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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_kappa.R ############################################################################
#
# Input variables:
# tumourdata: 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 kappa parameter updated
#
# Description: This function optimizes kappa, the strength parameter in the prior over the reference
# cancer profile. Note that we don't directly optimize kappa because it has constraints (must be
# greater than the minimum determined in ISOpure.step1.CPE.)
#
# REVISIT:
# 1. This function is not vectorized, as FOR STEP 1 in Matlab, as kappa is a scalar.
# May have to alter this for backwards compatibility with ISOLATE?
# 2. Re-test NUM_GRID_SEARCH_ITERATIONS > 0 part, as had some issues with it in initial testing.
ISOpureS1.model_optimize.opt_kappa <- function(tumordata, model, NUM_ITERATIONS_RMINIMIZE, iter, NUM_GRID_SEARCH_ITERATIONS) {
kappa <- as.numeric(model$kappa);
# note, kappa in our case is a scalar, so I didn't reproduce the matrix operations (transpose, etc) from
# the Matlab code in this R code
init_xx <- log(kappa - model$MIN_KAPPA); # model$kappa_weights
# perform the optimization
returnval <- ISOpure.model_optimize.cg_code.rminimize(init_xx, ISOpureS1.model_optimize.kappa.kappa_loglikelihood, ISOpureS1.model_optimize.kappa.kappa_deriv_loglikelihood, NUM_ITERATIONS_RMINIMIZE, tumordata=tumordata, model=model);
xx <- as.numeric(returnval[[1]]);
# this makes sure that kappa is at least MIN_KAPPA
model$kappa <- exp(xx) + model$MIN_KAPPA;
# do some random restarts for kappa optimization
if (iter <= NUM_GRID_SEARCH_ITERATIONS) {
loglikelihood <- ISOpureS1.model_optimize.kappa.kappa_compute_loglikelihood(model$kappa, tumordata, model);
MIN_POW_KAPPA <- ceiling(log10(model$MIN_KAPPA));
for (pow in MIN_POW_KAPPA:15) {
# again, this part is vectorized in the Matlab
scales <- 10^pow * rep(1, length(model$kappa));
init_xx <- log(scales - model$MIN_KAPPA);
returnval <- ISOpure.model_optimize.cg_code.rminimize(init_xx, ISOpureS1.model_optimize.kappa.kappa_loglikelihood, ISOpureS1.model_optimize.kappa.kappa_deriv_loglikelihood, NUM_ITERATIONS_RMINIMIZE, tumordata=tumordata, model=model);
newkappa <- exp(returnval[[1]]) + model$MIN_KAPPA;
newll <- ISOpureS1.model_optimize.kappa.kappa_compute_loglikelihood(newkappa, tumordata, model);
if (newll > loglikelihood) {
model$kappa <- newkappa;
loglikelihood <- newll;
}
}
}
return(model);
}
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