splom.immunoClust: Scatterplot Matrix of immunoClust Clustering Results

In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

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 [e-mail]

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)

immunoClust documentation built on Nov. 8, 2020, 5:19 p.m.