GiANT (version 1.3.4)

predefinedAnalyses: Predefined enrichment analyses

Description

Predefined analysis configurations that can be used in geneSetAnalysis

Usage

analysis.gsea()
analysis.overrepresentation()
analysis.customOverrepresentation()
analysis.averageCorrelation()
analysis.averageTStatistic()
analysis.globalTest()
analysis.globalAncova()

Value

All functions return an object of class gsAnalysis that specifies the corresponding analysis parameters for geneSetAnalysis.

Details

The above functions return configurations for state-of-the-art analysis pipelines that can be used in geneSetAnalysis. All configurations are preconfigured collections of standard methods for the different pipeline steps. The following lists the methods chosen for the different steps and their parameters. For more detailed descriptions of these methods, please refer to the linked manual pages.

• analysis.gsea defines the Gene Set Enrichment Analysis (GSEA) method by Subramanain et al. Here, the gene-level statistic the absolute correlation calculated by gls.cor with the associated parameters labs, method and a preprocessing by transformation.abs. As a gene set statistic, the enrichment score (function gss.enrichmentScore with parameter p) is calculated. The significance is assessed in a permutation test using significance.permutation with testAlternative = "greater" and free parameter numSamples, labs.

• analysis.overrepresentation calculates an overrepresentation analysis using the gene-level statistic gls.tStatistic with parameters pValue(should be TRUE), alternative and labs. The resulting values are then transformed via transformation.adjustAndBinarize (parameters are the adjMethod and threshold). Finally gss.fisherExactTest is used as gene set statistic.

• analysis.customOverrepresentation calculates an overrepresentation analysis using a user-defined core set coreSet. That is, instead of calculating this core set internally based on differential expression as the standard overrepresentation analysis, this function allows for defining custom core sets. It internally uses the global analysis global.overrepresentation.

• analysis.averageCorrelation calculates the gene-level statistic as the absolute correlation using gls.cor (with parameters labs, method) and transformation.abs. The gene set statistic is the mean correlation calculated by gss.mean. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets using significance.sampling (with the parameter numSamples and the preset parameter testAlternative = "greater").

• analysis.averageTStatistic uses the absolute t statistic as the gene-level statistic by applying gls.tStatistic (with parameters labs, pValue, alternative) and transformation.abs. The gene set statistic is the mean t statistic in the gene set as returned by gss.mean. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets using significance.sampling (with the parameter numSamples and the preset parameter testAlternative = "greater").

• analysis.globalTest performs a global gene set enrichment analysis by Goeman et al. by applying the global.test function which in turn wraps the gt function in the globaltest package.

• analysis.globalAncova applies the global ANCOVA method by Hummel et al. using the global method global.ancova which wraps the GlobalAncova function in the GlobalAncova package.

References

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., Mesirov, J. P. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Science of the United States of America, 102, 15545--15550.

Hummel, M., Meister, R., Mansmann, U. (2008) GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics, 24(1), 78--85.

Goeman, J. J., van de Geer, S. A., de Kort, F., van Houwelingen, H. C. (2004) A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1), 93--99.

See Also

geneSetAnalysis, gsAnalysis

Examples

# \donttest{
	data(exampleData)
  # apply a gene set analysis based on the average absolute correlation
  resAvCor <- geneSetAnalysis(
  	# parameters for geneSetAnalysis
  	dat = countdata,
  	geneSets = pathways[1],
  	analysis = analysis.averageCorrelation(),
  	adjustmentMethod = "fdr",
  	# additional parameters for analysis.averageCorrelation
    labs = labels,
  	method = "pearson",
  	numSamples = 10)
  
  # apply an overrepresentation analysis
  resOverrep <- geneSetAnalysis(
  	# parameters for geneSetAnalysis
  	dat = countdata,
  	geneSets = pathways,
  	analysis = analysis.overrepresentation(),
  	adjustmentMethod = "fdr",
  	# additional parameters for analysis.overrepresentation
  	pValue = TRUE,
  	threshold = 0.1,
  	labs = labels
  )
  
  # apply a global analysis using GlobalAncova
  resGA <- geneSetAnalysis(
  	# parameters for geneSetAnalysis
  	dat = countdata,
  	geneSets = pathways[1],
  	analysis = analysis.globalAncova(),
  	adjustmentMethod = "fdr",
  	# additional parameters for analysis.globalAncova
  	labs = labels,
  	method = "approx")
# }