ListHyperGParams-class(eisa)
ListHyperGParams-class()所属R语言包：eisa
Classes for quick GO/KEGG/CHR/miRNA target or other enrichment
为快速，GO / KEGG /人权/ miRNA靶或其他浓缩的类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These classes extend the HyperGParams class from the Category package to perform enrichment calculation quickly for multiple gene sets.
这些类扩展HyperGParams类Category包快速进行多个基因组富集计算。
用法----------Usage----------
## S4 method for signature 'ListHyperGParams'
makeValidParams(object)
## S4 method for signature 'ListHyperGParams'
drive(p)
## S4 replacement method for signature 'ListHyperGParams,logical'
drive(p) <- dri
## S4 method for signature 'GOListHyperGParams'
ontology(object)
## S4 replacement method for signature 'GOListHyperGParams,character'
ontology(object) <- go
## S4 method for signature 'GOListHyperGParams'
conditional(r)
## S4 replacement method for signature 'GOListHyperGParams,logical'
conditional(r) <- cond
## S4 method for signature 'ListHyperGParams'
hyperGTest(p)
参数----------Arguments----------
参数：object,p,r
A ListHyperGParams object.
一个ListHyperGParams对象。
参数：dri
Logical scalar, whether to store the genes that are in the intersection of the specified gene set and the annotation category.
逻辑标量，是否存储在指定的基因组和注释类的交集的基因。
参数：go
Character scalar, the ontology for GO, possible values: "BP", "CC", "MF".
字符标量，对GO本体，可能的值：“BP”，“CC”，“中频”。
参数：cond
Logical scalar, whether to perform conditional enrichment calculation. Currently this option is ignored.
逻辑标量，是否执行条件的富集计算。目前，这个选项被忽略。
Details
详情----------Details----------
The ListHyperGParams abstract class extends HyperGParams and allows to specify a list of gene sets for the enrichment calculation instead of a single set.
ListHyperGParams抽象类扩展HyperGParams，并允许指定一个基因组丰富，而不是一个单一的一套计算列表。
ListHyperGParams calculates the enrichment much faster than the original HyperGParams classes in the Category package, especially if the calculation is performed against the same gene universe for many gene sets.
ListHyperGParams计算速度远远超过原HyperGParams类Category包，尤其是如果计算对许多基因组相同的基因宇宙进行的浓缩铀。
ListHyperGParams is an abstract class, it is not possible to instantiate objects from it. Instead, its various extensions must be used: GOListHyperGParams, KEGGListHyperGParams, CHRListHyperGParams and miRNAListHyperGParams.
ListHyperGParams是一个抽象类，它是不可能的实例对象。相反，它的各种扩展必须用于：GOListHyperGParams，KEGGListHyperGParams，CHRListHyperGParams和miRNAListHyperGParams。
The various ListHyperGParams objects can be created with the standard new command, by giving all necessary arguments. Please see the examples below.
各种ListHyperGParams对象，可以创建标准的new命令，给予一切必要的参数。请参阅下面的例子。
值----------Value----------
makeValidParmas returns another ListHyperGParams instance that has the same class as its arguments'.
makeValidParmas返回另一个ListHyperGParams作为它的参数有相同的类的实例。
ontology returns a character vector of length one.
ontology返回一个长度为1的特征向量。
conditional returns a logical vector of length one.
conditional返回一个长度为1的逻辑向量。
drive returns a logical vector of length one.
drive返回一个长度为1的逻辑向量。
成员函数----------Member functions----------
Most of these functions are analogous to the ones defined in the Category package, the only difference is that they handle ListHyperGParams objects.
大部分这些功能是类似于Category包中定义的，唯一的区别是，他们处理ListHyperGParams对象。
makeValidParams validates ListHyperGParams object, in particular, it removes duplicate genes, both from the gene universe and the specified gene sets; and it also makes sure that all genes in the gene sets are included in the universe.
makeValidParams验证ListHyperGParams对象，尤其是，它消除重复的基因，从基因宇宙和指定的基因组，这也使得确保在基因组的所有基因都包含在宇宙中。
ontology can be used to query or set the ontology for enrichment calculated against the GO database.
ontology可用于查询或设置对GO数据库计算的富集本体。
conditional queries or sets whether conditional GO enrichment will be performed. This feature is not implemented yet, see the Category and GOstats packages for a working implementation and more information.
conditional查询，或是否将执行条件好富集套。此功能尚未实现，看到Category和GOstats工作的实施和更多信息包。
drive queries or sets whether the intersections of the gene sets and the universe are stored in the result object. This information can be calculated later as well, but it is faster to store it at the same time when the hypergeometric test is performed.
drive查询或套是否存储在结果对象的基因组和宇宙的交点。此信息以及稍后可以计算，但它是更快，存储超几何执行测试时，它在同一时间。
作者（S）----------Author(s)----------
Gabor Csardi <a href="mailto:Gabor.Csardi@unil.ch">Gabor.Csardi@unil.ch</a>
参见----------See Also----------
Functions for enrichment calculation of ISA modules: ISAGO, ISAKEGG, ISACHR, ISAmiRNA.
ISA的富集计算功能模块：ISAGO，ISAKEGG，ISACHR，ISAmiRNA。
Perhaps see also the vignette in the GOstats package.
或许也看到GOstats包中的小插曲。
举例----------Examples----------
# GO enrichment, "by hand"[去充实，“牵手”]
# Load data first[先加载数据]
data(ALLModulesSmall)
# Create gene sets[创建基因组]
library(hgu95av2.db)
genes <- getFeatureNames(ALLModulesSmall)
entrez <- lapply(genes, function(x) na.omit(unlist(mget(x,
hgu95av2ENTREZID))))
# Create universe[创造宇宙]
universe <- na.omit(unlist(mget(featureNames(ALLModulesSmall),
hgu95av2ENTREZID)))
# Create parameter object[创建参数对象]
param <- new("GOListHyperGParams", geneIds=entrez, universeGeneIds=universe,
pvalueCutoff=0.01, drive=FALSE, ontology="BP",
conditional=FALSE, testDirection="over",
annotation=annotation(ALLModulesSmall))
# Do the calculation[做计算]
GOBP <- hyperGTest(param)
# Inspect the result[检查结果]
GOBP
summary(GOBP)[[1]]
# How to create other parameter objects[如何创建其他参数对象]
paramKEGG <- new("KEGGListHyperGParams", geneIds=entrez,
universeGeneIds=universe, drive=FALSE,
annotation=annotation(ALLModulesSmall))
paramCHR <- new("CHRListHyperGParams", geneIds=entrez,
universeGeneIds=universe, drive=FALSE,
annotation=annotation(ALLModulesSmall))
# Enrichment with user-supplied categories, we use a list of[富集与用户提供的类别，我们使用一个列表]
# hand-picked genes that are involved in myelin formation[钦点，在髓鞘形成有关的基因]
mygenes <- c("YARS", "NFKB2", "NGFR", "CDH1", "NFAT5", "NDRG1", "GAP43",
"EGR2", "MSN", "ROCK1", "SREBF2", "SOX10", "FIG4", "EGR1", "PIK3R1",
"CDC42", "EDN3", "EDNRB", "NCAM1", "DHH", "OMG", "PMP22", "LAMA4",
"MPDZ", "MTMR2", "REL", "S100A1", "ITGA4", "GFAP", "FGF2", "RPSA",
"CADM1", "CDH19", "DNM2", "PAX3", "SREBF1", "DAG1", "DRP2", "SDC2",
"MBP", "RELA", "RELB", "JUN", "NAB1", "MOBP", "SKI", "COL5A2", "RHOA",
"NFASC", "NEFL", "MPZ", "MAG", "EDNRA", "ERBB4", "LITAF", "MMP2",
"PLP1", "CDKN1A", "PAK1", "RDX", "GJB1", "LAMA5", "JAM3", "ITGB1",
"PARD3", "FABP7", "LAMA2", "ERBB3", "CADM4", "FOXO4", "TSPAN31",
"GPR126", "PTK2", "RAC1", "CDKN2A", "CLDN5", "ID2", "LAMC1", "SOX2",
"CNTN2", "ERBB2", "NFKB1", "NAB2", "EDN2", "MMP9", "CCND1", "L1CAM",
"MOG")
library(org.Hs.eg.db)
myentrez <- na.omit(unlist(mget(mygenes, revmap(org.Hs.egSYMBOL))))
categories <- list(myelin=myentrez)
data(ALLModules)
genes2 <- getFeatureNames(ALLModules)
entrez2 <- lapply(genes2, function(x) na.omit(unlist(mget(x,
hgu95av2ENTREZID))))
# Create universe[创造宇宙]
universe2 <- na.omit(unlist(mget(featureNames(ALLModules),
hgu95av2ENTREZID)))
paramMY <- new("GeneralListHyperGParams", geneIds=entrez2,
universeGeneIds=universe2, drive=FALSE,
annotation=annotation(ALLModulesSmall),
categories=categories)
MY <- hyperGTest(paramMY)
MY
summary(MY)[[1]]
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 17:15:00
