cell.ClustData: Model Based Clustering of Data for a pre-defined Number of...

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

Description

Performs EM-iteration on cell events, where an initial event cluster membership is obtained by hierarchical clustering on a sample subset given a number of clusters.

Usage

cell.ClustData(data, K, parameters=NULL, expName="immunoClust Experiment", 
                sample.number=1500, sample.standardize=TRUE,
                B=50, tol=1e-5, modelName="mvt") 

Arguments

data	A numeric matrix, data frame of observations, or object of class flowFrame. Rows correspond to observations and columns correspond to measured parameters.
K	Given number of clusters for the final model.
parameters	A character vector specifying the parameters (columns) to be included in clustering. When it is left unspecified, all the parameters will be used.
expName	The name of the clustering experiment.
sample.number	The maximum number of samples used for initial hierarchical clustering.
sample.standardize	A numeric indicating whether the samples for hierarchical clustering are standardized (mean=0, SD=1).
B	The maximum number of EM-iterations.
tol	The tolerance used to assess the convergence of the EM-algorithm.
modelName	Used mixture model; either "mvt" for a t-mixture model or "mvn" for a Gaussian Mixture model.

Details

Although this function provides the possiblity to cluster an abitrary set of observed data into a fixed number of clusters, this function is used in the immunoClust-pipeline only for the calculation of the initial model with one cluster.

Value

The fitted model cluster 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

immunoClust-object, cell.hclust

Examples

data(dat.fcs)
res <- cell.ClustData(dat.fcs, parameters=c("FSC-A", "SSC-A"), 5)
summary(res)

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