cell.EM {immunoClust} | R Documentation

## immunoClust EMt-iteration on Cell-events given initial Model Parameters

### Description

Performs EMt-iteration on cell event observations giving initial model parameters and returns the fitted clusters information in an object of class `immunoClust`.

### Usage

    cell.EM(data, parameters=NULL, expName="immunoClust Experiment",
        history=NULL, state=NULL,
        K, w, m, s, B=50, tol=1e-5, bias=0.5, modelName="mvt")

    cell.Estimation(data, parameters=NULL, expName="immunoClust Experiment",
        history=NULL, state=NULL,
        K, w, m, s, modelName="mvt")

### Arguments

`data` | A numeric matrix, data frame of observations, or object of class flowFrame.
`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.
`history` | experimental; unused so far.
`state` | experimental: unused so far.
`K` | The number of clusters.
`w` | The K-dimensional vector of the mixture proportions.
`m` | The K x P-dimensional matrix of the K estimated cluster means.
`s` | The K x P x P-dimensional matrix of the K estimated cluster covariance matrices.
`B` | The maximum number of EMt-iterations.
`tol` | The tolerance used to assess the convergence of the EMt-algorithms.
`bias` | The ICL-bias used in the EMt-algorithm.
`modelName` | Used mixture model; either `"mvt"` or `"mvn"` for a t- or Gaussian mixture model respectively.

### Details

Whereas `cell.EM` performs a complete EMt-iteration, `cell.Estimate` only calculates the posterior probabilities and the Maximum-A-Posterior estimators of cluster membership for all events.

### Value

The fitted 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

`cell.ME`, `cell.FitModel`

### Examples

    data(dat.fcs)
    data(dat.exp)
    ## cell.clustering result for dat.fcs
    r <- dat.exp[[1]]
    summary(r)
    ## apply model parameter to all (unfiltered) events
    dat.trans <- trans.ApplyToData(r, dat.fcs)
    #r2 <- cell.EM(dat.trans, parameters=r@parameters,
    #    K=r@K, w=r@w,m=r@mu,s=r@sigma)
    r2 <- cell.EM(dat.trans, parameters(r), K=ncls(r),
                w=weights(r),m=mu(r),s=sigma(r))
    summary(r2)

[Package immunoClust version 1.19.4 Index]