MCMC sampling in tree space

Share:

Description

To sample the posterior trees. Major function of Canopy.

Usage

1
2
3
canopy.sample(R, X, WM, Wm, epsilonM, epsilonm, C=NULL,
              Y, K, numchain, simrun, writeskip, projectname,
              cell.line=NULL, diagnostics=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

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 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 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)