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.

# 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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(GiANT)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GiANT/plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot
> ### Title: Plots the results of an uncertainty analysis.
> ### Aliases: plot.uncertaintyResult
>
> ### ** Examples
>
> # load data
> require(GlobalAncova)
Loading required package: GlobalAncova
Loading required package: corpcor
Loading required package: globaltest
Loading required package: survival
> 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)
>
>
>
>
>
> dev.off()
null device
1
>