consensusKME(WGCNA)
consensusKME()所属R语言包：WGCNA
Calculate consensus kME (eigengene-based connectivities) across multiple data sets.
跨多个数据集的计算共识KME（eigengene基于连通）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculate consensus kME (eigengene-based connectivities) across multiple data sets, typically following a consensus module analysis.
跨多个数据集，一般的共识模块的分析计算共识KME（eigengene基于连通）。
用法----------Usage----------
consensusKME(multiExpr, moduleLabels,
multiEigengenes = NULL,
consensusQuantile = 0,
signed = TRUE,
useModules = NULL,
metaAnalysisWeights = NULL,
corAndPvalueFnc = corAndPvalue, corOptions = list(), corComponent = "cor",
getQvalues = FALSE,
useRankPvalue = TRUE,
rankPvalueOptions = list(calculateQvalue = getQvalues, pValueMethod = "scale"),
setNames = NULL,
excludeGrey = TRUE, greyLabel = ifelse(is.numeric(moduleLabels), 0, "grey"))
参数----------Arguments----------
参数：multiExpr
Expression (or other numeric) data in a multi-set format. A vector of lists; in each list there must be a component named "data" whose content is a matrix or dataframe or array of dimension 2.
的表达（或其他数字）在一个多设置的格式的数据。的向量的列表，在每个列表中，必须有一个名为数据，其内容是一个矩阵或2维的数据框或数组的组件。
参数：moduleLabels
Module labels: one label for each gene in multiExpr.
模块标签：一个标签，每个基因在multiExpr。
参数：multiEigengenes
Optional eigengenes of modules specified in moduleLabels. If not given, will be calculated from multiExpr.
可选的特征基因在moduleLabels指定的模块。如果没有给出，将计算出multiExpr。
参数：signed
logical: should the network be considered signed? In signed networks (TRUE), negative kME values are not considered significant and the corresponding p-values will be one-sided. In unsigned networks (FALSE), negative kME values are considered significant and the corresponding p-values will be two-sided.
网络的逻辑：应考虑签署？在签署网络（TRUE），负KME值不考虑显著和相应的p-值将是片面的。无符号网络（FALSE），负KME值被认为是显著和相应的p-值将双面。
参数：useModules
Optional specification of module labels to which the analysis should be restricted. This could be useful if there are many modules, most of which are not interesting. Note that the "grey" module cannot be used with useModules.
可选规范应限制分析模块的标签。这可能是有用的，如果有很多模块，其中大部分是有趣的。需要注意的是“灰色”模块不能使用useModules。
参数：consensusQuantile
Quantile for the consensus calculation. Should be a number between 0 (minimum) and 1.
分位数计算的共识。应该是0（最小）和1之间的一个数。
参数：metaAnalysisWeights
Optional specification of meta-analysis weights for each input set. If given, must be a numeric vector of length equal the number of input data sets (i.e., length(multiExpr)). These weights will be used in addition to constant weights and weights proportional to number of samples (observations) in each set.
可选规格为每个输入组的荟萃分析权重。如果给定的，必须是一个数字向量的长度等于输入数据集的数量（即，length(multiExpr)）。除了以恒定的重量和权重成比例的每个集合中的样本数（观测值）将被用在这些权重。
参数：corAndPvalueFnc
Function that calculates associations between expression profiles and eigengenes. See details.
函数，计算表达谱和特征基因之间的关联。查看详细信息。
参数：corOptions
List giving additional arguments to function corAndPvalueFnc. See details.
目录其他参数功能corAndPvalueFnc。查看详细信息。
参数：corComponent
Name of the component of output of corAndPvalueFnc that contains the actual correlation.
输出corAndPvalueFnc的组件的名称包含实际的相关性。
参数：getQvalues
logical: should q-values (estimates of FDR) be calculated?
逻辑：Q值（估计FDR）计算出来的？
参数：useRankPvalue
Logical: should the rankPvalue function be used to obtain alternative meta-analysis statistics?
逻辑：rankPvalue功能可用于获取替代的荟萃分析统计？
参数：rankPvalueOptions
Additional options for function rankPvalue. These include na.last (default "keep"), ties.method (default "average"), calculateQvalue (default copied from input getQvalues), and pValueMethod (default "scale"). See the help file for rankPvalue for full details.
其他选项功能rankPvalue。这些措施包括na.last（默认"keep"）ties.method（默认"average"）calculateQvalue（默认复制输入getQvalues），和pValueMethod（默认"scale"“）。为rankPvalue的全部详细信息，请参阅帮助文件。
参数：setNames
names for the input sets. If not given, will be taken from names(multiExpr). If those are NULL as well, the names will be "Set_1", "Set_2", ....
输入集的名称。如果没有给出，将采取从names(multiExpr)。如果这些都NULL以及名是"Set_1", "Set_2", ...。
参数：excludeGrey
logical: should the grey module be excluded from the kME tables? Since the grey module is typically not a real module, it makes little sense to report kME values for it.
逻辑：灰色的模块应该被排除在外的金桥表吗？由于灰色模块通常不是一个真正的模块，这是毫无意义的报告KME它的值。
参数：greyLabel
label that labels the grey module.
标签，标签的灰色模块。
Details
详细信息----------Details----------
The function corAndPvalueFnc is currently is expected to accept arguments x (gene expression profiles), y (eigengene expression profiles), and alternative with possibilities at least "greater", "two.sided". Any additional arguments can be passed via corOptions.
的功能corAndPvalueFnc目前预计接受参数x（基因的表达谱），y（eigengene表达谱），和alternative的可能性至少"greater", "two.sided" 。通过corOptions可以通过任何额外的参数。
The function corAndPvalueFnc should return a list which at the least contains (1) a matrix of associations of genes and eigengenes (this component should have the name given by corComponent), and (2) a matrix of the corresponding p-values, named "p" or "p.value". Other components are optional but for full functionality should include (3) nObs giving the number of observations for each association (which is the number of samples less number of missing data - this can in principle vary from association to association), and (4) Z giving a Z static for each observation. If these are missing, nObs is calculated in the main function, and calculations using the Z statistic are skipped.
的功能corAndPvalueFnc应该返回一个列表，其中在至少包含（1）的矩阵的关联的基因和特征基因（此组件应具有由corComponent），和（2）的矩阵给出的名称相应的p-值，命名为“p”或“p.value”。其他组件是可选的，但（3）的全部功能应该包括nObs给予观测值的数量为每个关联（这是的样本数量丢失的数据的数目少 - 在原则上，这可以从关联的关联而异），及（4）Z给予对于每个观测的Z静态。如果缺少这些，nObs计算的主要功能，并跳过使用的Z统计量的计算。
值----------Value----------
Data frame with the following components (for easier readability the order here is not the same as in the actual output):
数据框与下列组分（以方便的可读性这里的顺序是不一样的，在实际输出）：
参数：ID
Gene ID, taken from the column names of the first input data set
基因ID，从第一个输入数据集的列名
参数：consensus.kME.1, consensus.kME.2, ...
Consensus kME (that is, the requested quantile of the kMEs in the individual data sets)in each module for each gene across the input data sets. The module labels (here 1, 2, etc.) correspond to those in moduleLabels.
在每个模块，用于跨接在输入数据集的每一个基因的共识KME（即，所要求的位数中的单个数据集的kMEs）。该模块的标签（这里是1，2，等）对应那些在moduleLabels。
参数：weightedAverage.equalWeights.kME1, weightedAverage.equalWeights.kME2, ...
Average kME in each module for each gene across the input data sets.
横跨输入数据集在每个模块中的每个基因的平均KME。
参数：weightedAverage.RootDoFWeights.kME1, weightedAverage.RootDoFWeights.kME2, ...
Weighted average kME in each module for each gene across the input data sets. The weight of each data set is proportional to the square root of the number of samples in the set.
横跨输入数据集在每个模块中的每个基因的加权平均KME。重量的每一个数据集的集合中的样本数的平方根成正比。
参数：weightedAverage.DoFWeights.kME1, weightedAverage.DoFWeights.kME2, ...
Weighted average kME in each module for each gene across the input data sets. The weight of each data set is proportional to number of samples in the set.
横跨输入数据集在每个模块中的每个基因的加权平均KME。每个数据集的重量成正比的集合中的样本数。
参数：weightedAverage.userWeights.kME1, weightedAverage.userWeights.kME2, ...
(Only present if input metaAnalysisWeights is non-NULL.) Weighted average kME in each module for each gene across the input data sets. The weight of each data set is given in metaAnalysisWeights.
（只存在如果输入metaAnalysisWeights是NULL。）加权平均KME在每个模块中的每个基因在整个输入数据集。每一个数据集的重量给出在metaAnalysisWeights。
参数：meta.Z.equalWeights.kME1, meta.Z.equalWeights.kME2, ...
Meta-analysis Z statistic for kME in each module, obtained by weighing the Z scores in each set equally. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
Meta分析的Z统计KME，每个模块中获得同样的体重Z分数在各组。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.Z.RootDoFWeights.kME1, meta.Z.RootDoFWeights.kME2, ...
Meta-analysis Z statistic for kME in each module, obtained by weighing the Z scores in each set by the square root of the number of samples. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
Meta分析Z统计KME在每个模块中，通过以下方式获得称量每个集合中的采样数的平方根的Z分值。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.Z.DoFWeights.kME1, meta.Z.DoFWeights.kME2, ...
Meta-analysis Z statistic for kME in each module, obtained by weighing the Z scores in each set by the number of samples. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
Meta分析Z统计KME在每个模块中，通过以下方式获得称量Z分数每个集合中的样本数。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.Z.userWeights.kME1, meta.Z.userWeights.kME2, ...
Meta-analysis Z statistic for kME in each module, obtained by weighing the Z scores in each set by metaAnalysisWeights. Only returned if metaAnalysisWeights is non-NULL and the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
Meta分析的Z统计KME，每个模块中获得的体重Z分数在各组的metaAnalysisWeights。只有返回metaAnalysisWeights非NULL的功能corAndPvalueFnc返回的Z统计量对应的相关性。
参数：meta.p.equalWeights.kME1, meta.p.equalWeights.kME2, ...
p-values obtained from the equal-weight meta-analysis Z statistics. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
从等于重量荟萃分析Z统计得到的p-值。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.p.RootDoFWeights.kME1, meta.p.RootDoFWeights.kME2, ...
p-values obtained from the meta-analysis Z statistics with weights proportional to the square root of the number of samples. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
从荟萃分析的样本数的平方根成比例的配重块的Z统计得到的p-值。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.p.DoFWeights.kME1, meta.p.DoFWeights.kME2, ...
p-values obtained from the degree-of-freedom weight meta-analysis Z statistics. Only returned if the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
程度的自由重量的荟萃分析Z统计量的p值。只有返回的功能corAndPvalueFnc返回Z统计量对应的相关性。
参数：meta.p.userWeights.kME1, meta.p.userWeights.kME2, ...
p-values obtained from the user-supplied weight meta-analysis Z statistics. Only returned if metaAnalysisWeights is non-NULL and the function corAndPvalueFnc returns the Z statistics corresponding to the correlations.
获得用户提供的重量荟萃分析Z统计量的p值。只有返回metaAnalysisWeights非NULL的功能corAndPvalueFnc返回的Z统计量对应的相关性。
参数：meta.q.equalWeights.kME1, meta.q.equalWeights.kME2, ...
q-values obtained from the equal-weight meta-analysis p-values. Only present if getQvalues is TRUE and the function corAndPvalueFnc returns the Z statistics corresponding to the kME values.
从等于重量荟萃分析的p值获得的Q-值。如果getQvalues是TRUE和函数corAndPvalueFnc的返回对应的的KME值的Z统计量。
参数：meta.q.RootDoFWeights.kME1, meta.q.RootDoFWeights.kME2, ...
q-values obtained from the meta-analysis p-values with weights proportional to the square root of the number of samples. Only present if getQvalues is TRUE and the function corAndPvalueFnc returns the Z statistics corresponding to the kME values.
p-值的样本数的平方根成比例的配重块从元分析获得的Q-值。如果getQvalues是TRUE和函数corAndPvalueFnc的返回对应的的KME值的Z统计量。
参数：meta.q.DoFWeights.kME1, meta.q.DoFWeights.kME2, ...
q-values obtained from the degree-of-freedom weight meta-analysis p-values. Only present if getQvalues is TRUE and the function corAndPvalueFnc returns the Z statistics corresponding to the kME values.
从自由度重量荟萃分析p-值获得的Q-值。如果getQvalues是TRUE和函数corAndPvalueFnc的返回对应的的KME值的Z统计量。
参数：meta.q.userWeights.kME1, meta.q.userWeights.kME2, ...
q-values obtained from the user-specified weight meta-analysis p-values. Only present if metaAnalysisWeights is non-NULL, getQvalues is TRUE and the function corAndPvalueFnc returns the Z statistics corresponding to the kME values.
从用户指定的重量荟萃分析p-值获得的Q-值。如果metaAnalysisWeights非NULL，getQvalues是TRUE和功能corAndPvalueFnc返回的Z统计的KME值对应。
The next set of columns contain the results of function rankPvalue and are only present if input useRankPvalue is TRUE. Some columns may be missing depending on the options specified in rankPvalueOptions. We explicitly list columns that are based on weighing each set equally; names of these columns carry the suffix .equalWeights
接下来的一组列包含功能rankPvalue的结果，如果输入useRankPvalue是TRUE，是目前唯一。某些列可能会丢失根据指定的rankPvalueOptions的选项。我们明确地列出为基础的重每一套同样的列，这些列的名字进行的后缀.equalWeights
参数：pValueExtremeRank.ME1.equalWeights, pValueExtremeRank.ME2.equalWeights, ...
This is the minimum between pValueLowRank and pValueHighRank, i.e. min(pValueLow, pValueHigh)
这是之间pValueLowRank和pValueHighRank最小，即分钟（pValueLow，pValueHigh）
参数：pValueLowRank.ME1.equalWeights, pValueLowRank.ME2.equalWeights, ...
Asymptotic p-value for observing a consistently low value across the columns of datS based on the rank method.
渐近p值在列反三合会行动组观察持续低价值的排名方法的基础上。
参数：pValueHighRank.ME1.equalWeights, pValueHighRank.ME2.equalWeights, ...
Asymptotic p-value for observing a consistently low value across the columns of datS based on the rank method.
渐近p值在列反三合会行动组观察持续低价值的排名方法的基础上。
参数：pValueExtremeScale.ME1.equalWeights, pValueExtremeScale.ME2.equalWeights, ...
This is the minimum between pValueLowScale and pValueHighScale, i.e. min(pValueLow, pValueHigh)
这是之间pValueLowScale和pValueHighScale最小，即分钟（pValueLow，pValueHigh）
参数：pValueLowScale.ME1.equalWeights, pValueLowScale.ME2.equalWeights, ...
Asymptotic p-value for observing a consistently low value across the columns of datS based on the Scale method.
渐近p值在列反三合会行动组观察持续低价值的基础上的Scale方法。
参数：pValueHighScale.ME1.equalWeights, pValueHighScale.ME2.equalWeights, ...
Asymptotic p-value for observing a consistently low value across the columns of datS based on the Scale method.
渐近p值在列反三合会行动组观察持续低价值的基础上的Scale方法。
参数：qValueExtremeRank.ME1.equalWeights, qValueExtremeRank.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueExtremeRank
虚假的发现率（q值）对应的p值pValueExtremeRank
参数：qValueLowRank.ME1.equalWeights, qValueLowRank.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueLowRank
虚假的发现率（q值）对应的p值pValueLowRank
参数：qValueHighRank.ME1.equalWeights, lueHighRank.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueHighRank
虚假的发现率（q值）对应的p值pValueHighRank
参数：qValueExtremeScale.ME1.equalWeights, qValueExtremeScale.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueExtremeScale
虚假的发现率（q值）对应的的p值pValueExtremeScale的
参数：qValueLowScale.ME1.equalWeights, qValueLowScale.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueLowScale
虚假的发现率（q值）对应的的p值pValueLowScale的
参数：qValueHighScale.ME1.equalWeights,qValueHighScale.ME2.equalWeights, ...
local false discovery rate (q-value) corresponding to the p-value pValueHighScale
虚假的发现率（q值）对应的的p值pValueHighScale的
参数：...
Analogous columns corresponding to weighing individual sets by the square root of the number of samples, by number of samples, and by user weights (if given). The corresponding column name suffixes are .RootDoFWeights, .DoFWeights, and .userWeights.
类似的列对应于称重各套的样本数的平方根，由样本的数量，并通过用户的权重（如果给定）。相应的列名称的后缀是.RootDoFWeights，.DoFWeights和.userWeights。
The following set of columns summarize kME in individual input data sets.
下面的一组列的总结KME在个别的输入数据集。
参数：kME1.Set_1, kME1.Set_2, ..., kME2.Set_1, kME2.Set_2, ...
kME values for each gene in each module in each given data set.
KME值在每个模块中的每个基因在每个给定的数据集。
参数：p.kME1.Set_1, p.kME1.Set_2, ..., p.kME2.Set_1, p.kME2.Set_2, ...
p-values corresponding to kME values for each gene in each module in each given data set.
对值对应KME在每个模块中的每一个基因的值，在每个给定的数据集。
参数：q.kME1.Set_1, q.kME1.Set_2, ..., q.kME2.Set_1, q.kME2.Set_2, ...
q-values corresponding to kME values for each gene in each module in each given data set. Only returned if getQvalues is TRUE.
Q值对应于KME在每个模块中的每一个基因的值，在每个给定的数据集。仅返回getQvalues是TRUE。
参数：Z.kME1.Set_1, Z.kME1.Set_2, ..., Z.kME2.Set_1, Z.kME2.Set_2, ...
Z statistics corresponding to kME values for each gene in each module in each given data set. Only present if the function corAndPvalueFnc returns the Z statistics corresponding to the kME values.
Z统计量对应的KME在每个模块中的每一个基因的值，在每个给定的数据集。目前唯一的功能corAndPvalueFnc返回相应的的KME值的Z统计量。
（作者）----------Author(s)----------
Peter Langfelder
参考文献----------References----------
BMC Bioinformatics. 2008 Dec 29; 9:559.
参见----------See Also----------
signedKME for eigengene based connectivity in a single data set. corAndPvalue, bicorAndPvalue for two alternatives for calculating correlations and the corresponding p-values and Z scores. Both can be used with this function.
signedKME为eigengene基于在单个数据集的连接。用于计算相关性的两个备选方案和相应的p-值和Z分数corAndPvalue，bicorAndPvalue。既可以用于与此功能。
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。
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发表于 2012-10-1 21:10:22
