plot: 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 uncertaintyResult
plot(x,
	signLevel = x$signLevel,
	addLegend = TRUE,
	addMinimalStability = FALSE,
	...)

Value

No return value, called for creating a plot.

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.

Examples

Run this code

# load data
data(exampleData)

  res <- evaluateGeneSetUncertainty(
  	# parameters for evaluateGeneSetUncertainty
  	dat = countdata,
  	geneSet = pathways[[1]],
  	analysis = analysis.averageCorrelation(),
  	numSamplesUncertainty = 10,
  	N = seq(0.1,0.9, by=0.1),
  	# additional parameters for analysis.averageCorrelation
  	labs = labels,
  	numSamples = 10)
  
  # plot the results for the cell cycle control gene set
  plot(res, addMinimalStability = TRUE)