conformityBasedNetworkConcepts(WGCNA)
conformityBasedNetworkConcepts()所属R语言包：WGCNA
Calculation of conformity-based network concepts.
计算合格的网络概念。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes 3 types of network concepts (also known as network indices or statistics) based on an adjacency matrix and optionally a node significance measure.
该函数计算3种类型的网络概念（也被称为网络指数或统计数据）的基础上的邻接矩阵和任选的节点的意义度量。
用法----------Usage----------
conformityBasedNetworkConcepts(adj, GS = NULL)
参数----------Arguments----------
参数：adj
adjacency matrix. A symmetric matrix with components between 0 and 1.
邻接矩阵。与在0和1之间的组件的对称矩阵。
参数：GS
optional node significance measure. A vector with length equal the dimension of adj.
可选的节点意义的措施。一个向量，其长度相等的尺寸adj。
Details
详细信息----------Details----------
This function computes 3 types of network concepts (also known as network indices or statistics) based on an adjacency matrix and optionally a node significance measure. Specifically, it computes I) fundamental network concepts, II) conformity based network concepts, and III) approximate conformity based network concepts. These network concepts are defined for any symmetric adjacency matrix (weighted and unweighted). The network concepts are described in Dong and Horvath (2007) and Horvath and Dong (2008). In the following, we use the term gene and node interchangeably since these methods were originally developed for gene networks. In the following, we briefly describe the 3 types of network concepts:
该函数计算3种类型的网络概念（也被称为网络指数或统计数据）的基础上的邻接矩阵和任选的节点的意义度量。具体而言，它计算I）基本的网络概念，II）整合的网络概念，III）近似符合基于网络的概念。这些网络的概念被定义为任何对称邻接矩阵（加权和不加权）。董和霍瓦特（2007）和霍瓦特和董（2008年）中描述的网络概念。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。在下面，我们简要介绍了3种类型的网络概念：
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. 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). The conformity is defined using a monotonic, iterative algorithm that maximizes the factorizability measure. 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.
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。 II型：符合基于网络的概念是功能的非对角线元素的合格的邻接矩阵A.CF = CF * T（CF）和/或节点意义的措施。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，符合矢量CF的计算方法是，要求的A [i，j]的是约等于CF [I] * CF [J]。使用符合1可以定义矩阵A.CF = CF *吨（CF），它是与自身的符合性矢量的外积。在一般情况下，A.CF不是一个邻接矩阵，因为它的对角线元素是不同的从1。如果非对角（off-diagonal）的元素的A.CF类似那些根据的Frobenius矩阵范数的A，则A是约可因子分解的。若要测量一个网络的factorizability，人们可以计算出Factorizability，这是0和1之间的一个数（董和Horvath的2007）。符合定义的单调，迭代算法，最大限度地提高factorizability措施。 III型：，近似符合基于网络的概念是功能的所有元素的整合包括对角线邻接矩阵A.CF（）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。
值----------Value----------
A list with the following components:
以下组件列表：
参数：Factorizability
number between 0 and 1 giving the factorizability of the matrix. The closer to 1 the higher the evidence of factorizability, that is, A-I is close to outer(CF,CF)-diag(CF^2).
0和1之间的数字给factorizability的矩阵。越接近1越高的factorizability证据，即，AI是靠近外（CF，CF）诊断（CF ^ 2）。
参数：fundamentalNCs
fundamental network concepts, that is network concepts calculated directly from the given adjacency matrix adj. A list with components ScaledConnectivity (giving the scaled connectivity of each node), Connectivity (connectivity of each node), ClusterCoef (the clustering coefficient of each node), MAR (maximum adjacency ratio of each node), Density (the mean density of the network), Centralization (the centralization of the network), Heterogeneity (the heterogeneity of the network). If the input node significance GS is specified, the following additional components are included: NetworkSignificance (network significance, the mean node significance), and HubNodeSignificance (hub node significance given by the linear regression of node significance on connectivity).
基本的网络概念，也就是直接计算从给定的邻接矩阵adj的网络概念。列表组件ScaledConnectivity（比例的每个节点的连接），Connectivity（每个节点的连接），ClusterCoef（每个节点的聚类系数），MAR （每个节点的最大邻接比），Density（平均密度的网络），Centralization（集中的网络），Heterogeneity（网络）的异质性。如果指定的输入节点的意义GS，以下的附加组件包括：NetworkSignificance（网络意义，平均节点意义），和HubNodeSignificance（集线器节点给出的含义由线性回归的节点意义上的连接）。
参数：conformityBasedNCs
network concepts based on an approximate adjacency matrix given by the outer product of the conformity vector but with unit diagonal. A list with components Conformity (the conformity vector) and Connectivity.CF, ClusterCoef.CF, MAR.CF, Density.CF, Centralization.CF, Heterogeneity.CF giving the conformity-based analogs of the above network concepts.
网络概念的基础上给出的符合性矢量的外积，但单元对角线近似邻接矩阵。列表组件Conformity（合格矢量）和Connectivity.CF, ClusterCoef.CF, MAR.CF, Density.CF, Centralization.CF, Heterogeneity.CF给合格的类似上述的网络概念。
参数：approximateConformityBasedNCs
network concepts based on an approximate adjacency matrix given by the outer product of the conformity vector. A list with components Conformity (the conformity vector) and Connectivity.CF.App, ClusterCoef.CF.App, MAR.CF.App, Density.CF.App, Centralization.CF.App, Heterogeneity.CF.App giving the conformity-based analogs of the above network concepts.
网络概念的基础上给出的一个近似的邻接矩阵的符合性矢量的外积。列表组件Conformity（合格矢量）和Connectivity.CF.App, ClusterCoef.CF.App, MAR.CF.App, Density.CF.App, Centralization.CF.App, Heterogeneity.CF.App给合格的类似上述的网络概念。
（作者）----------Author(s)----------
Steve Horvath
参考文献----------References----------
Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): e1000117
参见----------See Also----------
networkConcepts for calculation of eigennode based network concepts for a correlation network;
networkConcepts计算eigennode基于网络概念的相关网络;
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。
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 2012-10-1 21:09:48
