canopy.sample.cluster.nocna: MCMC sampling in tree space with pre-clustering of SNAs

In Canopy: Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal and Spatial Clonal Evolutionary History by Next-Generation Sequencing

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)

Canopy documentation built on May 1, 2019, 7:59 p.m.