trans.FitToData {immunoClust} | R Documentation

## immunoClust asinh-Transformation Optimization

### Description

Performs variance stabilization transformation estimation on the fluorescense parameters of the observed cell events. It is integrated in the interative cell event clustering approach of immunoClust when transformation estimation should be applied.

### Usage

    trans.FitToData(x, data, B=10, tol=1e-5, certainty=0.3, proc="vsHtransAw")

### Arguments

`x` | The `immunoClust` object of the fitted mixture model and initial estimators for the transformation.
`data` | The numeric matrix, data frame of observations, or object of class flowFrame.
`B` | The maximum number of BFG2 minimizer iterations.
`tol` | The tolerance used to assess the convergence for the BFG2 minimizer.
`certainty` | Minimum probability for cluster membership of an observation to be taken into account.
`proc` | An experimental switch for alternative procedures; should be "vsHtransAw".

### Details

In immunoClust an asinh-transformation h(y)=asinh(a * y + b) is applied for all fluorescence parameter in the observed data.

The transformation optimization `trans.FitToData` requires a fitted model of cluster information together with suitable initial transformation estimation in an `immunoClust` object. It fits the transformation based on the initial scaling values `trans.a` and translation values `trans.b` to the observed data. It returns the optimized transformation parameter in a 2 x P-dimensional matrix, first row for the scaling and second row for the translation values. A scaling value of a=0 on input and output indicates, that a parameter should not be transformed.

The presented transformation optimization ("vsHtransAw") fits only the scaling value. An alternative procedure ("vsHtrans_w") fits both, the scaling and the translation value, but turns out to be less robust.

### Value

Optimized transformation scaling and translation values in a 2 x P-dimensional matrix, first row for the scaling and second row for the translation values.

### 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

`trans.ApplyToData`, `cell.process`

### Examples

    data(dat.fcs)
    data(dat.exp)
    ## in dat.exp the z-matrices of the immunoClust-object are removed
    ## so we have to re-calculate it first ...
    dat.trans <- trans.ApplyToData(dat.exp[[1]], dat.fcs)
    res <- cell.Classify(dat.exp[[1]], dat.trans)
    ## ... now the transformation parameter can be optimzed
    trans.FitToData(res, dat.fcs)

[Package immunoClust version 1.19.4 Index]