networkConcepts(WGCNA)
networkConcepts()所属R语言包：WGCNA
Calculations of network concepts
计算的网络概念
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This functions calculates various network concepts (topological properties, network indices) of a network calculated from expression data. See details for a detailed description.
此函数计算网络的各种网络概念（拓扑性质，网络指数）从表达数据的计算。查看详细资料进行了详细的描述。
用法----------Usage----------
networkConcepts(datExpr, power = 1, trait = NULL, networkType = "unsigned")
参数----------Arguments----------
参数：datExpr
a data frame containg the expression data, with rows corresponding to samples and columns to genes (nodes).
一个数据框containg的表达数据，相对应的基因（节点）的示例和列与行。
参数：power
soft thresholding power.
软阈值功率。
参数：trait
optional specification of a sample trait. A vector of length equal the number of samples in datExpr.
可选规范的样品的性状。甲向量，长度等于中的样本数datExpr。
参数：networkType
network type. Recognized values are (unique abbreviations of) "unsigned", "signed", and "signed hybrid".
网络类型。可识别的值是（唯一的缩写）"unsigned"，"signed"和"signed hybrid"。
Details
详细信息----------Details----------
This function computes various network concepts (also known as network statistics, topological properties, or network indices) for a weighted correlation network. The nodes of the weighted correlation network will be constructed between the columns (interpreted as nodes) of the input datExpr. If the option networkType="unsigned" then the adjacency between nodes i and j is defined as A[i,j]=abs(cor(datExpr[,i],datExpr[,j]))^power. In the following, we use the term gene and node interchangeably since these methods were originally developed for gene networks. The function computes the following 4 types of network concepts (introduced in Horvath and Dong 2008):
该函数计算的加权相关网络中的各种网络概念（也被称为网络统计信息，拓扑性质，或网络指数）。将构造的加权相关网络的节点之间的列（解释为节点）的输入datExpr。如果选项networkType="unsigned"然后之间的邻接节点i和j被定义为A[i,j]=abs(cor(datExpr[,i],datExpr[,j]))^power。在下文中，我们使用的术语基因和节点互换，因为这些方法最初开发用于基因网络。该函数计算以下4种类型的网络概念（霍瓦特2008年，董介绍）：
Type I: fundamental network concepts are defined as a function of the off-diagonal elements of an adjacency matrix A and/or a node significance measure GS. These network concepts can be defined for any network (not just correlation networks). The adjacency matrix of an unsigned weighted correlation network is given by A=abs(cor(datExpr,use="p"))^power and the trait based gene significance measure is given by GS= abs(cor(datExpr,trait, use="p"))^power where datExpr, trait, power are input parameters.
类型I：基本的网络概念被定义为一个函数的非对角（off-diagonal）的元素的邻接矩阵A和/或节点的意义度量GS。这些网络概念可以被定义为任何网络（不仅仅是相关的网络）。一个无符号的加权关联网络的邻接矩阵A=abs(cor(datExpr,use="p"))^power和基于性状的基因显着性措施的GS= abs(cor(datExpr,trait, use="p"))^powerdatExpr，trait，power是输入参数。
Type II: conformity-based network concepts are functions of the off-diagonal elements of the conformity based adjacency matrix A.CF=CF*t(CF) and/or the node significance measure. These network concepts are defined for any network for which a conformity vector can be defined. Details: For any adjacency matrix A, the conformity vector CF is calculated by requiring that A[i,j] is approximately equal to CF[i]*CF[j]. Using the conformity one can define the matrix A.CF=CF*t(CF) which is the outer product of the conformity vector with itself. In general, A.CF is not an adjacency matrix since its diagonal elements are different from 1. If the off-diagonal elements of A.CF are similar to those of A according to the Frobenius matrix norm, then A is approximately factorizable. To measure the factorizability of a network, one can calculate the Factorizability, which is a number between 0 and 1 (Dong and Horvath 2007). T he conformity is defined using a monotonic, iterative algorithm that maximizes the factorizability measure.
类型II：一致性为基础的网络的概念的非对角（off-diagonal）的一致性为基础的邻接矩阵的元素的功能A.CF=CF*t(CF)和/或节点的意义度量。这些网络的概念被定义为任何网络合格矢量可以被定义。详细说明：对于任何邻接矩阵A，符合向量CF计算的要求，A[i,j]是约等于CF[i]*CF[j]的。使用符合1可以定义矩阵A.CF=CF*t(CF)是与自身的符合性矢量的外积。在一般情况下，A.CF是不是一个邻接矩阵1，因为其对角线上的元素是不同的。如果元素的非对角（off-diagonal）A.CFA根据的Frobenius矩阵范数，然后A是约可因子分解的那些类似。要测量一个网络的factorizability，人们可以计算出的Factorizability，它是0和1之间的一个数（董和Horvath的2007）。他符合定义的单调，迭代算法，最大限度地提高factorizability措施。
Type III: approximate conformity based network concepts are functions of all elements of the conformity based adjacency matrix A.CF (including the diagonal) and/or the node significance measure GS. These network concepts are very useful for deriving relationships between network concepts in networks that are approximately factorizable.
III型：，近似符合基于网络的概念是功能的所有元素的整合为基础的邻接矩阵A.CF（包括对角线）和/或节点意义的措施GS。这些网络概念的网络，这是约因子分解所产生的网络概念之间的关系是非常有用的。
Type IV: eigengene-based (also known as eigennode-based) network concepts are functions of the eigengene-based adjacency matrix A.E=ConformityE*t(ConformityE) (diagonal included) and/or the corresponding eigengene-based gene significance measure GSE. These network concepts can only be defined for correlation networks. Details: The columns (nodes) of datExpr can be summarized with the first principal component, which is referred to as Eigengene in coexpression network analysis. In general correlation networks, it is called eigennode. The eigengene-based conformity ConformityE[i] is defined as abs(cor(datE[,i], Eigengene))^power where the power corresponds to the power used for defining the weighted adjacency matrix A. The eigengene-based conformity can also be used to define an eigengene-based adjacency matrix A.E=ConformityE*t(ConformityE). The eigengene based factorizability EF(datE) is a number between 0 and 1 that measures how well A.E approximates A when the power parameter equals 1. EF(datE) is defined with respect to the singular values of datExpr. For a trait based node significance measure GS=abs(cor(datE,trait))^power, one can also define an eigengene-based node significance measure GSE[i]=ConformityE[i]*EigengeneSignificance where the eigengene significance abs(cor(Eigengene,trait))^power is defined as power of the absolute value of the correlation between eigengene and trait. Eigengene-based network concepts are very useful for providing a geometric interpretation of network concepts and for deriving relationships between network concepts. For example, the hub gene significance measure and its eigengene-based analog have been used to characterize networks where highly connected hub genes are important with regard to a trait based gene significance measure (Horvath and Dong 2008).
IV型：eigengene基于（也称为作为eigennode基于）网络概念是职能的eigengene基于邻接矩阵A.E=ConformityE*t(ConformityE)（对角线包括）和/或相应的eigengene为基础的基因意义度量GSE 。这些网络的概念只能被定义为相关网络。详情：datExpr的列（节点）中的与所述第一主成分，其被称为作为Eigengene在共表达网络分析可以概括。在一般相关网络中，它被称为eigennode。 eigengene基于符合ConformityE[i]被定义为abs(cor(datE[,i], Eigengene))^power的电源对应的电源，用于限定的加权邻接矩阵A。也可用于eigengene基于符合定义基于eigengene的邻接矩阵A.E=ConformityE*t(ConformityE)。该eigengene的基于factorizability EF(datE)是一个数字0和1之间，测量有多好A.E接近A当电源参数等于1。 EF(datE)被定义相对于datExpr的奇异值。对于基于性状的节点意义度量GS=abs(cor(datE,trait))^power，1也可以定义一个eigengene基于节点意义度量GSE[i]=ConformityE[i]*EigengeneSignificanceeigengene意义abs(cor(Eigengene,trait))^power之间的绝对值的相关性定义为功率eigengene和特质。 Eigengene基于网络的概念提供了一个几何解释的网络概念和导出网络概念之间的关系，是非常有用的。例如，集线器基因显着性措施，并已被用来其eigengene基于模拟网络高度连接的枢纽基因关于一个基于性状的基因的意义度量（2008年霍瓦特和董）的重要特征。
值----------Value----------
A list with the following components:
以下组件列表：
参数：Summary
a data frame whose rows report network concepts that only depend on the adjacency matrix. Density (mean adjacency), Centralization , Heterogeneity (coefficient of variation of the connectivity), Mean ClusterCoef, Mean Connectivity. The columns of the data frame report the 4 types of network concepts mentioned in the description: Fundamental concepts, eigengene-based concepts, conformity-based concepts, and approximate conformity-based concepts.
一个数据框的行报告网络的邻接矩阵的概念，只是依赖。密度（平均邻接），集权化，异质性（变异系数的连通性），平均ClusterCoef“，平均连接。列的数据框报告的网络概念的描述中提到的4种类型：基本概念，eigengene为基础的概念，整合为基础的概念，和近似一致为基础的概念。
参数：Size
reports the network size, i.e. the number of nodes, which equals the number of columns of the input data frame datExpr.
报告与网络的规模，即节点，它等于输入数据框datExpr的数目的列的数目。
参数：Factorizability
a number between 0 and 1. The closer it is to 1, the better the off-diagonal elements of the conformity based network A.CF approximate those of A (according to the Frobenius norm).
0和1之间的一个数。它是越接近1，更好的一致性为基础的网络的非对角（off-diagonal）的元素A.CF近似那些A（根据Frobenius范数）。
参数：Eigengene
the first principal component of the standardized columns of datExpr. The number of components of this vector equals the number of rows of datExpr.
第一主成分的标准列datExpr。将此向量的组件的数目等于datExpr的数量的行。
参数：VarExplained
the proportion of variance explained by the first principal component (the Eigengene). It is numerically different from the eigengene based factorizability. While VarExplained is based on the squares of the singular values of datExpr, the eigengene-based factorizability is based on fourth powers of the singular values.
解释方差的比例由第一主成分（Eigengene）。它是数值不同于基于的eigengene的factorizability。虽然VarExplained的基础上的奇异值的平方datExpr，eigengene基于factorizability是根据在第四权力的奇异值。
参数：Conformity
numerical vector giving the conformity. The number of components of the conformity vector equals the number of columns in datExpr. The conformity is often highly correlated with the vector of node connectivities. The conformity is computed using an iterative algorithm for maximizing the factorizability measure. The algorithm and related network concepts are described in Dong and Horvath 2007.
数值矢量的整合。的整合向量的组件的数目等于在datExpr的列数。符合往往是高度相关的节点的连通性向量。计算的整合使用迭代算法最大化factorizability措施，。在2007年东和霍瓦特的算法和相关的网络概念。
参数：ClusterCoef
a numerical vector that reports the cluster coefficient for each node. This fundamental network concept measures the cliquishness of each node.
一个数值向量，报告的每个节点的聚类系数。这个基本的网络概念测量每个节点的cliquishness。
参数：Connectivity
a numerical vector that reports the connectivity (also known as degree) of each node. This fundamental network concept is also known as whole network connectivity. One can also define the scaled connectivity K=Connectivity/max(Connectivity) which is used for computing the hub gene significance.
报告每个节点的连接（也称为度）的数值矢量。这个基本的网络概念也被称为整个网络的连接。人们还可以定义缩放连通K=Connectivity/max(Connectivity)，其用于计算轮毂基因意义。
参数：MAR
a numerical vector that reports the maximum adjacency ratio for each node. MAR[i] equals 1 if all non-zero adjacencies between node i and the remaining network nodes equal 1. This fundamental network concept is always 1 for nodes of an unweighted network. This is a useful measure for weighted networks since it allows one to determine whether a node has high connectivity because of many weak connections (small MAR) or because of strong (but few) connections (high MAR), see Horvath and Dong 2008.
一个数值向量，报告的每个节点的最大邻接比。 MAR[i]等于1，如果所有非零的邻接节点i和剩余的网络节点等于1之间。这个基本的网络概念始终是不加权的网络节点。这是一个有用的指标，加权网络，因为它允许一个确定节点是否具有较高的连接，因为许多弱连接（小MAR），或因为强大的（但很少）连接（MAR），2008年霍瓦特和董。
参数：ConformityE
a numerical vector that reports the eigengene based (aka eigenenode based) conformity for the correlation network. The number of components equals the number of columns of datExpr.
一个数值向量，报告的eigengene的基础（又名eigenenode为主）符合相关网络。组件的数目等于数列datExpr。
参数：GS
a numerical vector that encodes the node (gene) significance. The i-th component equals the node significance of the i-th column of datExpr if a sample trait was supplied to the function (input trait). GS[i]=abs(cor(datE[,i], trait, use="p"))^power
的数值的矢量编码的节点（基因）的意义。的第i个分量等于节点的第i列中的意义datExpr如果样品的性状提供的功能（输入性状）。 GS[i]=abs(cor(datE[,i], trait, use="p"))^power
参数：GSE
a numerical vector that reports the eigengene based gene significance measure. Its i-th component is given by GSE[i]=ConformityE[i]*EigengeneSignificance where the eigengene significance abs(cor(Eigengene,trait))^power is defined as power of the absolute value of the correlation between eigengene and trait.
一个数值向量为基础的基因报告的eigengene的意义度量。它的第i个分量由下式给出GSE[i]=ConformityE[i]*EigengeneSignificance其中eigengene意义abs(cor(Eigengene,trait))^power被定义为功率的绝对值之间的相关性eigengene和特质。
参数：Significance
a data frame whose rows report network concepts that also depend on the trait based node significance measure. The rows correspond to network concepts and the columns correspond to the type of network concept (fundamental versus eigengene based). The first row of the data frame reports the network significance. The fundamental version of this network concepts is the average gene significance=mean(GS). The eigengene based analog of this concept is defined as mean(GSE). The second row reports the hub gene significance which is defined as slope of the intercept only regression model that regresses the gene significance on the scaled network connectivity K. The third row reports the eigengene significance abs(cor(Eigengene,trait))^power. More details can be found in Horvath and Dong (2008).
一个数据框的行报告网络的概念，也依赖于基于性状的节点意义的措施。的行相对应的网络概念和列对应于网络的概念（基本与eigengene的基础）的类型。的数据框中的第一行报告网络意义。这个网络的概念是最根本的版本=平均（GS）的的平均基因的意义。 （GSE）这一概念被定义为基于模拟的eigengene。的第二行报告倒退的基因意义上的缩放网络连接K.第三的行报告eigengene的的意义abs(cor(Eigengene,trait))^power仅截距回归模型的斜率被定义为的轮毂基因意义。更多的细节可以发现，在霍瓦特和东（2008）。
（作者）----------Author(s)----------
Jun Dong, Steve Horvath, Peter Langfelder
参考文献----------References----------
Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17
4(8): e1000117
参见----------See Also----------
conformityBasedNetworkConcepts for approximate conformity-based network concepts
conformityBasedNetworkConcepts的近似符合基于网络概念
fundamentalNetworkConcepts for calculation of fundamental network concepts only.
fundamentalNetworkConcepts只计算基本的网络概念。
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 2012-10-1 21:18:51
