canopy.cluster.Mstep: M-step of 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

M-step of EM algorithm for multivariate clustering of SNAs. Used in canopy.cluster.

Usage

    canopy.cluster.Mstep(pG, R, X, Tau_Kplus1)

Arguments

pG	matrix of posterior probability of cluster assignment for each mutation
R	alternative allele read depth matrix
X	total read depth matrix
Tau_Kplus1	proportion mutation cluster that is uniformly distributed to capture noise

Value

List of bic, converged Mu, Tau, and SNA cluster assignment.

Author(s)

Yuchao Jiang [e-mail]

Examples

    data(AML43)
    R = AML43$R; X = AML43$X
    num_cluster = 4 # Range of number of clusters to run
    num_run = 6 # How many EM runs per clustering step
    Tau_Kplus1=0.05 # Proportion of noise component
    Mu.init=cbind(c(0.01,0.15,0.25,0.45),c(0.2,0.2,0.01,0.2)) # initial value
                                                              # of centroid
    canopy.cluster=canopy.cluster(R = R, X = X, num_cluster = num_cluster,
                                  num_run = num_run, Mu.init = Mu.init,
                                  Tau_Kplus1=Tau_Kplus1)

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