canopy.cluster: EM algorithm for multivariate clustering of SNAs

In Canopy: Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal and Spatial Clonal Evolutionary History by Next-Generation Sequencing

Description

EM algorithm for multivariate clustering of SNAs.

Usage

    canopy.cluster(R, X, num_cluster, num_run, Mu.init = NULL, Tau_Kplus1 = NULL)

Arguments

R	alternative allele read depth matrix
X	total read depth matrix
num_cluster	number of mutation clusters (BIC as model selection metric)
num_run	number of EM runs for estimation for each specific number of clusters (to avoid EM being stuck in local optima)
Mu.init	(optional) initial value of the VAF centroid for each mutation cluster in each sample
Tau_Kplus1	(optional) pre-specified proportion of noise component in clustering, uniformly distributed between 0 and 1

Value

Matrix of posterior probability of cluster assignment for each mutation.

Author(s)

Yuchao Jiang [e-mail]

Examples

    data(AML43)
    R = AML43$R
    X = AML43$X
    Mu = AML43$Mu
    Tau = AML43$Tau
    pG = canopy.cluster.Estep(Tau, Mu, R, X)

Example output

Loading required package: ape
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-1 (2017-07-02) 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
Loading required package: pheatmap
Loading required package: scatterplot3d

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