# R/immunogen_mh_tools_2.r In liesb/BIITE: Bayesian Immunogenicity Inference Tool for ELISpot

#### Documented in get_acc_rate_chainget_proposalget_q_h1_given_h2mh_chainone_time_step

```#######################
# METROPOLIS-HASTINGS #
#######################

## sample through the population
# 2. get_q_h1_given_h2 :: get Q(h1|h2)
# 3. one_time_step :: exactly what it says :: with evidence_1
# 4. mh_chain :: initialisation + running of mh algorithm :: with evidence_1
# 5. get_acc_rate_chain :: Get the acceptance rate of a chain; used to tune the radius

########
# CODE #
########

#' Choosing a proposal state
#'
#' Given the current state of the MH chain, pick a proposal state. Also returns Q(proposal state|current state)
#' @param h current State of the MH chain
#' @param unif.prop If TRUE, proposal is chosen from [0,1]^m. If false, only looks in the overlap of [0,1]^m with a cube around h with side length 2*radius
#' @param radius If unif.prop is FALSE, we look for a proposal state in the overlap of [0.1]^m and [h-radius, h+radius].

# output: vector
# first el: the proposal hyp
if ( unif.prop ){
# just take a random point in unit hypercube
return(c(paste(runif(length(unlist(strsplit(h, ",")))), collapse=","),0))
}
else {  # non-independent sampling
h <- as.double(unlist(strsplit(h, ",")))
lower <- pmax(0, h - radius)
upper <- pmin(1, h + radius)
g <- lower + unlist(lapply(rep(1, length(h)), runif))*(upper-lower)
return(c(paste(g, collapse=","), -sum(log(upper-lower))))
}
}

# 2. get_q_h1_given_h2 :: get Q(h1|h2)
#' Evaluate Q function

get_q_h1_given_h2 <- function(h1, h2, unif.prop=T, radius){
if ( unif.prop ){
return(0)
}
else{
h2 <- as.double(unlist(strsplit(h2, ",")))
}
}

# 3. one_time_step :: exactly what it says :: with evidence_1
#' One update of MH chain
#'
#' Performs one time step in the MH chain:
#' @param h Hypothesis which is the current state of the chain
#' @param loglik_h Log likelihood of h
#' @param eli.dat ELISpot dataset
#' @param pep Peptide currently being processed
#' @param mol.names Names of the HLA molecules in the ELISpot dataset
#' @param unif.prop If TRUE, choose proposal state from [0,1]^m
#' @param radius If unif.prop is FALSE, we look for a proposal state in a hypercube around h restricted by radius
#' @param p.df Dataframe which describes the parameters of the prior distributions for each HLA

one_time_step <- function(h, loglik_h, eli.dat, pep, mol.names, unif.prop,
## get a proposal for next hypothesis
prop_h <- new_vec[1]
q_new_given_old <- as.double(new_vec[2])
# this is in log
loglik_prop_h <- get_evidence_1(eli.dat, prop_h, mol.names, pep) +
get_prior_hyp(molecs, prop_h, shape.df=p.df)

q_old_given_new <- get_q_h1_given_h2(h, prop_h, unif.prop=unif.prop, radius)
# this is in log
acc_rate <- min(1, exp(- loglik_h + loglik_prop_h)*exp(q_old_given_new - q_new_given_old) )
if ( runif(1) < acc_rate ) {
return(c(prop_h, loglik_prop_h))
}
else {
return(c(h, loglik_h))
}
}

# 4. mh_chain :: initialisation + running of mh algorithm :: with evidence_1
#' Metropolis-Hasting for a single peptide
#'
#' Implementation of the Metropolis-Hastings algorithm to find posterior distributions of pHLA immunogenicity for a single peptide
#' @param eli.dat ELISpot dataset
#' @param mol.names Names of the HLA molecules in the ELISpot dataset
#' @param init_h Initial state of the MH chain, can be produced randomly by generate_random_hypothesis
#' @param max_steps The required length of the chain. The default value, 5000, is too low but this was done for debugging reasons.
#' @param pep Peptide currently being processed
#' @param unif.prop If TRUE, choose proposal states from [0,1]^m
#' @param radius If unif.prop is FALSE, we look for a proposal state in a hypercube around h restricted by radius. Should be trained such that the acceptance rate of the chain is around 50%, see example.r.
#' @param p.df Dataframe which describes the parameters of the prior distributions for each HLA

mh_chain <- function(eli.dat, mol.names, init_h, max_steps=5000, pep, unif.prop=T,
mh_out <- data.frame(matrix(nrow=max_steps, ncol=2))
colnames(mh_out) <- c("Hyp", "LogLik")
mh_out [1,] <- c(init_h, get_evidence_1(eli.dat, init_h, mol.names, pep) +
get_prior_hyp(molecs, init_h, shape.df=p.df))
t <- 2
while ( ! t > max_steps){
out_t <- one_time_step(h=mh_out[t-1,1],
loglik_h=as.double(as.character(mh_out[t-1,2])),
eli.dat, pep, mol.names, unif.prop, radius, p.df)
mh_out[t,] <- c(paste(out_t[1], collapse=","), out_t[2])
t <- t + 1
}
mh_out\$LogLik <- as.double(as.character(mh_out\$LogLik))
mh_out
}

# 5. Get the acceptance rate of a chain; used to tune the radius
#' Acceptance Rate of an MH chain
#'
#' Computes the acceptance rate of a chain by comparing each state to the next one.
#' @param chain Which chain do you want to compute the acceptance rate of?
get_acc_rate_chain <- function(chain){
100*sum(!chain[1:(dim(chain)[1]-1),]\$Hyp == chain[2:dim(chain)[1],]\$Hyp)/dim(chain)[1]
}
```
liesb/BIITE documentation built on May 21, 2017, 1:35 p.m.