cell.hclust: Hierarchical Model Based Clustering of Cell-events in the...

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

Description

Performs model based agglomerative clustering on cell event observations with weights. It is used in the interative cell event clustering approach of immunoClust to obtain an initial cluster membership for the EM(t)-iteration.

Usage

cell.hclust(data, weights=NULL)

Arguments

data	The numeric N x P-dimensional data matrix to cluster. Each row contains a P-dimensional overservation vector.
weights	The N-dimensional vector of optional weights to be applied for the overservations.

Details

This function is used internally in cell.TestSubCluster procedure of immunoClust.

Value

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.

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

cell.TestSubCluster, cell.process

Examples

data(dat.fcs)
inc <- sample(1:nrow(dat.fcs), 50)
result <- cell.hclust(exprs(dat.fcs)[inc,])

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