgfrc',
'Qtotal.liverc',
'Vliverc'))
phys.par.all <- names(indiv.model.bio1)[names(indiv.model.bio1)!=
'liver.density']
parlist <- list('Clint',
'Funbound.plasma',
phys.par.all)
parlist <- c(parlist, as.list(phys.par))
#Note: Now sensitivity is being calculated to each of the physiological parameter independently, as well as grouped.
#If Clint is fixed at zero,
#don't test sensitivity to it
if (all(indiv.A[, Clint]==0)){
parlist <- parlist[parlist!='Clint']
}
#Now loop over parameters (and groups thereof)
eff.list <- lapply(parlist,
doforeachparam,
indiv.A=indiv.A,
indiv.B=indiv.B,
this.chemcas=this.chemcas,
model=model,
species=species,
css.method=css.method,
f.A=f.A,
f.B=f.B,
g0=g0,
g0prime=g0prime,
p0=p0)
parnames <- parlist
#Name the sensitivity indexes by their parameters
#For the group of physiological parameters, just name it "phys.par"
parnames[sapply(parnames, function(x) length(x)>1)] <- 'phys.par'
names(eff.list) <- parnames
#If Clint was zero, then sensitivity to it wasn't calculated.
#Set all the sensitivity quantities to NA to indicate this.
if (all(indiv.A[, Clint]==0)){
eff.list <- c(eff.list,
list(Clint=list(Ti=NA,
Si=NA,
p0=NA,
pj=NA,
csj=NA,
csminusj=NA,
cdj=NA,
cdminusj=NA,
caj=NA,
caminusj=NA)))
}
#Construct a data.table to return
eff.dt <- as.data.table(do.call(rbind.data.frame,
eff.list),
keep.rownames=TRUE)
eff.dt[, chemcas:=this.chemcas]
return(eff.dt)
}And finally, loop over the chemicals to actually do the sensitivity analysis.
model <- "3compartmentss"
species <- "Human"
sigma.factor <- 0.3
css.method <- "analytic"
chemlist <- httk::get_cheminfo(info="CAS",
exclude.fub.zero=FALSE)
cluster <- parallel::makeCluster(40,
outfile='globalsens_indep_parallel_out.txt')
evalout <- parallel::clusterEvalQ(cl=cluster,
{library(data.table)
library(httk)})
parallel::clusterExport(cl = cluster,
varlist = c('doforeachparam'))
#Set seeds on all workers for reproducibility
parallel::clusterSetRNGStream(cluster,
TeachingDemos::char2seed("Caroline Ring"))
for (poormetab in c(TRUE, FALSE)){
for (fup.censor in c(TRUE, FALSE)) {
#Now loop over the chemicals
eff.allchems <- rbindlist(
parLapplyLB(cl=cluster,
chemlist,
doforeachchem_indep,
model=model,
species=species,
sigma.factor=sigma.factor,
css.method=css.method,
indep.model.bio1=indep.bio1,
indep.model.bio2=indep.bio2,
poormetab=poormetab,
fup.censor=fup.censor,
nsamp=1000))
saveRDS(eff.allchems,
paste0('data/',
'sens_glenisaacs_nhanes_',
'allchems_',
'indepMC_',
model,
'_',
css.method,
'_fup_censor_',
fup.censor,
'_poormetab_',
poormetab,
"_FuptoFub",
'.Rdata'))
}
}
parallel::stopCluster(cluster)