# R/my.cubappr.r In cubfits: Codon Usage Bias Fits

```### Special note for cubappr().
### n.G and phi.DrawScale are actually the same role of
### n.G.pred and phi.pred.DrawScale in cubpred().
###

### Function to run MCMC inference for following model:
### Given phi, n, b; across n.G genes:
###   phi ~ rlnorm(n.G, mu.Phi, sigma.Phi)
###   y   ~ for(aa) rmultinom(n.G, invmlogit( phi * b[[aa]] ), n[[aa]] )
###
### Expects phi.pred.Init as vector of length n.G.
###
### Subsequent Gibbs sampler:
### (1) Sample of b | phi, y, using VGAM fit for Gaussian proposal
### (2) Sample mu.Phi, sigma.Phi | x
### (3) Sample of phi | b, nu.Phi, sigma.Phi, m.Phi, ww, y,
###     using logNormal proposal

### No observation (phi) is required.
my.cubappr <- function(reu13.df.obs, phi.pred.Init, y, n,
nIter = 1000,
b.Init = NULL, init.b.Scale = .CF.CONF\$init.b.Scale,
b.DrawScale = .CF.CONF\$b.DrawScale,
b.RInit = NULL,
p.Init = NULL, p.nclass = .CF.CONF\$p.nclass,
p.DrawScale = .CF.CONF\$p.DrawScale,
phi.pred.DrawScale = .CF.CONF\$phi.pred.DrawScale,
model = .CF.CT\$model[1],
model.Phi = .CF.CT\$model.Phi[1],
verbose = .CF.DP\$verbose,
iterThin = .CF.DP\$iterThin,
report = .CF.DP\$report){

### Setup functions ###
### Setup function pointers by type or model.
my.function <- my.init.function(model = model[1], model.Phi = model.Phi[1],
my.ncoef <- my.function\$my.ncoef

### Check Data ###
### check phi.pred.Init is well-behaved.
my.check.data(phi.Init = phi.pred.Init)

### Check if sort by ORF and length.
my.check.rearrange(reu13.df.obs, y, n, phi.Obs = phi.pred.Init)

### Initial Storages ###
### Setup data structures for results.
n.G <- nrow(y[[1]])                       # # of genes
n.aa <- length(y)                         # # of amino acids
nsyns <- sapply(y, function(ybit){ dim(ybit)[2] })
# # of synomous codons
nBparams <- my.ncoef * sum(nsyns - 1)     # total # of regression parameters
nSave <- nIter / iterThin + 1             # # of space for iterations
nPrior <- 2                               # # of prior parameters
if(model.Phi == "logmixture"){
nPrior <- 3 * p.nclass
}
if(.CF.CONF\$estimate.bias.Phi){
nPrior <- nPrior + 1                    # one more for bias.Phi
}

### Storages for saving posterior samples.
b.Mat <- my.generate.list(NA, nBparams, nSave)     # log(mu) and Delta.t
p.Mat <- my.generate.list(NA, nPrior, nSave)       # prior parameters
phi.pred.Mat <- my.generate.list(NA, n.G, nSave)   # E[Phi]
logL.Mat <- my.generate.list(NA, 1, nSave)         # logL

### Initial Parameters ###
# set switch coefficient to move between delta t and delta eta
### Initial values for p first since scaling may change phi.Obs.
p.Init <- my.pInit(p.Init, phi.pred.Init, model.Phi[1],
p.nclass = p.nclass, cub.method = "appr")

### Initial values for b and b.R.
b.InitList <- .cubfitsEnv\$my.fitMultinomAll(reu13.df.obs, phi.pred.Init, y, n)
if(is.null(b.RInit)){
b.RInitList <- lapply(b.InitList, function(B){ B\$R })
} else{
b.RInitList <- b.RInit
}

if(is.null(b.Init)){
b.Init <- lapply(b.InitList,
function(B){
B\$coefficients +
init.b.Scale * backsolve(B\$R, rnorm(nrow(B\$R)))
})
} else{
if(!is.null(b.Init[[1]]\$R)){
b.RInitList <- lapply(b.Init, function(B){ B\$R })
}
b.Init <- lapply(b.Init, function(B){ B\$coefficients })
}
b.InitVec <- unlist(b.Init)
names(b.RInitList) <- names(reu13.df.obs)

### Set current step ###
### Set current step for b.
b.Mat[[1]] <- b.InitVec
b.Curr <- b.Init

### Set current step for p.
p.Mat[[1]] <- p.Init
p.Curr <- p.Init

### Set current step for phi.
phi.pred.Mat[[1]] <- phi.pred.Init
phi.Curr <- phi.pred.Init

### For hyper-prior parameters.
hp.param <- list(log.phi.Obs.mean = mean(log(phi.pred.Init)),
# hp.sigma.Phi = 1 / sqrt(var(log(phi.pred.Init))),
hp.Init = p.Init)

### Set logL.
logL.Curr <- -Inf
if(.CF.CONF\$compute.logL){
tmpPred <- .cubfitsEnv\$my.logLAllPred(phi.Curr, y, n, b.Init,
reu13.df = reu13.df.obs)
logL.Curr <- sum(tmpPred)
logL.Mat[[1]] <- logL.Curr
}

### MCMC here ###
### Get length for acceptance and adaptive storage.
n.p <- 1
if(.CF.CONF\$estimate.bias.Phi && length(p.DrawScale) < n.p){
n.p <- n.p + 1
}

### Set acceptance rate storage.
my.set.acceptance(nIter + 1, n.aa, n.p = n.p, n.G.pred = n.G)

if(.CF.CONF\$estimate.bias.Phi){
### Bias of phi is coupled with p parameters.
p.DrawScale <- c(p.DrawScale, .CF.CONF\$bias.Phi.DrawScale)
}
n.aa = n.aa, b.DrawScale = b.DrawScale,
n.p = n.p, p.DrawScale = p.DrawScale,
n.G.pred = n.G, phi.pred.DrawScale = phi.pred.DrawScale,

### Run MCMC iterations.
my.verbose(verbose, 0, report)
.cubfitsEnv\$my.dump(0, list = c("b.Mat", "p.Mat", "phi.pred.Mat", "logL.Mat"))

### MCMC start.
for(iter in 1:nIter){
### Step 1: Update b using M-H step.
bUpdate <- .cubfitsEnv\$my.drawBConditionalAll(
b.Curr, phi.Curr, y, n, reu13.df.obs,
b.RInitList = b.RInitList)
b.Curr <- lapply(bUpdate, function(U){ U\$bNew })

### Step 2: Draw other parameters.
p.Curr <- .cubfitsEnv\$my.pPropTypeNoObs(
n.G, phi.Curr, p.Curr, hp.param)

### Step 3: Predict phi using M-H step.
###         This is different to cubfits() and cubpred().
phi.Curr <- my.drawPhiConditionalAllPred(
phi.Curr, y, n, b.Curr, p.Curr,
reu13.df = reu13.df.obs)

### Step logL:
if(.CF.CONF\$compute.logL && (iter %% iterThin) == 0){
tmpPred <- .cubfitsEnv\$my.logLAllPred(phi.Curr, y, n, b.Curr,
reu13.df = reu13.df.obs)
logL.Curr <- sum(tmpPred)
}

### Step A: Update scaling factor.
if(iter %/% .CF.AC\$renew.iter + 1 == .cubfitsEnv\$curr.renew){
} else{
.cubfitsEnv\$my.update.DrawScale(
c("b", "p", "phi.pred"),
c(.CF.AC\$b.DrawScale, .CF.AC\$p.DrawScale, .CF.AC\$phi.pred.DrawScale))
}

### Dump parameters out.
if((iter %% iterThin) == 0){
thinnedIter <- iter / iterThin + 1
b.Mat[[thinnedIter]] <- do.call("c", b.Curr)
p.Mat[[thinnedIter]] <- p.Curr
phi.pred.Mat[[thinnedIter]] <- phi.Curr
if(.CF.CONF\$compute.logL){
logL.Mat[[thinnedIter]] <- logL.Curr
}
}
my.verbose(verbose, iter, report)
.cubfitsEnv\$my.dump(iter, list = c("b.Mat", "p.Mat", "phi.pred.Mat",
"logL.Mat"))
} ### MCMC end.

### Check acceptance of last renew iteration.
my.check.acceptance(c("b", "p", "phi.pred"))

### Return ###
aa.names <- names(y)
in.names <- names(b.Mat[[1]])
names(b.Mat[[1]]) <- mapBMatNames(in.names, aa.names, model = model)

ret <- list(b.Mat = b.Mat, p.Mat = p.Mat, phi.pred.Mat = phi.pred.Mat,
logL.Mat = logL.Mat,
b.Init = b.Init, b.RInit = b.RInitList,
p.Init = p.Init, phi.pred.Init = phi.pred.Init)
ret
} # End of my.cubappr().
```

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cubfits documentation built on May 2, 2019, 4:08 a.m.