topGOdata-class {topGO} | R Documentation

## Class "topGOdata"

### Description

TODO: The node attributes are environments containing the genes/probes annotated to the respective node

If genes is a numeric vector than this should represent the gene's score. If it is factor it should discriminate the genes in interesting genes and the rest

TODO: it will be a good idea to replace the allGenes and allScore with an ExpressionSet class. In this way we can use tests like global test, globalAncova.... – ALL variables starting with . are just for internal class usage (private)

### Objects from the Class

Objects can be created by calls of the form `new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...)`. ~~ describe objects here ~~

### Slots

`description`:

  Object of class `"character"` ~~

`ontology`:

  Object of class `"character"` ~~

`allGenes`:

  Object of class `"character"` ~~

`allScores`:

  Object of class `"ANY"` ~~

`geneSelectionFun`:

  Object of class `"function"` ~~

`feasible`:

  Object of class `"logical"` ~~

`nodeSize`:

  Object of class `"integer"` ~~

`graph`:

  Object of class `"graphNEL"` ~~

`expressionMatrix`:

  Object of class `"matrix"` ~~

`phenotype`:

  Object of class `"factor"` ~~

### Methods

allGenes

  `signature(object = "topGOdata")`: ...

attrInTerm

  `signature(object = "topGOdata", attr = "character", whichGO = "character")`: ...

attrInTerm

  `signature(object = "topGOdata", attr = "character", whichGO = "missing")`: ...

countGenesInTerm

  `signature(object = "topGOdata", whichGO = "character")`: ...

countGenesInTerm

  `signature(object = "topGOdata", whichGO = "missing")`: ...

description<-

  `signature(object = "topGOdata")`: ...

description

  `signature(object = "topGOdata")`: ...

feasible<-

  `signature(object = "topGOdata")`: ...

feasible

  `signature(object = "topGOdata")`: ...

geneScore

  `signature(object = "topGOdata")`: ...

geneSelectionFun<-

  `signature(object = "topGOdata")`: ...

geneSelectionFun

  `signature(object = "topGOdata")`: ...

genes

  `signature(object = "topGOdata")`: A method for obtaining the list of genes, as a characther vector, which will be used in the further analysis.

numGenes

  `signature(object = "topGOdata")`: A method for obtaining the number of genes, which will be used in the further analysis. It has the same effect as: `lenght(genes(object))`.

sigGenes

  `signature(object = "topGOdata")`: A method for obtaining the list of significant genes, as a charachter vector.

genesInTerm

  `signature(object = "topGOdata", whichGO = "character")`: ...

genesInTerm

  `signature(object = "topGOdata", whichGO = "missing")`: ...

getSigGroups

  `signature(object = "topGOdata", test.stat = "classicCount")`: ...

getSigGroups

  `signature(object = "topGOdata", test.stat = "classicScore")`: ...

graph<-

  `signature(object = "topGOdata")`: ...

graph

  `signature(object = "topGOdata")`: ...

initialize

  `signature(.Object = "topGOdata")`: ...

ontology<-

  `signature(object = "topGOdata")`: ...

ontology

  `signature(object = "topGOdata")`: ...

termStat

  `signature(object = "topGOdata", whichGO = "character")`: ...

termStat

  `signature(object = "topGOdata", whichGO = "missing")`: ...

updateGenes

  `signature(object = "topGOdata", geneList = "numeric", geneSelFun = "function")`: ...

updateGenes

  `signature(object = "topGOdata", geneList = "factor", geneSelFun = "missing")`: ...

updateTerm<-

  `signature(object = "topGOdata", attr = "character")`: ...

usedGO

  `signature(object = "topGOdata")`: ...

### Author(s)

Adrian Alexa

### See Also

`buildLevels`, `annFUN`

### Examples

    ## load the dataset
    data(geneList)
    library(package = affyLib, character.only = TRUE)

    ## the distribution of the adjusted p-values
    hist(geneList, 100)

    ## how many differentially expressed genes are:
    sum(topDiffGenes(geneList))

    ## build the topGOdata class
    GOdata <- new("topGOdata",
                  ontology = "BP",
                  allGenes = geneList,
                  geneSel = topDiffGenes,
                  description = "GO analysis of ALL data: Differential Expression between B-cell and T-cell",
                  annot = annFUN.db,
                  affyLib = affyLib)

    ## display the GOdata object
    GOdata

    ##########################################################
    ## Examples on how to use the methods
    ##########################################################

    ## description of the experiment
    description(GOdata)

    ## obtain the genes that will be used in the analysis
    a <- genes(GOdata)
    str(a)
    numGenes(GOdata)

    ## obtain the score (p-value) of the genes
    selGenes <- names(geneList)[sample(1:length(geneList), 10)]
    gs <- geneScore(GOdata, whichGenes = selGenes)
    print(gs)

    ## if we want an unnamed vector containing all the feasible genes
    gs <- geneScore(GOdata, use.names = FALSE)
    str(gs)

    ## the list of significant genes
    sg <- sigGenes(GOdata)
    str(sg)
    numSigGenes(GOdata)

    ## to update the gene list
    .geneList <- geneScore(GOdata, use.names = TRUE)
    GOdata ## more available genes
    GOdata <- updateGenes(GOdata, .geneList, topDiffGenes)
    GOdata ## the available genes are now the feasible genes

    ## the available GO terms (all the nodes in the graph)
    go <- usedGO(GOdata)
    length(go)

    ## to list the genes annotated to a set of specified GO terms
    sel.terms <- sample(go, 10)
    ann.genes <- genesInTerm(GOdata, sel.terms)
    str(ann.genes)

    ## the score for these genes
    ann.score <- scoresInTerm(GOdata, sel.terms)
    str(ann.score)

    ## to see the number of annotated genes
    num.ann.genes <- countGenesInTerm(GOdata)
    str(num.ann.genes)

    ## to summarise the statistics
    termStat(GOdata, sel.terms)

[Package topGO version 2.30.1 Index]