ISOpureS2.model_optimize.theta.theta_deriv_loglikelihood

the theta weights corresponding to patient dd, a 1xK matrix

a GxD matrix representing gene expression profiles of tumor samples

tumordata

list containing all the parameters to be optimized

model

Computes the derivative of the loglikelihood function relevant to optimizing theta, not 
	with respect to theta but with respect to unconstrained variables

math

Deconvolution of mixed tumour profiles into normal and cancer for each patient, using
the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires
mixed tumour profiles and a set of unmatched "basis" normal profiles.

ISOpureS2.model_optimize.theta.theta_deriv_loglikelihood: Compute the derivative of loglikelihood relevant to theta for step 2

Description

Usage

Arguments

Value