ISA.KEGG: Calculate KEGG Pathway enrichment for transcription modules
In eisa: Expression data analysis via the Iterative Signature Algorithm

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

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

1 2	ISAKEGG (modules,ann = annotation(modules), features = featureNames(modules), hgCutoff = 0.05, correction = TRUE, correction.method = "holm")

`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.

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.

A KEGGListHyperGResult object.

Gabor Csardi csardi.gabor@gmail.com

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.

ISAGO, ISACHR, ISAmiRNA for other enrichment calculations.

The KEGG.db and Category packages.

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

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KEGG.db contains mappings based on older data because the original
  resource was removed from the the public domain before the most
  recent update was produced. This package should now be considered
  deprecated and future versions of Bioconductor may not have it
  available.  Users who want more current data are encouraged to look
  at the KEGGREST or reactome.db packages

Gene to   test for over-representation 
287  ids tested (0-8 have p < 0.05)
Selected gene set sizes: 11-26 
     Gene universe size: 1552 
     Annotation package: hgu95av2 
[1] "05310" "05320" "05330" "05150" "04672" "05332" "04940" "04612"
           Pvalue OddsRatio  ExpCount Count Size
05310 0.001430229  34.50000 0.2010309     4   12
05320 0.002771392  27.56364 0.2345361     4   14
05330 0.003661459  25.04132 0.2512887     4   15
05150 0.003661459  25.04132 0.2512887     4   15
04672 0.004629415  22.93939 0.2680412     4   16
05332 0.005857642  21.16084 0.2847938     4   17
04940 0.007283715  19.63636 0.3015464     4   18
04612 0.007758801  12.29064 0.5695876     5   34