splom.immunoClust {immunoClust} | R Documentation

## Scatterplot Matrix of immunoClust Clustering Results

### Description

This method generates scatterplot matrix revealing the cluster assignment.

### Usage

    ## S4 method for signature 'immunoClust,missing'
    splom(x, data, include=seq_len(x@K), ...)

    ## S4 method for signature 'immunoClust,flowFrame'
    splom(x, data, include=seq_len(x@K),
    subset=seq_len(length(attributes(x)$param)), N=NULL,label=NULL, desc=NULL,
    add.param=c(), ...)

    ## S4 method for signature 'immunoClust,matrix'
    splom(x, data, include=seq_len(x@K),
    subset=seq_len(length(attributes(x)$param)), N=NULL, label=NULL,
    desc=NULL, ...)

    datSplom(label, data, subset=seq_len(ncol(data)),
    include=seq_len(nrow(data)), ...)

### Arguments

`x` | An object of class `immunoClust` as return by `cell.process` or `meta.process`.
`data` | Missing, a matrix, or object of class `flowFrame`. This is the object of observations on which `cell.process` was performed.
`include` | A numeric vector specifying which clusters will be shown on the plot. By default, all clusters are included.
`subset` | A numeric vector indicating which parameters are selected for the scatterplot matrix.
`N` | An integer for the maximum number of observations to be plotted. By default all observations are plotted.
`label` | A integer vector for the cluster mebership of the observations. By default this is `x@label`.
`desc` | A character vector for the parameter description.
`add.param` | A list of additional parameters to plot, which are not used for clustering.
`...` | Further graphical parameters passed to the generic function `splom`.

### Value

An object of class `trellis` as returned by the generic `splom` function of the `lattice`-package. The `print` method (called by default) will plot it on an appropriate plotting device.

### 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`

### Examples

    data(dat.fcs)
    data(dat.exp)
    # cell clustering results of dat.fcs
    dat.res <- dat.exp[[1]]
    dat.trans <- trans.ApplyToData(dat.res, dat.fcs)
    splom(dat.res, data=dat.trans, N=1000)

[Package immunoClust version 1.19.4 Index]