R: Plots the results of an uncertainty analysis.

plot {GiANT} | R Documentation

## Plots the results of an uncertainty analysis.

### Description

For each percentage of original gene set genes, the quantiles of the distribution obtained by a resampling simulation are plotted. Significance threshold (quantile of the Null distribution) and the test statistic of the original gene set are drawn as horizontal lines.

### Usage

    ## S3 method for class 'uncertaintyResult'
    plot(x,
    	signLevel = x$signLevel,
    	addLegend = TRUE,
    	addMinimalStability = FALSE,
    	...)

### Arguments

`x` | A result of a call to `evaluateGeneSetUncertainty` (see also Details).
`signLevel` | Only results with significance level smaller than the given value are plotted.
`addLegend` | If set to true (default), a `legend` is added to the plot.
`addMinimalStability` | If set to true, a line is added to the plot giving the minimal stability.
`...` | Other parameters which can be used for histograms (see `plot`).

### Details

The function plots the quantiles of the resampling distributions for evaluated degrees of fuzziness. It requires the significance assessment step of the enrichment analysis configuration (parameter `significance` or `gsAnalysis`) to be a computer-intensive testing procedure that yields a distribution of gene set statistic values under the null hypothesis. Predefined configurations for which this plot works are `analysis.gsea`, `analysis.averageCorrelation` and `analysis.averageTStatistic`.

Three lines, corresponding to the different qunatiles with one dot per fuzziness evaluation (`k`) are plotted for the analysis in `x`. The significance threshold is shown as a green horizontal line. The statistic value of the original input set is depicted as a red horizontal line.

If `addMinimalStability` is `TRUE`, the lower bound of the stability is ploted as a dotted line.

### See Also

`geneSetAnalysis`, `predefinedAnalyses`, `gsAnalysis`, `evaluateGeneSetUncertainty`

### Examples

    # load data
    require(GlobalAncova)
    data(vantVeer)
    data(phenodata)
    data(pathways)

    res <- evaluateGeneSetUncertainty(
    	# parameters for evaluateGeneSetUncertainty
    	dat = vantVeer,
    	geneSet = pathways[[1]],
    	analysis = analysis.averageCorrelation(),
    	numSamplesUncertainty = 10,
    	N = seq(0.1,0.9, by=0.1),
    	# additional parameters for analysis.averageCorrelation
    	labs = phenodata$metastases,
    	numSamples = 100)

    # plot the results for the cell cycle control gene set
    plot(res, addMinimalStability = TRUE)

[Package GiANT version 1.2 Index]