metaAnalysis(WGCNA)
metaAnalysis()所属R语言包：WGCNA
Meta-analysis of binary and continuous variables
二进制和连续变量的Meta分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is a meta-analysis complement to functions standardScreeningBinaryTrait and standardScreeningNumericTrait. Given expression (or other) data from multiple independent data sets, and the corresponding clinical traits or outcomes, the function calculates multiple screening statistics in each data set, then calculates meta-analysis Z scores, p-values, and optionally q-values (False Discovery Rates). Three different ways of calculating the meta-analysis Z scores are provided: the Stouffer method, weighted Stouffer method, and using user-specified weights.
这是一个荟萃分析的补充功能standardScreeningBinaryTrait和standardScreeningNumericTrait。由于表达式（或其它）的数据从多个独立的数据集，和相应的临床性状或成果，该函数计算多个筛选统计在每个数据集，然后计算荟萃分析Z分数，p-值，和任选的Q-值（假发现率）。三种不同的方式计算的荟萃分析的Z分数的的斯托弗方法，加权斯托弗方法，以及使用用户指定的权重。
用法----------Usage----------
metaAnalysis(multiExpr, multiTrait,
binary = NULL,
metaAnalysisWeights = NULL,
corFnc = cor, corOptions = list(use = "p"),
getQvalues = FALSE,
getAreaUnderROC = FALSE,
useRankPvalue = TRUE,
rankPvalueOptions = list(),
setNames = NULL,
kruskalTest = FALSE, var.equal = FALSE,
metaKruskal = kruskalTest, na.action = "na.exclude")
参数----------Arguments----------
参数：multiExpr
Expression data (or other data) in multi-set format (see checkSets). 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.
表达在多设置的格式的数据（或其他数据）（见checkSets）。一个向量的列表，每个列表中必须有一个名为data“，其内容是一个矩阵或数据框或数组的维数为2的组件。
参数：multiTrait
Trait or ourcome data in multi-set format. Only one trait is allowed; consequesntly, the data component of each component list can be either a vector or a data frame (matrix, array of dimension 2).
多套格式的特质或ourcome数据。只有一个特征被允许; consequesntly，data组件的每个组件列表可以是一个矢量或一个数据框（2维阵列的矩阵，）。
参数：binary
Logical: is the trait binary (TRUE) or continuous (FALSE)? If not given, the decision will be made based on the content of multiTrait.
逻辑：二进制的特征（TRUE）或连续（FALSE）的吗？如果没有给出，决定将根据内容的multiTrait。
参数：metaAnalysisWeights
Optional specification of set weights for meta-analysis. If given, must be a vector of non-negative weights, one entry for each set contained in multiExpr.
可选规格设置的权重进行荟萃分析。如果给定的，必须是一个非负权重向量，每一组的一个条目中包含的multiExpr。
参数：corFnc
Correlation function to be used for screening. Should be either the default cor or its robust alternative, bicor.
相关函数被用于筛选。应的默认cor或其强大的替代，bicor。
参数：corOptions
A named list giving extra arguments to be passed to the correlation function.
提供额外的参数被传递到相关函数的命名列表。
参数：getQvalues
Logical: should q-values (FDRs) be calculated?
逻辑：Q值（FDRs）计算出来的？
参数：getAreaUnderROC
Logical: should area under the ROC be calculated? Caution, enabling the calculation will slow the function down considerably for large data sets.
逻辑的ROC曲线下面积如何计算？注意，使计算将减缓功能下降的大型数据集。
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 "all"). 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"all"“）。为rankPvalue的全部详细信息，请参阅帮助文件。
参数：setNames
Optional specification of set names (labels). These are used to label the corresponding components of the output. If not given, will be taken from the names attribute of multiExpr. If names(multiExpr) is NULL, generic names of the form Set_1, Set2, ... will be used.
可选规格的集名称（标签）。这些被用于标记的输出的相应的组件。如果没有给出，将采取从names属性multiExpr。如果names(multiExpr)是NULL，通用名称的形式Set_1, Set2, ...的使用。
参数：kruskalTest
Logical: should the Kruskal test be performed in addition to t-test? Only applies to binary traits.
逻辑：应进行t检验的Kruskal测试？仅适用于二进制的特征。
参数：var.equal
Logical: should the t-test assume equal variance in both groups? If TRUE, the function will warn the user that the returned test statistics will be different from the results of the standard t.test function.
逻辑：t-检验假设等方差在这两个群体？如果TRUE“的功能向用户发出警告，返回的检验统计量的标准t.test函数的结果会有所不同。
参数：metaKruskal
Logical: should the meta-analysis be based on the results of Kruskal test (TRUE) or Student t-test (FALSE)?
逻辑：应的克鲁斯卡尔测试结果（TRUE）或Student t检验（FALSE）的荟萃分析的基础上吗？
参数：na.action
Specification of what should happen to missing values in t.test.
会发生什么遗漏值t.test规范。
Details
详细信息----------Details----------
The Stouffer method of combines Z statistics by simply taking a mean of input Z statistics and multiplying it by sqrt(n), where n is the number of input data sets. We refer to this method as Stouffer.equalWeights. In general, a better (i.e., more powerful) method of combining Z statistics is to weigh them by the number of degrees of freedom (which approximately equals n). We refer to this method as weightedStouffer. Finally, the user can also specify custom weights, for example if a data set needs to be downweighted due to technical concerns; however, specifying own weights by hand should be done carefully to avoid possible selection biases.
斯托弗方法的结合Z统计量，通过采取简单的平均输入Z统计量乘以sqrt(n)，n是输入数据集的数量。我们参考此方法Stouffer.equalWeights。在一般情况下，更好的（即，更强大的）的方法相结合的Z统计是权衡他们的自由度的数目（其中约等于n）。我们参考此方法weightedStouffer。最后，用户还可以指定自定义的权重，例如，如果一个数据集需要downweighted由于技术问题，然而，通过手工指定自己的重量应小心，以避免可能的选择偏差。
值----------Value----------
Data frame with the following components:
数据框与以下组件：
参数：ID
Identifier of the input genes (or other variables)
标识符输入基因（或其他变量）
参数：Z.equalWeights
Meta-analysis Z statistics obtained using Stouffer's method with equal weights
Meta分析Z统计量使用斯托弗的方法获得相同的权重
参数：p.equalWeights
p-values corresponding to Z.Stouffer.equalWeights
Z.Stouffer.equalWeights对应的p-值
参数：q.equalWeights
q-values corresponding to p.Stouffer.equalWeights, only present if getQvalues is TRUE.
Q值的相应的p.Stouffer.equalWeights，只有getQvalues如果是TRUE。
参数：Z.RootDoFWeights
Meta-analysis Z statistics obtained using Stouffer's method with weights given by the square root of the number of (non-missing) samples in each data set
Meta分析Z统计使用斯托夫的方法与在每个数据集的数量（非缺失）样本的平方根给出的权重得到
参数：p.RootDoFWeights
p-values corresponding to Z.DoFWeights
Z.DoFWeights对应的p-值
参数：q.RootDoFWeights
q-values corresponding to p.DoFWeights, only present if getQvalues is TRUE.
Q值的相应的p.DoFWeights，只有getQvalues如果是TRUE。
参数：Z.DoFWeights
Meta-analysis Z statistics obtained using Stouffer's method with weights given by the number of (non-missing) samples in each data set
Meta分析的Z统计量使用斯托弗的方法，在每个数据集的比重由数量的样本（非缺失）
参数：p.DoFWeights
p-values corresponding to Z.DoFWeights
Z.DoFWeights对应的p-值
参数：q.DoFWeights
q-values corresponding to p.DoFWeights, only present if getQvalues is TRUE.
Q值的相应的p.DoFWeights，只有getQvalues如果是TRUE。
参数：Z.userWeights
Meta-analysis Z statistics obtained using Stouffer's method with user-defined weights. Only present if input metaAnalysisWeights are present.
Meta分析Z统计量获得使用斯托弗的方法与用户定义的权重。如果输入metaAnalysisWeights存在。
参数：p.userWeights
p-values corresponding to Z.userWeights
Z.userWeights对应的p-值
参数：q.userWeights
q-values corresponding to p.userWeights, only present if getQvalues is TRUE.
Q值的相应的p.userWeights，只有getQvalues如果是TRUE。
The next set of columns is present only if input useRankPvalue is TRUE and contain the output of the function rankPvalue with the same column weights as the above meta-analysis. Depending on the input options calculateQvalue and pValueMethod in rankPvalueOptions, some columns may be missing. The following columns are calculated using equal weights for each data set.
接下来的一组列是目前仅在输入useRankPvalue是TRUE“，并包含了输出的功能rankPvalue具有相同的列权重的上述荟萃分析。根据输入选项calculateQvalue和pValueMethod rankPvalueOptions，可能会丢失一些列中。下面的列使用相同的权重计算每个数据集。
参数：pValueExtremeRank.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.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.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.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.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.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.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.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.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.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.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.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 calculated by weighting each input set using the square root of the number of samples, number of samples, and user weights (if given). The corresponding column names carry the suffixes RootDofWeights, DoFWeights, userWeights.
类似于通过加权计算的列每个输入集使用的样本数的平方根，采样数，和用户权重（如果给定）。进行相应的列名的后缀RootDofWeights，DoFWeights，userWeights。
The following columns contain results returned by standardScreeningBinaryTrait or standardScreeningNumericTrait (depending on whether the input trait is binary or continuous).
下面列包含standardScreeningBinaryTrait或standardScreeningNumericTrait（根据输入的特点是二进制或连续）返回的结果。
For binary traits, the following information is returned for each set:
对于二进制的特征，将返回以下信息为每一组：
参数：corPearson.Set_1, corPearson.Set_2,...
Pearson correlation with a binary numeric version of the input variable. The numeric variable equals 1 for level 1 and 2 for level 2. The levels are given by levels(factor(y)).
Pearson相关输入变量与一个二进制的数字版本。的数字变量等于1，第2级为1级和2。水平（因子（y）的）由下式给出的水平。
参数：t.Student.Set_1, t.Student.Set_2, ...
Student t-test statistic
学生t-检验统计量
参数：pvalueStudent.Set_1, pvalueStudent.Set_2, ...
two-sided Student t-test p-value.
两面Student t检验的p值。
参数：qvalueStudent.Set_1, qvalueStudent.Set_2, ...
(if input qValues==TRUE) q-value (local false discovery rate) based on the Student T-test p-value (Storey et al 2004).
（若输入qValues==TRUE）Q值（本地虚假的发现率），根据学生t检验P值（楼层等人，2004年）。
参数：foldChange.Set_1, foldChange.Set_2, ...
a (signed) ratio of mean values. If the mean in the first group (corresponding to level 1) is larger than that of the second group, it equals meanFirstGroup/meanSecondGroup. But if the mean of the second group is larger than that of the first group it equals -meanSecondGroup/meanFirstGroup (notice the minus sign).
（签名）比的平均值。如果第一组中的平均值（对应于第1级）是大于所述第二组，，它等于meanFirstGroup / meanSecondGroup。但是，如果第二组的平均值是大于第一组等于-meanSecondGroup/meanFirstGroup（注意到减号）。
参数：meanFirstGroup.Set_1, meanSecondGroup.Set_2, ...
means of columns in input datExpr across samples in the second group.
输入datExpr样本之间的第二组中的列中的装置。
参数：SE.FirstGroup.Set_1, SE.FirstGroup.Set_2, ...
standard errors of columns in input datExpr across samples in the first group. Recall that SE(x)=sqrt(var(x)/n) where n is the number of non-missing values of x.
标准误差中的列输入datExpr跨在第一组中的样品。回想一下，SE（x）的=的sqrt（var（x）的的/ n）的，其中，n是x的非缺失值的数目。
参数：SE.SecondGroup.Set_1, SE.SecondGroup.Set_2, ...
standard errors of columns in input datExpr across samples in the second group.
标准误差中的列输入datExpr跨在第二组中的样品。
参数：areaUnderROC.Set_1, areaUnderROC.Set_2, ...
the area under the ROC, also known as the concordance index or C.index. This is a measure of discriminatory power. The measure lies between 0 and 1 where 0.5 indicates no discriminatory power. 0 indicates that the "opposite" predictor has perfect discriminatory power. To compute it we use the function rcorr.cens with outx=TRUE (from Frank Harrel's package Hmisc).
的ROC曲线下的面积，也称为一致性指数或C.index。这是一个衡量的歧视性权力。措施是0和1之间，其中0.5表示没有歧视性的权力。 0表示“相反”的预测具有完善的鉴别力。要计算它，我们使用的功能rcorr.cens的，用outx=TRUE（从弗兰克Harrel包Hmisc）。
参数：nPresentSamples.Set_1, nPresentSamples.Set_2, ...
number of samples with finite measurements for each gene.
与有限测量的每个基因的样本数。
If input kruskalTest is TRUE, the following columns further summarize results of Kruskal-Wallis test:
，如果输入kruskalTestTRUE，下面列进一步总结Kruskal-Wallis检验的结果：
参数：stat.Kruskal.Set_1, stat.Kruskal.Set_2, ...
Kruskal-Wallis test statistic.
Kruskal-Wallis检验统计量。
参数：stat.Kruskal.signed.Set_1, stat.Kruskal.signed.Set_2,...
(Warning: experimental) Kruskal-Wallis test statistic including a sign that indicates whether the average rank is higher in second group (positive) or first group (negative).
（警告：实验）Kruskal-Wallis检验统计，包括标志上显示的平均排名是否是在第二组（阳性）或第一组（负）。
参数：pvaluekruskal.Set_1, pvaluekruskal.Set_2, ...
Kruskal-Wallis test p-value.
Kruskal-Wallis检验p值。
参数：qkruskal.Set_1, qkruskal.Set_2, ...
q-values corresponding to the Kruskal-Wallis test p-value (if input qValues==TRUE).
Q值对应的Kruskal-Wallis检验p值（如果输入qValues==TRUE“）。
参数：Z.Set1, Z.Set2, ...
Z statistics obtained from pvalueStudent.Set1, pvalueStudent.Set2, ... or from pvaluekruskal.Set1, pvaluekruskal.Set2, ..., depending on input metaKruskal.
从pvalueStudent.Set1, pvalueStudent.Set2, ...或pvaluekruskal.Set1, pvaluekruskal.Set2, ...Z统计量，根据输入metaKruskal。
For numeric traits, the following columns are returned:
对于数字特征，下面的列被返回：
参数：cor.Set_1, cor.Set_2, ...
correlations of all genes with the trait
的所有基因的相关性的特征
参数：Z.Set1, Z.Set2, ...
Fisher Z statistics corresponding to the correlations
费舍尔Z统计量对应的相关性
参数：pvalueStudent.Set_1, pvalueStudent.Set_2, ...
Student p-values of the correlations
学生的p值的相关性
参数：qvalueStudent.Set_1, qvalueStudent.Set_1, ...
(if input qValues==TRUE) q-values of the correlations calculated from the p-values
（如果输入qValues==TRUE）q中值的相关性计算出的p值
参数：AreaUnderROC.Set_1, AreaUnderROC.Set_2, ...
area under the ROC
的ROC曲线下面积
参数：nPresentSamples.Set_1, nPresentSamples.Set_2, ...
number of samples present for the calculation of each association.
存在的样本数为每个关联的计算。
（作者）----------Author(s)----------
Peter Langfelder
参考文献----------References----------
Soldier, Vol. 1: Adjustment during Army Life. Princeton University Press, Princeton.
approach, Journal of Evolutionary Biology 18:5 1368 (2005)
参见----------See Also----------
standardScreeningBinaryTrait, standardScreeningNumericTrait for screening functions for individual data sets
standardScreeningBinaryTrait，standardScreeningNumericTrait个人数据的筛选功能设置
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 21:16:44
