canopy.sample.cluster: 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(R, X, sna_cluster, 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
sna_cluster	cluster assignment for each mutation from the EM Binomial clustering algorithm
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 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(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.cluster(R = R, X = X, sna_cluster=c(1,2,3,4),
    #             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)

Example output

Loading required package: ape
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.2-0 (2018-06-19) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps
See www.image.ucar.edu/~nychka/Fields for
 a vignette and other supplements. 
Loading required package: pheatmap
Loading required package: scatterplot3d

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