R: Null distribution histogram and statistic of the input set...

hist {GiANT} | R Documentation

## Null distribution histogram and statistic of the input set for enrichment analyses.

### Description

Plots the distribution of gene set statistic values obtained in different resampling settings of an enrichment analysis, and draws the statistic value of the input set as a vertical line.

### Usage

    ## S3 method for class 'gsaResult'
    hist(x,
    	signLevel = x$signLevel,
    	subset = NULL,
    	ask = FALSE,
    	addLegend = TRUE,
    	...)

### Arguments

`x` | A result of a call to `geneSetAnalysis` (see also Details).
`signLevel` | The significance level that should be applied for the plots. Default is the significance level used for the analysis in `x`.
`subset` | Indices for the results that should be included in the diagram.
`ask` | If set to true, the plot function will prompt for a user input for each new plot that is shown on an interactive device (see `par("ask")`).
`addLegend` | If set to true (default), a `legend` is added to the plot.
`...` | Other parameters which can be used for histograms (see `hist`).

### Details

The function plots the distribution of gene set statistic values under the null hypothesis. 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 p-values under the null hypothesis. Predefined configurations for which this plot works are `analysis.gsea`, `analysis.averageCorrelation` and `analysis.averageTStatistic`.

A histogram is plotted for the analysis in `x`. If `x` includes the analyses for several gene sets, one histogram is plotted for each of the gene sets.

The statistic value of the input set is depicted as a vertical line.

The most common graphical parameters can be supplied as vectors (one entry per analyzed gene set) to vary them between the different analyses. These parameters are: `main`, `xlab`, `ylab`.

### See Also

`geneSetAnalysis`, `predefinedAnalyses`, `gsAnalysis`, `evaluateGeneSetUncertainty`, `plot.uncertaintyResult`

### Examples

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

    res <- geneSetAnalysis(
    	# global parameters
    	dat = vantVeer,
    	geneSets = pathways[3],
    	analysis = analysis.averageCorrelation(),
    	# additional parameters for analysis.averageCorrelation
    	labs = phenodata$metastases,
    	p = 1,
    	numSamples = 100)

    # plot the histogram for the cell cycle control gene set
    hist(res, main = names(pathways[3]))

[Package GiANT version 1.2 Index]