ListHyperGParams-class {eisa} | R Documentation

## Classes for quick GO/KEGG/CHR/miRNA target or other enrichment calculation of multiple gene sets.

### Description

These classes extend the `HyperGParams` class from the `Category` package to perform enrichment calculation quickly for multiple gene sets.

### Usage

    ## S4 method for signature 'ListHyperGParams'
    makeValidParams(object)
    ## S4 method for signature 'ListHyperGParams'
    drive(p)
    ## S4 replacement method for signature 'ListHyperGParams,logical'
    drive(p) <- dri

    ## S4 method for signature 'GOListHyperGParams'
    ontology(object)
    ## S4 replacement method for signature 'GOListHyperGParams,character'
    ontology(object) <- go
    ## S4 method for signature 'GOListHyperGParams'
    conditional(r)
    ## S4 replacement method for signature 'GOListHyperGParams,logical'
    conditional(r) <- cond

    ## S4 method for signature 'ListHyperGParams'
    hyperGTest(p)

### Arguments

`object,p,r` | A `ListHyperGParams` object.
`dri` | Logical scalar, whether to store the genes that are in the intersection of the specified gene set and the annotation category.
`go` | Character scalar, the ontology for GO, possible values: ‘BP’, ‘CC’, ‘MF’.
`cond` | Logical scalar, whether to perform conditional enrichment calculation. Currently this option is ignored.

### Details

The `ListHyperGParams` abstract class extends `HyperGParams` and allows to specify a list of gene sets for the enrichment calculation instead of a single set.

`ListHyperGParams` calculates the enrichment much faster than the original `HyperGParams` classes in the `Category` package, especially if the calculation is performed against the same gene universe for many gene sets.

`ListHyperGParams` is an abstract class, it is not possible to instantiate objects from it. Instead, its various extensions must be used: `GOListHyperGParams`, `KEGGListHyperGParams`, `CHRListHyperGParams` and `miRNAListHyperGParams`.

The various `ListHyperGParams` objects can be created with the standard `new` command, by giving all necessary arguments. Please see the examples below.

### Value

`makeValidParmas` returns another `ListHyperGParams` instance that has the same class as its arguments'.

`ontology` returns a character vector of length one.

`conditional` returns a logical vector of length one.

`drive` returns a logical vector of length one.

### Member functions

Most of these functions are analogous to the ones defined in the `Category` package, the only difference is that they handle `ListHyperGParams` objects.

`makeValidParams` validates `ListHyperGParams` object, in particular, it removes duplicate genes, both from the gene universe and the specified gene sets; and it also makes sure that all genes in the gene sets are included in the universe.

`ontology` can be used to query or set the ontology for enrichment calculated against the GO database.

`conditional` queries or sets whether conditional GO enrichment will be performed. This feature is not implemented yet, see the `Category` and `GOstats` packages for a working implementation and more information.

`drive` queries or sets whether the intersections of the gene sets and the universe are stored in the result object. This information can be calculated later as well, but it is faster to store it at the same time when the hypergeometric test is performed.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### See Also

Functions for enrichment calculation of ISA modules: `ISAGO`, `ISAKEGG`, `ISACHR`, `ISAmiRNA`.

Perhaps see also the vignette in the `GOstats` package.

### Examples

    # GO enrichment, "by hand"
    # Load data first
    data(ALLModulesSmall)

    # Create gene sets
    library(hgu95av2.db)
    genes <- getFeatureNames(ALLModulesSmall)
    entrez <- lapply(genes, function(x) na.omit(unlist(mget(x,
                  hgu95av2ENTREZID))))

    # Create universe
    universe <- na.omit(unlist(mget(featureNames(ALLModulesSmall),
                  hgu95av2ENTREZID)))

    # Create parameter object
    param <- new("GOListHyperGParams", geneIds=entrez, universeGeneIds=universe,
                 pvalueCutoff=0.01, drive=FALSE, ontology="BP",
                 conditional=FALSE, testDirection="over",
                 annotation=annotation(ALLModulesSmall))

    # Do the calculation
    GOBP <- hyperGTest(param)

    # Inspect the result
    GOBP
    summary(GOBP)[[1]]

    # How to create other parameter objects
    paramKEGG <- new("KEGGListHyperGParams", geneIds=entrez,
                     universeGeneIds=universe, drive=FALSE,
                     annotation=annotation(ALLModulesSmall))
    paramCHR <- new("CHRListHyperGParams", geneIds=entrez,
                     universeGeneIds=universe, drive=FALSE,
                     annotation=annotation(ALLModulesSmall))

    # Enrichment with user-supplied categories, we use a list of
    # hand-picked genes that are involved in myelin formation
    mygenes <- c("YARS", "NFKB2", "NGFR", "CDH1", "NFAT5", "NDRG1", "GAP43",
     "EGR2", "MSN", "ROCK1", "SREBF2", "SOX10", "FIG4", "EGR1", "PIK3R1",
     "CDC42", "EDN3", "EDNRB", "NCAM1", "DHH", "OMG", "PMP22", "LAMA4",
     "MPDZ", "MTMR2", "REL", "S100A1", "ITGA4", "GFAP", "FGF2", "RPSA",
     "CADM1", "CDH19", "DNM2", "PAX3", "SREBF1", "DAG1", "DRP2", "SDC2",
     "MBP", "RELA", "RELB", "JUN", "NAB1", "MOBP", "SKI",  "COL5A2", "RHOA",
     "NFASC", "NEFL", "MPZ", "MAG", "EDNRA", "ERBB4", "LITAF", "MMP2",
     "PLP1", "CDKN1A", "PAK1", "RDX", "GJB1", "LAMA5", "JAM3", "ITGB1",
     "PARD3", "FABP7", "LAMA2", "ERBB3", "CADM4", "FOXO4", "TSPAN31",
     "GPR126", "PTK2", "RAC1", "CDKN2A", "CLDN5", "ID2", "LAMC1", "SOX2",
     "CNTN2", "ERBB2", "NFKB1", "NAB2", "EDN2", "MMP9", "CCND1", "L1CAM",
     "MOG")

    library(org.Hs.eg.db)
    myentrez <- na.omit(mapIds(org.Hs.eg.db, mygenes, 'ENTREZID',
    keytype='SYMBOL'))
    categories <- list(myelin=myentrez)

    data(ALLModules)
    genes2 <- getFeatureNames(ALLModules)
    entrez2 <- lapply(genes2, function(x) na.omit(unlist(mget(x,
                  hgu95av2ENTREZID))))

    # Create universe
    universe2 <- na.omit(unlist(mget(featureNames(ALLModules),
                  hgu95av2ENTREZID)))

    paramMY <- new("GeneralListHyperGParams", geneIds=entrez2,
                   universeGeneIds=universe2, drive=FALSE,
                   annotation=annotation(ALLModulesSmall),
                   categories=categories)
    MY <- hyperGTest(paramMY)
    MY
    summary(MY)[[1]]

[Package eisa version 1.40.0 Index]