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# The ISOpureR package is copyright (c) 2014 Ontario Institute for Cancer Research (OICR)
# This package and its accompanying libraries is free software; you can redistribute it and/or modify it under the terms of the GPL
# (either version 1, or at your option, any later version) or the Artistic License 2.0. Refer to LICENSE for the full license text.
# OICR makes no representations whatsoever as to the SOFTWARE contained herein. It is experimental in nature and is provided WITHOUT
# WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR ANY OTHER WARRANTY, EXPRESS OR IMPLIED. OICR MAKES NO REPRESENTATION
# OR WARRANTY THAT THE USE OF THIS SOFTWARE WILL NOT INFRINGE ANY PATENT OR OTHER PROPRIETARY RIGHT.
# By downloading this SOFTWARE, your Institution hereby indemnifies OICR against any loss, claim, damage or liability, of whatsoever kind or
# nature, which may arise from your Institution's respective use, handling or storage of the SOFTWARE.
# If publications result from research using this SOFTWARE, we ask that the Ontario Institute for Cancer Research be acknowledged and/or
# credit be given to OICR scientists, as scientifically appropriate.
### FUNCTION: ISOpureS1.model_optimize.opt_vv.R ############################################################################
#
# Input variables:
# tumordata: a GxD matrix representing gene expression profiles of tumour samples
# model: list containing all the parameters to be optimized
# NUM_ITERATIONS_RMINIMIZE: minimum number of iteration that the minimization algorithm runs
# iter: the iteration number
# NUM_GRID_SEARCH_ITERATIONS: number of times to try restarting with different initial values
#
# Output variables:
# model: the model with the vv parameter updated
#
# Description: This function optimizes vv, the strength parameter in the prior over the reference
# cancer profile. Note that we don't directly optimize vv because it has constraints (must be >=1
# to guarantee real-valued likelihoods).
ISOpureS1.model_optimize.opt_vv <- function(tumordata, model, NUM_ITERATIONS_RMINIMIZE, iter, NUM_GRID_SEARCH_ITERATIONS) {
# K = number of normal profiles+1
K <- length(model$vv);
# reshape vv into a Kx1 matrix
vv <- matrix(model$vv,nrow=K, ncol=1);
# note that we don't directly optimize vvs because it has constraints
# (must be >=1 to guarantee real-valued likelihoods).
init_ww <- as.matrix(ISOpure.util.matlab_log(vv-1));
sum_log_theta <- matrix(colSums(ISOpure.util.matlab_log(model$theta)), nrow=1, ncol=K);
# perform the optimization
r_min_ret <- ISOpure.model_optimize.cg_code.rminimize(init_ww, ISOpure.model_optimize.vv.vv_loglikelihood, ISOpure.model_optimize.vv.vv_deriv_loglikelihood, NUM_ITERATIONS_RMINIMIZE, sum_log_theta, ncol(tumordata));
ww <- matrix(r_min_ret[[1]]);
# convert back into vv
vv <- exp(ww)+1;
model$vv <- matrix(vv,nrow=1, ncol=K);
# do some re-starts because they're not computationally expensive
if (iter <= NUM_GRID_SEARCH_ITERATIONS) {
loglikelihood <- ISOpureS1.model_optimize.vv.vv_compute_loglikelihood(model$vv, sum_log_theta, ncol(tumordata));
for (cancer_vv in seq(10,100,10)) {
newvv <- matrix(1,ncol(model$vv),nrow(model$vv));
newvv[nrow(newvv),1] <- cancer_vv;
r_min_ret <- ISOpure.model_optimize.cg_code.rminimize(log(newvv-1), ISOpure.model_optimize.vv.vv_loglikelihood, ISOpure.model_optimize.vv.vv_deriv_loglikelihood, NUM_ITERATIONS_RMINIMIZE, sum_log_theta, ncol(tumordata));
xx <- matrix(r_min_ret[[1]]);
newvv <- t(exp(xx))+1;
# note: here in both Matlab and R: if xx has entries that are -Inf, then exp(xx)+1 will be 1
# vv's will be 1, so should be okay?
newll <- ISOpureS1.model_optimize.vv.vv_compute_loglikelihood(newvv, sum_log_theta, ncol(tumordata));
if (newll>loglikelihood) {
model$vv <- newvv;
loglikelihood <- newll;
}
}
}
return(model);
}
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