ISOpureS1.model_optimize.vv.vv_compute_loglikelihood

Kx1 matrix representing the weights of the normal profiles B_i used to make the 
		weighted combination that forms the mean parameter vector for the Dirichlet distribution
		over m

the column sums of log(theta), a 1xK matrix

sum_log_theta

Computes the part of the loglikelihood function relevant to optimizing vv for step 1.

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.

Paul C. Boutros

ISOpureR

Deconvolution of Tumour Profiles

Gerald Quon 

Catalina V Anghel 

Syed Haider 

Francis Nguyen 

Amit G Deshwar 

Quaid D Morris 

Paul C Boutros 

ISOpureS1.model_optimize.vv.vv_compute_loglikelihood function

Compute loglikelihood relevant to vv for step 1 — ISOpureS1.model_optimize.vv.vv_compute_loglikelihood

Compute loglikelihood relevant to vv for step 1

ISOpureS1.model_optimize.vv.vv_compute_loglikelihood: Compute loglikelihood relevant to vv for step 1

Description

Usage

Arguments

Value