Functions to perform global gene set analyses, as used in the globalStat parameter of gsAnalysis.
global.overrepresentation(dat,
geneSet,
coreSet)global.ancova(dat,
geneSet,
labs,
...)
global.test(dat,
geneSet,
labs,
...)
A list containing the following items:
The p-value for the significance of geneSet.
This element is only returned in case of an overrepresentation analysis and consists of a vector of genes included in both sets (geneSet and coreSet).
The full result object returned by fisher.test, GlobalAncova or gt respectively.
A numeric matrix of gene expression values for all analyzed genes. Here, each row corresponds to one gene, and each column corresponds to one sample. The rows must be named with the gene names used in the gene sets.
A gene set in form of a vector of gene names corresponding to the row names of dat.
A gene set of interest resulting from an analysis of dat that should be compared to geneSet in the overrepresentation analysis. This is also a vector of gene names corresponding to the row names of dat.
A vector of class labels for the samples in dat.
Further parameters for GlobalAncova and gt, as defined in the corresponding manual pages. The parameters xx, test.genes and group are set automatically by global.ancova, and the parameters alternative, subsets and response are set automatically by global.test.
Wrapper functions for global gene set analyses.
global.overrepresentation: This function performs an overrepresentation analysis by rating the overlap of geneSet and coreSet with respect to the set of all genes using Fisher's exact test.
global.ancova: This function performs a global gene set enrichment analysis using the global ANCOVA method by Hummel et al. It wraps the GlobalAncova function in the GlobalAncova package.
global.test: This function performs a global gene set enrichment analysis using a global test by Goeman et al. It wraps the gt function in the globaltest package.
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.
geneSetAnalysis, gsAnalysis