canopy.sample.cluster.nocna {Canopy}

MCMC sampling in tree space with pre-clustering of SNAs

Description

To sample the posterior trees with pre-clustering step of SNAs. Major function of Canopy.

Usage

    canopy.sample.cluster.nocna(R, X, sna_cluster, K, numchain, 
                                max.simrun, min.simrun, writeskip, projectname,
                                cell.line=NULL, plot.likelihood=NULL)

Arguments

R

alternative allele read depth matrix

X

total read depth matrix

sna_cluster

cluster assignment for each mutation from the EM Binomial clustering algorithm

K

number of subclones (vector)

numchain

number of MCMC chains with random initiations

max.simrun

maximum number of simutation iterations for each chain

min.simrun

minimum number of simutation iterations for each chain

writeskip

interval to store sampled trees

projectname

name of project

cell.line

default to be FALSE, TRUE if input sample is cell line (no normal cell contamination)

plot.likelihood

default to be TRUE, posterior likelihood plot generated for check of convergence and selection of burnin and thinning in canopy.post

Value

List of sampleed trees in subtree space with different number of subclones; plot of posterior likelihoods in each subtree space generated (pdf format).

Author(s)

Yuchao Jiang [e-mail]

Examples

    data(toy3)
    R = toy3$R; X = toy3$X
    sna_cluster = toy3$sna_cluster
    K = 3:5
    numchain = 10
    projectname = 'toy3'
    # sampchain = canopy.sample.cluster.nocna(R = R, X = X, 
    #             sna_cluster=sna_cluster, K = K, numchain = numchain, 
    #             max.simrun = 40000, min.simrun = 10000, writeskip = 200, 
    #             projectname = projectname,
    #             cell.line = TRUE, plot.likelihood = TRUE)