canopy.sample.cluster.nocna {Canopy}  R Documentation 
MCMC sampling in tree space with preclustering of SNAs
Description
To sample the posterior trees with preclustering 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

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 yuchaoj@wharton.upenn.edu
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)