topGOdata-class

0th

Percentile

Class "topGOdata"

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)

Keywords
classes, graphs
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"): ...

See Also

buildLevels, annFUN

Aliases
  • topGOdata-class
  • allGenes
  • attrInTerm
  • countGenesInTerm
  • description<-
  • description
  • feasible<-
  • feasible
  • geneSelectionFun<-
  • geneSelectionFun
  • genes
  • genesInTerm
  • graph<-
  • graph
  • numGenes
  • ontology<-
  • ontology
  • sigGenes
  • numSigGenes
  • termStat
  • updateGenes
  • updateTerm<-
  • usedGO
  • expressionMatrix
  • phenotype
  • expressionMatrix,topGOdata-method
  • phenotype,topGOdata-method
  • geneScore
  • geneScore,topGOdata,missing-method
  • geneScore,topGOdata,character-method
  • scoresInTerm
  • scoresInTerm,topGOdata,missing-method
  • scoresInTerm,topGOdata,character-method
  • show,topGOdata-method
  • allGenes,topGOdata-method
  • attrInTerm,topGOdata,character,character-method
  • attrInTerm,topGOdata,character,missing-method
  • countGenesInTerm,topGOdata,character-method
  • countGenesInTerm,topGOdata,missing-method
  • description<-,topGOdata,ANY-method
  • description,topGOdata-method
  • feasible<-,topGOdata-method
  • feasible,topGOdata-method
  • geneScore,topGOdata-method
  • geneSelectionFun<-,topGOdata-method
  • geneSelectionFun,topGOdata-method
  • genes,topGOdata-method
  • genesInTerm,topGOdata,character-method
  • genesInTerm,topGOdata,missing-method
  • graph<-,topGOdata-method
  • graph,topGOdata-method
  • initialize,topGOdata-method
  • numGenes,topGOdata-method
  • ontology<-,topGOdata-method
  • ontology,topGOdata-method
  • print,topGOdata-method
  • sigGenes,topGOdata-method
  • numSigGenes,topGOdata-method
  • termStat,topGOdata,character-method
  • termStat,topGOdata,missing-method
  • updateGenes,topGOdata,numeric,function-method
  • updateGenes,topGOdata,factor,missing-method
  • updateTerm<-,topGOdata,character-method
  • usedGO,topGOdata-method
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)
Documentation reproduced from package topGO, version 2.24.0, License: LGPL

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