Gene Set Collection Analysis or NetWork Analysis

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Description

This is a generic function.

When implemented as the S4 method for objects of class GSCA, this function invokes function analyzeGeneSetCollections to do hypergeometic tests and GSEA.

When implemented on an object of class NWA, it calls function networkAnalysis to do subnetwork identification.

To use this function for objects of class GSCA:

analyze(object, para = list(pValueCutoff = 0.05, pAdjustMethod = "BH", nPermutations = 1000, minGeneSetSize = 15, exponent = 1), verbose = TRUE, doGSCA=TRUE, doGSEA=TRUE)

To use this function for objects of class NWA:

analyze(object, fdr=0.001, species, verbose=TRUE)

Usage

1
analyze(object, ...)

Arguments

object

an object. When this function is implemented as the S4 method of class 'GSCA' or 'NWA', this argument is an object of class 'GSCA' or 'NWA'.

...

other arguments depending on class (see below for the arguments supported by the method of class 'GSCA' or 'NWA')

para:

a list of parameters for GSEA and hypergeometric tests including:

para$pValueCutoff

a single numeric value specifying the cutoff for p-values considered significant

para$pAdjustMethod:

a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details)

para$nPermutations:

a single integer or numeric value specifying the number of permutations for deriving p-values in GSEA

para$minGeneSetSize:

a single integer or numeric value specifying the minimum number of elements in a gene set that must map to elements of the gene universe. Gene sets with fewer than this number are removed from both hypergeometric analysis and GSEA.

para$exponent:

a single integer or numeric value used in weighting phenotypes in GSEA (see 'gseaScores' function)

fdr:

a single numeric value specifying the false discovery for the scoring of nodes (see BioNet::scoreNodes and Dittrich et al., 2008 for details)

species:

a single character value specifying the species for which the data should be read. The current version supports one of the following species: "Dm" ("Drosophila_melanogaster"), "Hs" ("Homo_sapiens"), "Rn" ("Rattus_norvegicus"), "Mm" ("Mus_musculus"), "Ce" ("Caenorhabditis_elegans"). This is an optional argument here. If it is provided, then the labels of nodes of the identified subnetwork will be mapped from Entrez IDs to gene symbols; otherwise, Entrez IDs will be used as labels for those nodes.

verbose:

a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE)

doGSOA:

a single logical value specifying to perform gene set overrepresentation analysis (when doGSOA=TRUE) or not (when doGSOA=FALSE)

doGSEA:

a single logical value specifying to perform gene set enrichment analysis (when doGSEA=TRUE) or not (when doGSEA=FALSE)

Details

For objects of class GSCA:

The function will store the results from function analyzeGeneSetCollections in slot result, and update information about these results to slot summary of class GSCA.

See function analyzeGeneSetCollections for the detailed information about the returned results.

For objects of class NWA:

The function will store the subnetwork module identified by BioNet (if species is given, labels of nodes will also be mapped from Entrez IDs to gene symbols), and update information about these results to slot summary of class NWA.

See function networkAnalysis for the detailed information about the returned results.

Value

In the end, this function will return an updated object of class GSCA or NWA.

Author(s)

Xin Wang xw264@cam.ac.uk

References

Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010 Apr 15;26(8):1129-30.

Dittrich MT, Klau GW, Rosenwald A., Dandekar T and Muller T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 2008 24(13): i223-i231.

See Also

analyzeGeneSetCollections, networkAnalysis

Examples

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## Not run: 
library(org.Dm.eg.db)
library(KEGG.db)
##load data for enrichment analyses
data("KcViab_Data4Enrich")
##select hits
hits <- names(KcViab_Data4Enrich)[which(abs(KcViab_Data4Enrich) > 2)]
##set up a list of gene set collections
PW_KEGG <- KeggGeneSets(species = "Dm")
gscList <- list(PW_KEGG = PW_KEGG)
##create an object of class 'GSCA'
gsca <- new("GSCA", listOfGeneSetCollections=gscList,
geneList = KcViab_Data4Enrich, hits = hits)
##print gsca
gsca
##do preprocessing (KcViab_Data4Enrich has already been preprocessed)
gsca <- preprocess(gsca, species="Dm", initialIDs = "Entrez.gene", 
keepMultipleMappings = TRUE, duplicateRemoverMethod = "max", 
orderAbsValue = FALSE)
##print gsca again
gsca
##do hypergeometric tests and GSEA
gsca <- analyze(gsca, para = list(pValueCutoff = 0.05, pAdjustMethod 
= "BH", nPermutations = 1000, minGeneSetSize = 100,exponent = 1), doGSOA=TRUE, 
doGSEA=TRUE)
##updated object
gsca
summarize(gsca)

## End(Not run)

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