GiANT (version 1.3.4)

hist: 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 gsaResult
hist(x,
	signLevel = x$signLevel,
	subset = NULL,
	ask = FALSE,
	addLegend = TRUE,
	...)

Value

Returns a list with all the underlying data for the plotted histograms as invisible object.

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
data(exampleData)
  res <- geneSetAnalysis(
  	# global parameters
  	dat = countdata,
  	geneSets = pathways[3], 
  	analysis = analysis.averageCorrelation(), 
  	# additional parameters for analysis.averageCorrelation
  	labs = labels,
  	p = 1,
  	numSamples = 10)
  
  # plot the histogram for the cell cycle control gene set
  hist(res, main = names(pathways[3]))