topGOdata-class(topGO)
topGOdata-class()所属R语言包：topGO
Class "topGOdata"
类“topGOdata”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
TODO: The node attributes are environments containing the genes/probes annotated to the respective node
TODO：在节点属性的环境中包含的基因/探针注明各自的节点
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
如果基因是一个数值向量，比本应代表该基因的得分。如果它是因素，它应该歧视有趣的基因的基因，其余
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)
TODO：这将是一个好主意，以取代与ExpressionSet类allGenes和allScore。这样，我们可以使用诸如全球测试，globalAncova测试.... - 所有的变量开始。只是内部类的使用（私人）
类的对象----------Objects from the Class----------
Objects can be created by calls of the form new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...). ~~ describe objects here ~~
创建对象可以通过检测的形式new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...)。 ~~在这里描述的对象~
插槽----------Slots----------
description: Object of class "character" ~~
description：Object类的"character"~~
ontology: Object of class "character" ~~
ontology：Object类的"character"~~
allGenes: Object of class "character" ~~
allGenes：Object类的"character"~~
allScores: Object of class "ANY" ~~
allScores：Object类的"ANY"~~
geneSelectionFun: Object of class "function" ~~
geneSelectionFun：Object类的"function"~~
feasible: Object of class "logical" ~~
feasible：Object类的"logical"~~
nodeSize: Object of class "integer" ~~
nodeSize：Object类的"integer"~~
graph: Object of class "graphNEL" ~~
graph：Object类的"graphNEL"~~
expressionMatrix: Object of class "matrix" ~~
expressionMatrix：Object类的"matrix"~~
phenotype: Object of class "factor" ~~
phenotype：Object类的"factor"~~
方法----------Methods----------
allGenes signature(object = "topGOdata"): ...
allGenessignature(object = "topGOdata")...
attrInTerm signature(object = "topGOdata", attr = "character", whichGO = "character"): ...
attrInTermsignature(object = "topGOdata", attr = "character", whichGO = "character")...
attrInTerm signature(object = "topGOdata", attr = "character", whichGO = "missing"): ...
attrInTermsignature(object = "topGOdata", attr = "character", whichGO = "missing")...
countGenesInTerm signature(object = "topGOdata", whichGO = "character"): ...
countGenesInTermsignature(object = "topGOdata", whichGO = "character")...
countGenesInTerm signature(object = "topGOdata", whichGO = "missing"): ...
countGenesInTermsignature(object = "topGOdata", whichGO = "missing")...
description<- signature(object = "topGOdata"): ...
说明< - signature(object = "topGOdata")：...
description signature(object = "topGOdata"): ...
描述signature(object = "topGOdata")：...
feasible<- signature(object = "topGOdata"): ...
可行< - signature(object = "topGOdata")...
feasible signature(object = "topGOdata"): ...
可行的signature(object = "topGOdata")：...
geneScore signature(object = "topGOdata"): ...
geneScoresignature(object = "topGOdata")...
geneSelectionFun<- signature(object = "topGOdata"): ...
geneSelectionFun < - signature(object = "topGOdata")...
geneSelectionFun signature(object = "topGOdata"): ...
geneSelectionFunsignature(object = "topGOdata")...
genes signature(object = "topGOdata"): A method for obtaining the list of genes, as a characther vector, which will be
基因signature(object = "topGOdata")：一个基因的名单，获得作为characther矢量的方法，这将是
numGenes signature(object = "topGOdata"): A method for obtaining the number of genes, which will be used in the further
numGenessignature(object = "topGOdata")：一个基因的数量，这将进一步获得方法
sigGenes signature(object = "topGOdata"): A method for
sigGenessignature(object = "topGOdata")方法：
genesInTerm signature(object = "topGOdata", whichGO = "character"): ...
genesInTermsignature(object = "topGOdata", whichGO = "character")...
genesInTerm signature(object = "topGOdata", whichGO = "missing"): ...
genesInTermsignature(object = "topGOdata", whichGO = "missing")...
getSigGroups signature(object = "topGOdata", test.stat = "classicCount"): ...
getSigGroupssignature(object = "topGOdata", test.stat = "classicCount")...
getSigGroups signature(object = "topGOdata", test.stat = "classicScore"): ...
getSigGroupssignature(object = "topGOdata", test.stat = "classicScore")...
graph<- signature(object = "topGOdata"): ...
图< - signature(object = "topGOdata")...
graph signature(object = "topGOdata"): ...
图signature(object = "topGOdata")：...
initialize signature(.Object = "topGOdata"): ...
初始化signature(.Object = "topGOdata")...
ontology<- signature(object = "topGOdata"): ...
本体论< - signature(object = "topGOdata")...
ontology signature(object = "topGOdata"): ...
本体signature(object = "topGOdata")：...
termStat signature(object = "topGOdata", whichGO = "character"): ...
termStatsignature(object = "topGOdata", whichGO = "character")...
termStat signature(object = "topGOdata", whichGO = "missing"): ...
termStatsignature(object = "topGOdata", whichGO = "missing")...
updateGenes signature(object = "topGOdata", geneList = "numeric", geneSelFun = "function"): ...
updateGenessignature(object = "topGOdata", geneList = "numeric", geneSelFun = "function")...
updateGenes signature(object = "topGOdata", geneList = "factor", geneSelFun = "missing"): ...
updateGenessignature(object = "topGOdata", geneList = "factor", geneSelFun = "missing")...
updateTerm<- signature(object = "topGOdata", attr = "character"): ...
updateTerm < - signature(object = "topGOdata", attr = "character")...
usedGO signature(object = "topGOdata"): ...
usedGOsignature(object = "topGOdata")...
作者（S）----------Author(s)----------
Adrian Alexa
参见----------See Also----------
buildLevels, annFUN
buildLevels，annFUN
举例----------Examples----------
## load the dataset [＃加载数据集]
data(geneList)
library(package = affyLib, character.only = TRUE)
## the distribution of the adjusted p-values[＃调整p值分布]
hist(geneList, 100)
## how many differentially expressed genes are:[＃如何差异表达的基因有很多：]
sum(topDiffGenes(geneList))
## build the topGOdata class [＃建立topGOdata类。]
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[＃显示GOdata对象。]
GOdata
##########################################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃＃＃＃＃＃]
## Examples on how to use the methods[＃例如如何使用的方法]
##########################################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃＃＃＃＃＃]
## description of the experiment[＃描述的实验]
description(GOdata)
## obtain the genes that will be used in the analysis[＃获取将在分析中使用的基因]
a <- genes(GOdata)
str(a)
numGenes(GOdata)
## obtain the score (p-value) of the genes[＃获取基因的得分（p值）]
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[＃如果我们想要一位不愿透露姓名的向量，包含了所有可行的基因]
gs <- geneScore(GOdata, use.names = FALSE)
str(gs)
## the list of significant genes[＃显著的基因列表]
sg <- sigGenes(GOdata)
str(sg)
numSigGenes(GOdata)
## to update the gene list [＃更新的基因列表]
.geneList <- geneScore(GOdata, use.names = TRUE)
GOdata ## more available genes[＃提供更多的基因]
GOdata <- updateGenes(GOdata, .geneList, topDiffGenes)
GOdata ## the available genes are now the feasible genes[＃可用的基因是可行的基因]
## the available GO terms (all the nodes in the graph)[＃可用的GO术语（图中的所有节点）]
go <- usedGO(GOdata)
length(go)
## to list the genes annotated to a set of specified GO terms[＃列出一组指定的GO术语注释的基因]
sel.terms <- sample(go, 10)
ann.genes <- genesInTerm(GOdata, sel.terms)
str(ann.genes)
## the score for these genes[＃这些基因的得分]
ann.score <- scoresInTerm(GOdata, sel.terms)
str(ann.score)
## to see the number of annotated genes[＃注释基因的数量]
num.ann.genes <- countGenesInTerm(GOdata)
str(num.ann.genes)
## to summarise the statistics[＃总结统计]
termStat(GOdata, sel.terms)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 15:44:43
