profilePlot {eisa} | R Documentation

## Profile plots for ISA biclusters

### 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 csardi.gabor@gmail.com

### 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")
    }

[Package eisa version 1.40.0 Index]