topGOdata-class: 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)

Arguments

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"): ...

Examples

Run this code

## 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)