qpFunctionalCoherence(qpgraph)
qpFunctionalCoherence()所属R语言包：qpgraph
Functional coherence estimation
功能的一致性估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates functional coherence for a given transcriptional regulatory network specified either as an adjacency matrix with a list of transcription factor gene identifiers or as a list of transcriptional regulatory modules.
估计一个给定的转录调控作为一个转录因子基因标识的列表或邻接矩阵作为一个转录调控模块列表或者指定的网络功能的连贯性。
用法----------Usage----------
## S4 method for signature 'lsCMatrix'
qpFunctionalCoherence(object, TFgenes, geneUniverse=rownames(object),
chip, minRMsize=5, verbose=FALSE, clusterSize=1)
## S4 method for signature 'lspMatrix'
qpFunctionalCoherence(object, TFgenes, geneUniverse=rownames(object),
chip, minRMsize=5, verbose=FALSE, clusterSize=1)
## S4 method for signature 'lsyMatrix'
qpFunctionalCoherence(object, TFgenes, geneUniverse=rownames(object),
chip, minRMsize=5, verbose=FALSE, clusterSize=1)
## S4 method for signature 'matrix'
qpFunctionalCoherence(object, TFgenes, geneUniverse=rownames(object),
chip, minRMsize=5, verbose=FALSE, clusterSize=1)
## S4 method for signature 'list'
qpFunctionalCoherence(object, geneUniverse=unique(c(names(object), unlist(object, use.names=FALSE))),
chip, minRMsize=5, verbose=FALSE, clusterSize=1)
参数----------Arguments----------
参数：object
object containing the transcriptional regulatory modules for which we want to estimate their functional coherence. It can be an adjacency matrix of the undirected graph representing the transcriptional regulatory network or a list of gene target sets where the name of the entry should be the transcription factor identifier.
对象，其中包含我们要估计其功能的连贯性的转录调控模块。它可以是一个代表的转录调控网络或列表列出条目的名称应该是转录因子识别的基因目标的无向图的邻接矩阵。
参数：TFgenes
when the input object is a matrix, it is required to provide a vector of transcription factor gene identifiers (which should match somewhere in the row and column names of the matrix.
当输入的对象是一个矩阵，它需要提供一个向量转录因子基因标识（应匹配的矩阵的行和列名在某处。
参数：geneUniverse
vector of all genes considered in the analysis. By default it equals the rows and column names of object when it is a matrix, or the set of all different gene identifiers occuring in object when it is a list.
分析认为在所有的基因向量。默认情况下它等于行和列名object当它是一个矩阵，或者发生在object时，它是一个列表的所有不同的基因标识的集合。
参数：chip
name of the .db package containing the Gene Ontology (GO) annotations.
名称.db包包含的基因本体（GO）的注释。
参数：minRMsize
minimum size of the target gene set in each regulatory module where functional enrichment will be calculated and thus where functional coherence will be estimated.
设置在每个监管模块功能浓缩将计算和功能的一致性，将估计的靶基因的最小尺寸。
参数：verbose
logical; if TRUE the function will show progress on the calculations; if FALSE the function will remain quiet (default).
逻辑;如果为TRUE，函数将显示在计算上取得进展，如果为FALSE，函数将保持安静（默认）。
参数：clusterSize
size of the cluster of processors to employ if we wish to speed-up the calculations by performing them in parallel. A value of 1 (default) implies a single-processor execution. The use of a cluster of processors requires having previously loaded the packages snow and rlecuyer.
聘请如果我们希望加快执行并行计算处理器聚类的大小。 1（默认）值意味着一个单处理器的执行。要求曾装包snow和rlecuyer使用的处理器的聚类。
Details
详情----------Details----------
This function estimates the functional coherence of a transcriptional regulatory network represented by means of an undirected graph encoded by an adjacency matrix and of a set of transcription factor genes. The functional coherence of a transcriptional regulatory network is calculated as specified by Castelo and Roverato (2009) and corresponds to the distribution of individual functional coherence values of every of the regulatory modules of the network each of them defined as a transcription factor and its set of putatively regulated target genes. In the calculation of the functional coherence value of a regulatory module, Gene Ontology (GO) annotations are employed through the given annotation .db package and the conditional hyper-geometric test implemented in the GOstats package from Bioconductor.
这个功能估计代表一个无向图的邻接矩阵和一组转录因子基因编码一个转录调控网络的功能一致性。转录调控网络的计算功能的一致性为卡斯特洛和Roverato（2009）规定和对应到个人，每一个监管网络模块功能的一致性值的分布，他们每个人作为一个转录因子和其一套定义公认调控的靶基因。在监管模块功能的连贯性价值计算，基因本体（GO）的注释是通过给定的注解.db包和超几何测试条件实施Bioconductor在GOstats包就业。
值----------Value----------
A list with three slots, a first one containing the transcriptional regulatory network as a list of regulatory modules and their targets, a second one containing this same network but including only those modules with GO BP annotations and a third one consisting of a vector of functional coherence values.
与三个插槽的名单中，第一个含有转录调控网络的管理模块和他们的目标的列表，第二个包含此相同的网络，但只有那些好血压注释和功能的向量组成的第三个模块，包括相干值。
作者（S）----------Author(s)----------
R. Castelo and A. Roverato
参考文献----------References----------
networks from microarray data with qp-graphs. J. Comp. Biol., 16(2):213-227, 2009.
参见----------See Also----------
qpAvgNrr qpGraph
qpAvgNrrqpGraph
举例----------Examples----------
library(org.EcK12.eg.db)
# load RegulonDB data from this package[加载这个包RegulonDB数据]
data(EcoliOxygen)
# pick two TFs from the RegulonDB data in this package[从这个包中的RegulonDB数据接2转录因子]
TFgenes <- c("mhpR", "iscR")
# get their Entrez Gene Identifiers[得到他们的Entrez基因标识符]
TFgenesEgIDs <- unlist(mget(TFgenes, AnnotationDbi::revmap(org.EcK12.egSYMBOL)))
# get all genes involved in their regulatory modules from[在涉及其监管的模块从所有基因]
# the RegulonDB data in this package[在此包中RegulonDB数据]
mt <- match(filtered.regulon6.1[,"EgID_TF"], TFgenesEgIDs)
allGenes <- as.character(unique(as.vector(
as.matrix(filtered.regulon6.1[!is.na(mt),
c("EgID_TF","EgID_TG")]))))
mtTF <- match(filtered.regulon6.1[,"EgID_TF"],allGenes)
mtTG <- match(filtered.regulon6.1[,"EgID_TG"],allGenes)
# select the corresponding subset of the RegulonDB data in this package[选择相应的子集，这个包中的RegulonDB数据]
subset.filtered.regulon6.1 <- filtered.regulon6.1[!is.na(mtTF) & !is.na(mtTG),]
TFi <- match(subset.filtered.regulon6.1[,"EgID_TF"], allGenes)
TGi <- match(subset.filtered.regulon6.1[,"EgID_TG"], allGenes)
subset.filtered.regulon6.1 <- cbind(subset.filtered.regulon6.1,
idx_TF=TFi, idx_TG=TGi)
# build an adjacency matrix representing the transcriptional regulatory[代表转录调控，建立邻接矩阵]
# relationships from these regulatory modules[从这些监管模块的关系]
p <- length(allGenes)
adjacencyMatrix <- matrix(FALSE, nrow=p, ncol=p)
rownames(adjacencyMatrix) <- colnames(adjacencyMatrix) <- allGenes
idxTFTG <- as.matrix(subset.filtered.regulon6.1[,c("idx_TF","idx_TG")])
adjacencyMatrix[idxTFTG] <-
adjacencyMatrix[cbind(idxTFTG[,2],idxTFTG[,1])] <- TRUE
# calculate functional coherence on these regulatory modules[这些监管模块计算功能的一致性]
fc <- qpFunctionalCoherence(adjacencyMatrix, TFgenes=TFgenesEgIDs,
chip="org.EcK12.eg.db")
print(sprintf("the %s module has a FC value of %.2f",
mget(names(fc$functionalCoherenceValues),org.EcK12.egSYMBOL),
fc$functionalCoherenceValues))
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 11:39:23
