hist: Null distribution histogram and statistic of the input set...
In GiANT: Gene Set Uncertainty in Enrichment Analysis

Description Usage Arguments Details See Also Examples

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.

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

`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`).

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.

geneSetAnalysis, predefinedAnalyses, gsAnalysis, evaluateGeneSetUncertainty, plot.uncertaintyResult

# 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 = 10)

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