R语言 ScISI包 maximizeSimilarity()函数中文帮助文档(中英文对照)

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maximizeSimilarity(ScISI)
maximizeSimilarity()所属R语言包：ScISI

                                        A function compares two bipartite graph matrices and finds the most
                                         一个功能比较两个二分图矩阵，并找到最

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function takes a matrix of similarity indices between two bipartite graph matrices and determines, for each complex of the first bipartite graph matrix (bg1), the most similiar complexes of the second bipartite graph matrix (bg2).  
此功能需要两个二分图矩阵，并确定之间的相似性指数矩阵，每个复杂的第一个二分图矩阵（BG1），第二次二分图矩阵（BG2）的最同级的复合物。


用法----------Usage----------


maximizeSimilarity(simMat, bywhich = "ROW", zeroSim = "NO")



参数----------Arguments----------

参数：simMat
A matrix of similarity coefficients between two bipartite graph matrices (bg1 and bg2) where the rows are indexed by the first bipartite graph matrix and the colunms are indexed by the second bipartite graph matrix. The (i,j)th entry is the similarity index between complex i of bg1 and complex j of bg2.
两个二分图矩阵（BG1和BG2），其中索引的第一个二分图矩阵的行和colunms之间的相似性系数矩阵是第二次二分图矩阵索引。 （I，J）日进入BG1和BG2复J复杂我之间的相似性指数。


参数：bywhich
Takes one of these three arguments: "ROW", "COL", "BOTH"
注意到这三个参数之一：“排”，“胶原”，“既”


参数：zeroSim
Takes either one of the following arguments: "NO", "YES"
注意到下列参数中的任何一个：“NO”，“是”


Details

详情----------Details----------

This function's purpose is to take one (or both) bipartite graph matrix, wlog we take bg1, and, for each complex, C-i, of bg1, finds the complex(es) of bg2 that are the most similiar to C-i based on the similarity index. Since the complexes of bg1 is indexed by the rows of simMat argument, finding the complexes of bg2 that are the most similar to C-i means finding the maximal value, m, of row i and then the complexes, K-j, that index the colunm for which m belongs.
此功能的目的来采取1（或两者）二分图矩阵，wlog我们采取BG1，每一个复杂的，词，对BG1，发现在复杂的BG2的最同级以词（ES）的相似性的基础索引。由于BG1的复合物索引的行simMat参数，找到最相似的词是指发现的最大价值，M，i行，然后配合物，KJ，BG2的复合物，该指数的colunm M属于。

If byWhich argument is set to "ROW", the function parses through each complex of bg1 and finds the complex(es) of bg2 which are most similar. If byWhich is set to "COL", the function parses through each complex of bg2 and finds the complex(es) of bg1 which are most similar. If byWhich is set to "BOTH", the function parses through both the complexes of bg1 and bg2. Since this matrix is not symmetric (this matrix is usually not square) this maximizing is different between row and column.
如果byWhich参数设置“列”，功能解析通过每个BG1复杂和发现最相似的，的BG2这是复杂的（ES）。 ，如果byWhich设置“胶原”，功能解析通过每个BG2复杂和发现BG1这是最相似的复杂（ES）。 ，如果byWhich设置“既”，功能解析BG1和BG2同时通过复合物。因为这不是对称矩阵（此矩阵通常是不平方米），这个最大化的行和列之间是不同的。

If zeroSim argument is set to "NO", the only maximal matching occurs if the similarity index is nonzero; e.g. if we want to maximize the match for complex C-i of bg1, but row i is comprised only of 0, C-i will not be matched to any complex of bg2.
如果zeroSim参数设置为“NO”，唯一的最大匹配发生的相似性指数是非零;如如果我们想最大限度地复杂的词BG1的比赛，但行我只有0组成，词将不会被匹配到任何复杂的BG2。


值----------Value----------

The return value is a list consisting of a vector and a list:
返回值是一个向量和一个列表组成的列表：


参数：maximize
A named numeric vector. The name is the complex, C-i, for which the function is trying to find a maximal match. The entries of the vector is the maximal similarity index between C-i and all of the complexes of the other bipartite graph matrix, i.e. the maximal entry row i in simMat.
一个名为数字矢量。这个名字是复杂的词，它的功能正试图找到一个最大匹配。向量的条目是词和所有其他的二分图矩阵的复合物之间的最大相似性指数，即最大输入行，我在simMat。


参数：maxComp
A named list of named vectors. The name is the complex, C-i, for which the function is trying to find a maximal match. The named vector consists of the positions of the maximal matches (either which row or which column) and the names correspond to the conmplex of maximal matching.
一个命名为向量命名名单。这个名字是复杂的词，它的功能正试图找到一个最大匹配。命名的向量组成的最大的比赛（无论是哪一行或哪一列）的位置和名称到最大匹配conmplex的对应。


作者（S）----------Author(s)----------


Tony Chiang



举例----------Examples----------


#go = getGOInfo(wantAllComplexes = FALSE)[去= getGOInfo（wantAllComplexes = FALSE时）]
#mips = getMipsInfo(wantSubComplexes = FALSE)[MIPS = getMipsInfo（wantSubComplexes = FALSE时）]
#goM = createGOMatrix(go)[GOM = createGOMatrix（去）]
#mipsM = createMipsMatrix(mips)[mipsM = createMipsMatrix（MIPS）]
#cc = runCompareComplex(mipsM, goM, byWhich="ROW")[CC = runCompareComplex（mipsM，GOM，byWhich =“列”）]
#m = maximizeSimilarity(cc$JC, byWhich = "ROW")[M = maximizeSimilarity（CC $ JC，byWhich =“排”）]
#m$maximize[M $最大化]
#m$maxComp[百万元maxComp]

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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