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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.theta.theta_loglikelihood.R #######################################################
#
# Input variables:
# ww: the theta weights corresponding to patient dd, a 1xK matrix
# tumordata: a GxD matrix representing gene expression profiles of tumor samples
# dd: the patient number
# model: list containing all the parameters to be optimized
#
# Output variables:
# loglikelihood: loglikelihood function relevant to optimizing theta
ISOpureS1.model_optimize.theta.theta_loglikelihood <- function(ww, tumordata, dd, model) {
# K = number of normal profiles + 1
K <- length(ww);
# G = number of genes
G <- ncol(model$log_all_rates);
# theta_weights for patient dd
ww <- matrix(ww, nrow=1, ncol=K);
expww <- exp(ww);
# theta for patient dd (constrained so that sum of entries is 1)
theta <- expww / sum(expww);
# loglikelihood of theta
# log p(theta_d | vv) = log Dirichlet( theta_d | vv ), ignoring the vv term in front
loglikelihood <- (model$vv-1) %*% t(ISOpure.util.matlab_log(theta));
# loglikelihood of observed tumour profile t_dd
# log p(t_d | B, theta_d c_d) = log Multinomial(t_n| alpha*c_d + theta_d*B)
log_P_t_given_theta <- ISOpure.util.logsum(t(ISOpure.util.repmat(ISOpure.util.matlab_log(theta), G, 1)) + model$log_all_rates,1);
loglikelihood <- loglikelihood + (log_P_t_given_theta %*% tumordata[,dd]);
# take the negative of the loglikelihood since using a minimizer
loglikelihood <- -loglikelihood;
return(as.numeric(loglikelihood));
}
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