enrichment: Enrichment analysis for transcription modules, based on...
In eisa: Expression data analysis via the Iterative Signature Algorithm

Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs enrichment analysis for each ISA module separately, comparing it to user-defined categories. It is useful to test against other databases and annotations than the Gene Ontology or KEGG pathways.

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ISAEnrichment (modules, categories, ann = annotation(modules),
    features = featureNames(modules), hgCutoff = 0.05,
    correction = TRUE, correction.method = "holm")

`modules`	An `ISAModules` object, a set of ISA modules.
`categories`	A named list of gene categories. The names of the entries are used as category names. Each entry of the list must be a character vector containing Entrez gene ids.
`ann`	Character scalar. The annotation package to be used. By default it is taken from the `modules` argument.
`features`	Character vector. The names of the features. By default it is taken from the `modules` argument.
`hgCutoff`	Numeric scalar. The cutoff value to be used for the enrichment significance. This can be changed later, without recalculating the test.
`correction`	Logical scalar, whether to perform multiple hypothesis testing correction.
`correction.method`	Character scalar, the multiple testing correction method to use. Possible values: “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”. See the `p.adjust` function for details on these.

This function performs enrichment analysis, based on user defined gene labels. It is useful if one want to test ISA modules against databases, other than GO and KEGG.

The hypergeometric test, a version Fisher's exact test, takes a gene label and a gene set (in our case coming from an ISA module) and asks whether the number of genes in the set labelled by the label is significantly more (or less) than what one would expect by chance.

ISAEnrichment performs the hypergeometric test for every module, for all user supplied gene labels. The mapping from the probe ids on the array to Entrez Ids is done using the appropriate chip annotation package.

ISAEnrichment currently cannot test for under-representation.

A GeneralListHyperGResult object.

Gabor Csardi csardi.gabor@gmail.com

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

ISAGO, ISACHR, ISAKEGG and ISAmiRNA for other enrichment calculations.

The Category package.

data(ALLModulesSmall)
library(hgu95av2.db)
entrez <- unique(unlist(mget(featureNames(ALLModulesSmall), hgu95av2ENTREZID)))
categories <- lapply(letters, function(x) sample(entrez, 100))
names(categories) <- letters
fakeEnrichment1 <- ISAEnrichment(ALLModulesSmall, categories, correction=FALSE)
fakeEnrichment2 <- ISAEnrichment(ALLModulesSmall, categories, correction=TRUE)