meta.ME {immunoClust} | R Documentation

## immunoClust EM(t)-iteration on Cell-clusters

### 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 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`

### 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)))

[Package immunoClust version 1.19.4 Index]