MCMC sampling in tree space
Description
To sample the posterior trees. Major function of Canopy.
Usage
1 2 3 
Arguments
R 
alternative allele read depth matrix 
X 
total read depth matrix 
WM 
observed major copy number matrix 
Wm 
observed minor copy number matrix 
epsilonM 
observed standard deviation of major copy number (scalar input is transformed into matrix) 
epsilonm 
observed standard deviation of minor copy number (scalar input is transformed into matrix) 
C 
CNA and CNAregion overlapping matrix, only needed if overlapping CNAs are used as input 
Y 
SNA and CNAregion overlapping matrix 
K 
number of subclones (vector) 
numchain 
number of MCMC chains with random initiations 
simrun 
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) 
diagnostics 
default to be FALSE, TRUE for diagnostic output 
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14  data(MDA231)
R = MDA231$R
X = MDA231$X
WM = MDA231$WM
Wm = MDA231$Wm
C = MDA231$C
Y = MDA231$Y
K = 3:6
numchain = 20
projectname = 'MDA231'
# sampchain = canopy.sample(R = R, X = X, WM = WM, Wm = Wm, epsilonM = epsilonM,
# epsilonm = epsilonm, C = C, Y = Y, K = K, numchain = numchain,
# simrun = 50000, writeskip = 200, projectname = projectname,
# cell.line = TRUE, plot.likelihood = TRUE)
