analyze {HTSanalyzeR} | R Documentation

## Gene Set Collection Analysis or NetWork Analysis

### 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

    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

    ## 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)

[Package HTSanalyzeR version 2.32.0 Index]