cell.ClustData {immunoClust} | R Documentation

## Model Based Clustering of Data for a pre-defined Number of Clusters

### 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.seed=1, 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.seed` | The seed integer for the random number generator.
`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 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

`immunoClust-object`, `cell.hclust`

### Examples

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

[Package immunoClust version 1.16.0 Index]