meta.hclust: Hierarchical Meta-clustering of Cell-clusters in the...
In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Performs agglomerative clustering on cell-clusters. It is used in the interative meta-clustering approach of immunoClust to obtain an initial meta-cluster membership for the EM(t)-iteration.

1	meta.hclust(P, N, W, M, S)

`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 cluster covariance matrices.

This function is used internally in meta.TestSubCluster of immunoClust.

A numeric (N-1) x 2-dimensional matrix which gives the minimum index for observations in each of the two clusters merged at the ith step in each row.

The merging distances need not to be monotonic increasing.

Till Sörensen till-antoni.soerensen@charite.de

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).

meta.TestSubCluster, meta.process

data(dat.exp)
r <- dat.exp[[1]]
#hcPairs <- meta.hclust(r@P, r@K, r@w, r@mu, t(apply(r@sigma,1,c)))
hcPairs <- meta.hclust(npar(r), ncls(r), weights(r), 
    mu(r), t(apply(sigma(r),1,c)))