overlapTableUsingKME(WGCNA)
overlapTableUsingKME()所属R语言包：WGCNA
Determines significant overlap between modules in two networks based on kME tables.
确定重大重叠的两个网络的基础上KME表中的模块。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Takes two sets of expression data (or kME tables) as input and returns a table listing the significant overlap between each module in each data set, as well as the actual genes in common for every module pair. Modules can be defined in several ways (generally involving kME) based on user input.
将两组的表达数据（或KME表）作为输入，并返回一个表，列出了各个模块之间的显著重叠在每个数据集，以及为每一个模块对实际的共同基因。模块可以被定义在几个方面（一般涉及KME）基于用户的输入。
用法----------Usage----------
overlapTableUsingKME(dat1, dat2, colorh1, colorh2, MEs1 = NULL, MEs2 = NULL, name1 = "MM1", name2 = "MM2", cutoffMethod = "assigned", cutoff = 0.5, omitGrey = TRUE, datIsExpression = TRUE)
参数----------Arguments----------
参数：dat1,dat2
Either expression data sets (with samples as rows and genes as columns) or module membership (kME) tables (with genes as rows and modules as columns). Function reads these inputs based on whether datIsExpression=TRUE or FALSE. ***Be sure that these inputs include relevant row and column names, or else the function will not work properly.***
无论是表达数据集（行和列的基因的样本）或模块的成员（KME）表的（与基因行和列的模块）。功能读取这些投入的基础上是否datIsExpression = TRUE或FALSE。 ***确定，这些投入包括相关的行和列的名称，否则该功能将无法正常工作。***
参数：colorh1,colorh2
Color vector (module assignments) corresponding to the genes from dat1/2. This vector must be the same length as the Gene dimension from dat1/2.
颜色矢量（模块分配）对应从DAT1 / 2的基因。该向量必须是基因维度从DAT1 / 2的长度相同。
参数：MEs1,MEs2
If entered (default=NULL), these are the module eigengenes that will be used to form the kME tables. Rows are samples and columns are module assignments. Note that if datIsExpression=FALSE, these inputs are ignored.
如果输入（默认值= NULL），这些模块的特征基因，将用于形成KME表。行的样品和列是模块分配。需要注意的是，如果datIsExpression = FALSE，这些输入将被忽略。
参数：name1,name2
The names of the two data sets being compared. These names affect the output parameters.
被比较的两个数据集的名称。这些名称会影响输出参数。
参数：cutoffMethod
This variable is used to determine how modules are defined in each data set. Must be one of four options: (1) "assigned" -> use the module assignments in colorh (default); (2) "kME" -> any gene with kME > cutoff is in the module; (3) "numGenes" -> the top cutoff number of genes based on kME is in the module; and (4) "pvalue" -> any gene with correlation pvalue < cutoff is in the module (this includes both positively and negatively-correlated genes).
这个变量被用于确定如何模块中定义的每一个数据集。必须在四个选项之一：（1）“分配” - >使用的模块分配在colorh（默认值），（2）“KME” - 的KME>截断的任何基因是在模块（ 3）“numGenes” - >最佳截止基于KME基因数是在该模块中，以及（4）“P值” - >相关P值<截止任何基因是在模块中（这既包括正和负相关的基因）。
参数：cutoff
For all cutoffMethods other than "assigned", this parameter is used as the described cutoff value.
对于所有cutoffMethods以外的“分配”，使用此参数所描述的临界值。
参数：omitGrey
If TRUE the grey modules (non-module genes) for both networks are not returned.
如果为true，灰色模块（模块非基因）两个网络都没有回来。
参数：datIsExpression
If TRUE (default), dat1/2 is assumed to be expression data. If FALSE, dat1/2 is assumed to be a table of kME values.
如果是TRUE（默认），DAT1 / 2被认为是表达数据。如果为FALSE，DAT1 / 2被假定为一个表KME值。
值----------Value----------
参数：PvaluesHypergeo
A table of p-values showing significance of module overlap based on the hypergeometric test. Note that these p-values are not corrected for multiple comparisons.
一个表中的p值显示模块的意义重叠的基础上超几何测试。请注意，这些p-值是没有多重比较校正。
参数：AllCommonGenes
A character vector of all genes in common between the two data sets.
两个数据之间的共同基因集的字符向量。
参数：Genes<name1/2>
A list of character vectors of all genes in each module in both data sets. All genes in the MOD module in data set MM1 could be found using "<outputVariableName>$GenesMM1$MM1_MOD"
在两个数据集的每个模块中的所有基因的特征向量的列表。所有的基因在MOD模块的数据集MM1可以发现使用“<outputVariableName>的$ GenesMM1 $ MM1_MOD的”
参数：OverlappingGenes
A list of character vectors of all genes for each between-set comparison from PvaluesHypergeo. All genes in MOD.A from MM1 that are also in MOD.B from MM2 could be found using "<outputVariableName>$OverlappingGenes$MM1_MOD.A_MM2_MOD.B"
一个列表中的所有基因间集从PvaluesHypergeo比较的特征向量。所有基因的MOD.A MM1也在MOD.B从MM2可以使用“<outputVariableName> OverlappingGenes $ MM1_MOD.A_MM2_MOD.B的”
（作者）----------Author(s)----------
Jeremy Miller
参见----------See Also----------
overlapTable
overlapTable
实例----------Examples----------
# Example: first generate simulated data.[例：首先生成模拟数据。]
set.seed(100)
ME.A = sample(1:100,50); ME.B = sample(1:100,50)
ME.C = sample(1:100,50); ME.D = sample(1:100,50)
ME.E = sample(1:100,50); ME.F = sample(1:100,50)
ME.G = sample(1:100,50); ME.H = sample(1:100,50)
ME1 = data.frame(ME.A, ME.B, ME.C, ME.D, ME.E)
ME2 = data.frame(ME.A, ME.C, ME.D, ME.E, ME.F, ME.G, ME.H)
simDat1 = simulateDatExpr(ME1,1000,c(0.2,0.1,0.08,0.05,0.04,0.3), signed=TRUE)
simDat2 = simulateDatExpr(ME2,1000,c(0.2,0.1,0.08,0.05,0.04,0.03,0.02,0.3), signed=TRUE)
# Now run the function using assigned genes[现在运行的功能，使用指定的基因]
results = overlapTableUsingKME(simDat1$datExpr, simDat2$datExpr, labels2colors(simDat1$allLabels), labels2colors(simDat2$allLabels), cutoffMethod="assigned")
results$PvaluesHypergeo
# Now run the function using a p-value cutoff, and inputting the original MEs[现在运行该函数使用一个p-值截止，和输入的原始的ME]
colnames(ME1) = standardColors(5); colnames(ME2) = standardColors(7)
results = overlapTableUsingKME(simDat1$datExpr, simDat2$datExpr, labels2colors(simDat1$allLabels), labels2colors(simDat2$allLabels), ME1, ME2, cutoffMethod="pvalue", cutoff=0.05)
results$PvaluesHypergeo
# Check which genes are in common between the black modules from set 1 and the green module from set 2[检查哪些基因组1和第2组的绿色模块之间的黑色模块]
results$OverlappingGenes$MM1_green_MM2_black
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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 21:20:29
