profilePlot: Profile plots for ISA biclusters

In eisa: Expression data analysis via the Iterative Signature Algorithm

Description

Line plots to compare biclusters to the background, i.e. the rest of the expression matrix.

Usage

profilePlot (modules, module, eset, plot = c("samples", "features", 
     "both"), norm = "default", background = TRUE, 
    col = gray(0.7), col.mod = 1, type = "l", type.mod = type,
    mean = TRUE, meancol = "green", meancol.mod = "red",
    xlabs = c("Features", "Samples"), ylab = "Expression", 
    ...) 

Arguments

modules	An ISAModules object.
module	Numeric scalar, the module to plot.
eset	An ExpressionSet or ISAExpressionSet object. If an ExpressionSet object is supplied (and the norm argument is not set to ‘raw’), then it is normalised by calling ISANormalize on it. A subset of eset is selected that corresponds to the features included in modules.
plot	Character constant, specifies what to plot. ‘sample’ plots sample scores, ‘features’ plots feature scores. If ‘both’ is given, then the plot is divided into two subplots and both scores are plotted.
norm	Character constant, specifies how to normalize the expression matrix for plotting. It can be of length one or two, the latter for the case when plots are made both for features and samples. Possible values: ‘raw’ uses the raw expression values; ‘feature’ uses featExprs to extract the expression values from the expression set object; ‘sample’ uses sampExprs; ‘default’ means ‘feature’ for sample plots and ‘sample’ for feature plots.
background	Logical scalar, whether to plot the features/samples that are not in the module.
col	Color of lines corresponding to the background features/samples.
col.mod	Color of the lines corresponding to the features/samples included in the module.
type	Type of the plot, for the background features/samples. It is passed to plot.
type.mod	Type of the plot, for the features/samples included in the module. It is passed to plot.
mean	Logical scalar, whether to plot the mean expression for each feature/sample, separately for the samples/features that are in the module and the ones that are not.
meancol	Color of the line for the mean expression values, background.
meancol.mod	Color of the line for the mean expression values, module.
xlabs	Character vector of length one or two. The labels of the horizontal axes of the plot, the second value is used if both the feature and the sample plots are drawn.
ylab	Character vector of length one. The label of the vertical axes.
...	Additional graphical arguments. They are passed to the lines function that creates the lines of the plot.

Details

plot="both" uses the mfrow graphical parameter to create the two subplots. This does not work properly if you already have subplots.

Value

None. (Well, NULL, invisibly.)

Author(s)

Gabor Csardi [e-mail]

References

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

See Also

The similar parallelCoordinates function in the biclust package.

Examples

data(ALLModulesSmall)
library(ALL)
data(ALL)
if (interactive()) {
  profilePlot(ALLModulesSmall, 2, ALL, plot="samples")
}

eisa documentation built on Nov. 8, 2020, 6:47 p.m.