predefinedAnalyses {GiANT}R Documentation

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()

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.

Value

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

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


	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")


[Package GiANT version 1.3.4 Index]