Description Objects from the Class Slots Methods Author(s) See Also Examples
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"
~~
ontology
:Object of class "character"
~~
allGenes
:Object of class "character"
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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"
~~
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ## 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)
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