meta.ME: immunoClust EM(t)-iteration on Cell-clusters

In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

Description

Performs an EM(t)-iteration on cell-clusters given an initial meta-cluster membership for the cell-clusters and returns the fitted meta-clusters information in an object of class immunoClust.

Usage

meta.ME(P, N, K, W, M, S, label, B=100, tol=1e-5, method=20, bias=0.25, 
    alpha=0.5, min.class=0)

Arguments

P	The number of observed parameters for the cell event clusters.
N	The number of cell-clustering experiments.
K	The N-dimensional vector with the numbers of cell event clusters in each experiment. The total number of clusters is totK = sum_{i=1}^K K_i.
W	The totK-dimensional vector with weights, i.e. number of events, of all clusters.
M	The totK x P-dimensional matrix of all cluster means.
S	The totK x P x P-dimensional matrix of all cluster covariance matrices.
label	The totK-dimension integer vector with the initial cell-cluster to meta-cluster membership.
B	The totK x P x P-dimensional matrix of all cluster covariance matrices.
tol	The tolerance used to assess the convergence of the EM(t)-algorithms.
method	0 = KL-minimization not weighted 1 = BC-maximization not weighted 10 = BC-maximization weighted 2 = EMt-classification not weighted 20 = EMt-classification weighted
bias	The ICL-bias used in the EMt-iteration of the meta-clustering.
alpha	A value between 0 and 1 used to balance the bhattacharrya probabilities calculated with either the full covariance matrices or using only the diagonal elements of it.
min.class	The minimum number of clusters for the final model.

Details

This function is used internally by the meta-clustering procedures meta.process and meta.Clustering in immunoClust.

Value

The fitted meta-clusters information in an object of class immunoClust.

Author(s)

Till Sörensen [e-mail]

References

Sörensen, T., Baumgart, S., Durek, P., Grützkau, A. and Häupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

See Also

meta.process, meta.Clustering

Examples

data(dat.exp)
d <- meta.exprs(dat.exp)
r <- meta.ME(d$P, d$N, d$K, d$clsEvents, d$M, d$S, label=rep(1,sum(d$K)))

immunoClust documentation built on Nov. 8, 2020, 5:19 p.m.