plot.flowClust: Scatterplot of Clustering Results

In RGLab/flowClust: Clustering for Flow Cytometry

Scatterplot of Clustering Results

Description

This method generates scatterplot revealing the cluster assignment, cluster boundaries according to the specified percentile as well as supplemental information like outliers or filtered observations.

Usage

plot(x, y, ...)

## S4 method for signature 'flowClust,missing'
plot(
  x,
  data,
  subset = c(1, 2),
  ellipse = T,
  show.outliers = T,
  show.rm = F,
  include = 1:(x@K),
  main = NULL,
  grayscale = F,
  col = (if (grayscale) gray(1/4) else 2:(length(include) + 1)),
  pch = ".",
  cex = 0.6,
  col.outliers = gray(3/4),
  pch.outliers = ".",
  cex.outliers = cex,
  col.rm = 1,
  pch.rm = 1,
  cex.rm = 0.6,
  ecol = 1,
  elty = 1,
  level = NULL,
  u.cutoff = NULL,
  z.cutoff = NULL,
  npoints = 100,
  add = F,
  ...
)

## S4 method for signature 'flowClustList,missing'
plot(
  x,
  data,
  subset = c(1, 2),
  ellipse = T,
  show.outliers = T,
  show.rm = F,
  include = 1:(x@K),
  main = NULL,
  grayscale = F,
  col = (if (grayscale) gray(1/4) else 2:(length(include) + 1)),
  pch = ".",
  cex = 0.6,
  col.outliers = gray(3/4),
  pch.outliers = ".",
  cex.outliers = cex,
  col.rm = 1,
  pch.rm = 1,
  cex.rm = 0.6,
  ecol = 1,
  elty = 1,
  level = NULL,
  u.cutoff = NULL,
  z.cutoff = NULL,
  npoints = 501,
  add = F,
  ...
)

Arguments

x	Object returned from flowClust.
y	missing
...	Further graphical parameters passed to the generic function plot.
data	A matrix, data frame of observations, or object of class flowFrame. This is the object on which flowClust was performed.
subset	A numeric vector of length two indicating which two variables are selected for the scatterplot. Alternatively, a character vector containing the names of the two variables is allowed if x@varNames is not NULL.
ellipse	A logical value indicating whether the cluster boundary is to be drawn or not. If TRUE, the boundary will be drawn according to the level specified by level or cutoff.
show.outliers	A logical value indicating whether outliers will be explicitly shown or not.
show.rm	A logical value indicating whether filtered observations will be shown or not.
include	A numeric vector specifying which clusters will be shown on the plot. By default, all clusters are included.
main	Title of the plot.
grayscale	A logical value specifying if a grayscale plot is desired. This argument takes effect only if the default values of relevant graphical arguments are taken.
col	Color(s) of the plotting characters. May specify a different color for each cluster.
pch	Plotting character(s) of the plotting characters. May specify a different character for each cluster.
cex	Size of the plotting characters. May specify a different size for each cluster.
col.outliers	Color of the plotting characters denoting outliers.
pch.outliers	Plotting character(s) used to denote outliers. May specify a different character for each cluster.
cex.outliers	Size of the plotting characters used to denote outliers. May specify a different size for each cluster.
col.rm	Color of the plotting characters denoting filtered observations.
pch.rm	Plotting character used to denote filtered observations.
cex.rm	Size of the plotting character used to denote filtered observations.
ecol	Color(s) of the lines representing the cluster boundaries. May specify a different color for each cluster.
elty	Line type(s) drawing the cluster boundaries. May specify a different line type for each cluster.
level, u.cutoff, z.cutoff	These three optional arguments specify the rule used to identify outliers. By default, all of them are left unspecified, meaning that the rule stated in x@ruleOutliers will be taken. Otherwise, these arguments will be passed to ruleOutliers.
npoints	The number of points used to draw each cluster boundary.
add	A logical value. If TRUE, add to the current plot.

Note

The cluster boundaries need not be elliptical since Box-Cox transformation has been performed.

Author(s)

Raphael Gottardo <[e-mail]>, Kenneth Lo <[e-mail]>

References

Lo, K., Brinkman, R. R. and Gottardo, R. (2008) Automated Gating of Flow Cytometry Data via Robust Model-based Clustering. Cytometry A 73, 321-332.

See Also

flowClust

RGLab/flowClust documentation built on June 9, 2025, 2:51 p.m.