GSCA-class(HTSanalyzeR)
GSCA-class()所属R语言包：HTSanalyzeR
An S4 class for Gene Set Collection Analyses on high-throughput screens
S4类基因组收集分析，高通量筛选
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This S4 class includes a series of methods to do gene set enrichment analysis and hypergeometric tests for high-throughput screens.
这S4类包括一系列的方法，高通量筛选，基因组富集分析和超几何试验。
类的对象----------Objects from the Class----------
Objects of class GSCA can be created from new("GSCA", listOfGeneSetCollections, geneList, hits) (see the examples below)
可以创建对象的类GSCA new("GSCA", listOfGeneSetCollections, geneList, hits)（见下面的例子）
插槽----------Slots----------
a list of gene set collections (a 'gene set collection' is a list of gene sets).
一个基因组的集合名单（“基因组的集合”是一个基因组的列表）。
a numeric or integer vector of phenotypes named by gene identifiers.
基因标识符命名的表型的数字或整数向量。
a character vector of the gene identifiers (used as hits in the hypergeometric tests).
基因标识的特征向量（点击超几何测试使用）。
a list of parameters for hypergeometric tests and GSEA. These parameters are pValueCutoff, pAdjustMethod, nPermutations, minGeneSetSize and exponent (see function analyzeGeneSetCollections for detailed descriptions about these parameters).
为超几何测试和GSEA参数列表。这些参数是pValueCutoff，pAdjustMethod，nPermutations，minGeneSetSize和exponent（见函数analyzeGeneSetCollections有关这些参数的详细描述）。
a list of results (see the returned values in the function analyzeGeneSetCollections).
结果列表（见函数返回值analyzeGeneSetCollections）。
a list of summary information for listOfGeneSetCollections, geneList, hits, para, and result.
listOfGeneSetCollections，geneList，hits，para，result的摘要信息列表。
a single logical value specifying whether or not the input data has been preprocessed.
一个逻辑值，指定或不输入数据是否已被预处理。
方法----------Methods----------
An overview of methods with class-specific functionality: More detailed introduction can be found in help for each specific function.
概述类特定功能的方法：更详细的介绍，可以发现在每个特定功能的帮助。
do preprocessing on input vectors of phenotypes and hits including: a) removing NAs in the geneList and hits; b) invoking function duplicateRemover to process duplicated phenotypes (see duplicateRemover for more details); c) invoking function annotationConvertor to convert annotations; d) ranking phenotypes in a decreasing order.
表型和点击输入向量预处理包括：a）删除的NASgeneList和hits; B）调用函数duplicateRemover来处理重复的表型（见duplicateRemover更多细节）; C）调用函数annotationConvertor转换注释; D）排名表型的递减顺序。
perform hypergeometric tests and Gene Set Enrichment Analysis based on input parameter list para.
执行超几何测试和基因组富集分析，根据输入参数列表para。
append gene set terms to GSCA results
追加基因组方面GSCA结果
print summary information about listOfGeneSetCollections, geneList, hits, para, and result.
关于listOfGeneSetCollections，geneList，hits，para，result打印摘要信息。
select top significant gene sets from object@results$`resultName` by setting ntop or allSig.
选择object@results$resultName设置ntop或allSig顶部显着的基因集。
write observed hits in gene sets for hypergeometric tests.
写观察到基因组的超几何测试的次数。
view a figure of GSEA results for a gene set in a gene set collection.
查看GSEA结果为基因组在基因组集合的数字。
plot and save figures of GSEA results for top significant gene sets in a gene set collection.
GSEA结果顶级重要基因的图，并保存数字设置在基因组的集合。
plot an enrichment map for GSEA or hypergeometric test results
富集图绘制GSEA或超几何测试结果
plot and save an enrichment map for GSEA or hypergeometric test results
绘制并保存GSEA或超几何测试结果的富集图
generate html reports.
生成HTML报告。
作者（S）----------Author(s)----------
Xin Wang <a href="mailto:xw264@cam.ac.uk">xw264@cam.ac.uk</a>
参见----------See Also----------
preprocess analyze appendGSTerms summarize getTopGeneSets writeHits viewGSEA plotGSEA viewEnrichMap plotEnrichMap report
preprocessanalyzeappendGSTermssummarizegetTopGeneSetswriteHitsviewGSEAplotGSEAviewEnrichMapplotEnrichMap report
举例----------Examples----------
## Not run: [＃无法运行：]
library(org.Dm.eg.db)
library(KEGG.db)
library(AnnotationDbi)
library(igraph)
##load data for enrichment analyses[＃加载富集分析数据。]
data("KcViab_Data4Enrich")
##select hits[＃选择点击]
hits <- names(KcViab_Data4Enrich)[which(abs(KcViab_Data4Enrich) > 2)]
##set up a list of gene set collections[＃建立一个基因组的集合列表]
PW_KEGG <- KeggGeneSets(species = "Dm")
gscs <- list(PW_KEGG = PW_KEGG)
##create an object of class 'GSCA'[＃创建一个对象类“GSCA]
gsca <- new("GSCA", listOfGeneSetCollections=gscs, geneList =
KcViab_Data4Enrich, hits = hits)
##do preprocessing (KcViab_Data4Enrich has already been preprocessed)[＃做预处理（KcViab_Data4Enrich已经被预处理）]
gsca <- preprocess(gsca, species="Dm", initialIDs = "Entrez.gene",
keepMultipleMappings = TRUE, duplicateRemoverMethod = "max",
orderAbsValue = FALSE)
##do hypergeometric tests and GSEA[＃做超几何测试和GSEA]
gsca <- analyze(gsca, para = list(pValueCutoff = 0.05, pAdjustMethod
= "BH", nPermutations = 1000, minGeneSetSize = 60, exponent = 1))
##print summary information[＃打印摘要信息。]
summarize(gsca)
##get all significant gene sets in "PW_KEGG"[＃“PW_KEGG”的所有重大的基因组]
sigGSs<-getTopGeneSets(gsca, "GSEA.results", "PW_KEGG", allSig=TRUE)
##view a GSEA figure[＃查看GSEA图。]
viewGSEA(gsca, gscName="PW_KEGG", gsName=sigGSs[["PW_KEGG"]][1])
dev.off()
##append gene set terms to results[＃追加基因组方面成果]
gsca<-appendGSTerms(gsca, keggGSCs="PW_KEGG")
##view an enrichment map for GSEA results[＃查看GSEA结果富集图]
eb<-viewEnrichMap(gsca, gscs="PW_KEGG", allSig=TRUE, gsNameType="term",
displayEdgeLabel=FALSE, layout="layout.fruchterman.reingold")
##write html reports[＃写的HTML报告。]
report(object = gsca, experimentName = "GSCATest", species = "Dm",
allSig = TRUE, keggGSCs = "PW_KEGG", reportDir="GSCATestReport")
##browse the index page[＃浏览索引页]
browseURL(file.path(getwd(), "GSCATestReport", "index.html"))
## End(Not run)[＃结束（不运行）]
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。
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发表于 2012-2-25 21:55:30
