topGOdata-class {topGO}R Documentation

Class "topGOdata"

Description

TODO: The node atributes 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 exprSet class. In this way we can use tests like global test, globalAncova.... – ALL variables sarting 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" ~~
graph:
Object of class "graphNEL" ~~

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"): ...
genesInTerm
signature(object = "topGOdata", whichGO = "character"): ...
genesInTerm
signature(object = "topGOdata", whichGO = "missing"): ...
genTable
signature(object = "topGOdata", resList = "list"): ...
GenTable
signature(object = "topGOdata", ...): ...
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"): ...
numGenes
signature(object = "topGOdata"): ...
ontology<-
signature(object = "topGOdata"): ...
ontology
signature(object = "topGOdata"): ...
print
signature(x = "topGOdata"): ...
sigGenes
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, mapGenes2GOgraph, annFUN.hgu

Examples


## load the ALL dataset and the annotation library
library(ALL); data(ALL)
affyLib <- annotation(ALL)
library(package = affyLib, character.only = TRUE)

library(genefilter)
f1 <- pOverA(0.25, log2(100))
f2 <- function(x) (IQR(x) > 0.5)
ff <- filterfun(f1, f2)
ALL <- ALL[genefilter(ALL, ff), ]

## obtain the list of differentially expressed genes
## discriminate B-cell from T-cell
classLabel <- as.integer(sapply(ALL$BT, function(x) return(substr(x, 1, 1) == 'T')))

## over-expressed genes for T-cell samples
geneList <- getPvalues(exprs(ALL), classlabel = classLabel)

## the distribution of the adjusted p-values
hist(geneList, 100)
hist(geneList[geneList < 1], 100)

## define a function to select the "significant" genes
topDiffGenes <- function(allScore) {
  return(allScore < 0.01)
}
## 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.hgu,
              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 1.2.1 Index]