meta.SubClustering {immunoClust} | R Documentation

## immunoClust Model Refinement Step in iterative Meta-clustering

### Description

These function tests each meta-cluster of a model for refining it into more sub-clusters and returns the refined cluster memberships in an integer array.s

### Usage

    meta.SubClustering(P, N, W, M, S, label, tol=1e-5, bias=0.25, alpha=1.0,
        EM.method=20)

    meta.TestSubCluster(P, N, W, M, S, J=8, B=500, tol=1e-5, bias=0.5, alpha=1.0,
        EM.method=2, HC.samples=2000)

### Arguments

`P` | The number of parameters.
`N` | The number of clusters.
`W` | The N-dimensional vector with cluster weights, i.e. numbers of events in a cluster.
`M` | The N x P-dimensional vector with cluster means.
`S` | The N x P x P-dimensional vector with the cluster covariance matrices.
`label` | The N-dimension integer vector with the cell-cluster to meta-cluster membership.
`tol` | The tolerance used to assess the convergence of the EM(t)-algorithms in Sub-Clustering.
`bias` | he ICL-bias used in the EMt-algorithm.
`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.
`J` | The number of sub-models to be builded and tested for a particular cluster.
`B` | The maximum number of EM(t)-iterations in Sub-Clustering.
`EM.method` | 0 = KL-minimization not weighted 1 = BC-maximization not weighted 10 = BC-maximization weighted 2 = EMt-classification not weighted 20 = EMt-classification weighted
`HC.samples` | The number of samples used for initial hierarchical clustering.

### Details

These function are used internally by the meta-clustering procedures `meta.process` and `meta.Clustering` in immunoClust and are not intended to be used directly.

### Value

An integer array of length N containing the cell-clusters meta-cluster memberships of the refined model.

### Author(s)

Till Sörensen till-antoni.soerensen@charite.de

### 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`, `meta.hclust`

### Examples

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

    r1 <- meta.ME(d$P, d$N, d$K, d$clsEvents, d$M, d$S, label)

[Package immunoClust version 1.24.0 Index]