ISAKEGG: Calculate KEGG Pathway enrichment for transcription modules

Description

KEGG pathway enrichment is calculated for each ISA module separately. In the end the result is corrected for multiple hypothesis testing.

Usage

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

Arguments

modules

An ISAModules object, a set of ISA modules.

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.

Value

KEGGListHyperGResult object.

Details

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of online databases dealing with genomes, enzymatic pathways, and biological chemicals. The PATHWAY database records networks of molecular interactions in the cells, and variants of them specific to particular organisms.

The hypergeometric test, a version Fisher's exact test, takes a KEGG pathway and a gene set (in our case coming from an ISA module) and asks whether the number of genes in the set participating in the pathway, is significantly more (or less) than what one would expect by chance. ISAKEGG performs the hypergeometric test for every module, for all KEGG pathways. The KEGG data is taken from the KEGG.db package and the annotation package of the chip.

ISAKEGG currently cannot test for under-representation.

References

http://www.genome.jp/kegg/

Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M., The KEGG resource for deciphering the genome, Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80. 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.

Examples

Run this code

data(ALLModulesSmall)
KEGG <- ISAKEGG(ALLModulesSmall)
KEGG
sigCategories(KEGG)[[1]]
summary(KEGG)[[1]][,1:5]

Run the code above in your browser using DataCamp Workspace