trans.ApplyToData {immunoClust} | R Documentation

## immunoClust asinh-Transformation

### Description

Applies the transformation information of the `immunoClust` object to the raw observed FC dataset.

### Usage

    trans.ApplyToData(x, data, add.param=c(), max.decade=attr(x,"trans.decade"),
        lin.scale=attr(x,"trans.scale") )

### Arguments

`x` | The `immunoClust` object containing the estimators for the transformation `trans.a` and `trans.b`.
`data` | The numeric matrix, data frame of observations, or object of class `flowFrame`.
`add.param` | A list of additional parameters in the `flowFrame`, which are not used for clustering but should be included in the final transformed resulting `flowFrame`.
`max.decade` | A numeric scale for the maximum transformed observation value; if missing or below 0, no scaling of the transformed values is apllied, which is the default in immunoClust.
`lin.scale` | A numeric scaling factor for the linear, i.e. not transformed, parameters; if missing no scaling, i.e. lin.scale=1, is applied, which is the default in immunoClust.

### Details

In immunoClust an asinh-transformation h(y)=asinh(a*y + b) is applied to the fluorescence parameter in the observed data. The scatter parameter are assumed to be linear.

### Value

A matrix or `flowFrame` with replaced transformed oberservation 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

`immunoClust`, `trans.FitToData`, `cell.process`

### Examples

    data(dat.fcs)
    data(dat.exp)
    dat.trans <- trans.ApplyToData(dat.exp[[1]], dat.fcs)
    #
    #plot(dat.exp[[1]], data=dat.trans)
    #

[Package immunoClust version 1.19.4 Index]