topGOdata-class {topGO} | R Documentation |
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 can be created by calls of the form new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...)
.
~~ describe objects here ~~
description
:Object of class "character"
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ontology
:Object of class "character"
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allGenes
:Object of class "character"
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allScores
:Object of class "ANY"
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geneSelectionFun
:Object of class "function"
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feasible
:Object of class "logical"
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nodeSize
:Object of class "integer"
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graph
:Object of class "graphNEL"
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expressionMatrix
:Object of class "matrix"
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phenotype
:Object of class "factor"
~~
signature(object = "topGOdata")
: ...
signature(object = "topGOdata", attr = "character", whichGO = "character")
: ...
signature(object = "topGOdata", attr = "character", whichGO = "missing")
: ...
signature(object = "topGOdata", whichGO = "character")
: ...
signature(object = "topGOdata", whichGO = "missing")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: A method for
obtaining the list of genes, as a characther vector, which will be
used in the further analysis.
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))
.
signature(object = "topGOdata")
: A method for
obtaining the list of significant genes, as a charachter vector.
signature(object = "topGOdata", whichGO = "character")
: ...
signature(object = "topGOdata", whichGO = "missing")
: ...
signature(object = "topGOdata", test.stat = "classicCount")
: ...
signature(object = "topGOdata", test.stat = "classicScore")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(.Object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata")
: ...
signature(object = "topGOdata", whichGO = "character")
: ...
signature(object = "topGOdata", whichGO = "missing")
: ...
signature(object = "topGOdata", geneList = "numeric", geneSelFun = "function")
: ...
signature(object = "topGOdata", geneList = "factor", geneSelFun = "missing")
: ...
signature(object = "topGOdata", attr = "character")
: ...
signature(object = "topGOdata")
: ...
Adrian Alexa
## 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)