canopy.sample {Canopy} | R Documentation |
MCMC sampling in tree space
Description
To sample the posterior trees. Major function of Canopy.
Usage
canopy.sample(R, X, WM, Wm, epsilonM, epsilonm, C=NULL,
Y, 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 |
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 CNA-region overlapping matrix, only needed if overlapping CNAs are used as input |
Y |
SNA and CNA-region overlapping matrix |
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(MDA231)
R = MDA231$R; X = MDA231$X
WM = MDA231$WM; Wm = MDA231$Wm
epsilonM = MDA231$epsilonM; epsilonm = MDA231$epsilonm
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,
# max.simrun = 50000, min.simrun = 10000, writeskip = 200,
# projectname = projectname, cell.line = TRUE, plot.likelihood = TRUE)