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

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arrayWeights(limma)
arrayWeights()所属R语言包：limma

                                        Array Quality Weights
                                         阵列的质量权重

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Estimates relative quality weights for each array in a multi-array experiment.
估计每个阵列在一个多阵列实验的质量相对权重。


用法----------Usage----------


arrayWeights(object, design = NULL, weights = NULL, method = "genebygene", maxiter = 50, tol = 1e-10, trace=FALSE)
arrayWeightsSimple(object, design = NULL, maxiter = 100, tol = 1e-6, maxratio = 100, trace=FALSE)



参数----------Arguments----------

参数：object
object of class numeric, matrix, MAList, marrayNorm, ExpressionSet or PLMset containing log-ratios or log-values of expression for a series of microarrays.
对象类numeric，matrix，MAList，marrayNorm，ExpressionSet或PLMset含数比率或表达一个log值系列芯片。


参数：design
the design matrix of the microarray experiment, with rows corresponding to arrays and columns to coefficients to be estimated.  Defaults to the unit vector meaning that the arrays are treated as replicates.
芯片实验设计矩阵，与估计的系数阵列和列对应的行。默认的单位向量的含义，阵列被视为重复。


参数：weights
optional numeric matrix containing prior weights for each spot.
可选的数字矩阵，每个点前的重量。


参数：method
character string specifying the estimating algorithm to be used. Choices are "genebygene" and "reml".
字符串指定要使用的估计算法。选择是"genebygene"和"reml"。


参数：maxiter
maximum number of iterations allowed.
迭代允许的最大数量。


参数：tol
convergence tolerance.
收敛公差。


参数：maxratio
maximum ratio between largest and smallest weights before iteration stops
最大和最小重量之间的最大比前迭代停止


参数：trace
logical variable. If true then output diagnostic information at each iteration of the '"reml"' algorithm, or at every 1000th iteration of the  '"genebygene"' algorithm.
逻辑变量。如果真，那么“REML”的算法，或“”genebygene 1000在每一个迭代算法的每次迭代输出诊断信息。


Details

详情----------Details----------

The relative reliability of each array is estimated by measuring how well the expression values for that array follow the linear model.
通过测量阵列的表达值按照线性模型估计每个阵列的相对可靠性。

The method is described in Ritchie et al (2006). A heteroscedastic model is fitted to the expression values for  each gene by calling the function lm.wfit.  The dispersion model  is fitted to the squared residuals from the mean fit, and is set up to  have array specific coefficients, which are updated in either full REML  scoring iterations, or using an efficient gene-by-gene update algorithm.   The final estimates of these array variances are converted to weights.
里奇等人（2006）描述的方法。异方差模型被安装到每一个基因的表达值，通过调用函数lm.wfit。装有扩散模型的平均拟合的残差平方，设置阵列中的特定系数，REML法要么全得分迭代更新，或者使用一个高效的基因通过基因的更新算法。最终这些阵列方差估计转换权。

The data object object is interpreted as for lmFit. In particular, the arguments design and weights will be extracted from the data  object if available and do not normally need to be set explicitly in  the call; if any of these are set in the call then they will over-ride  the slots or components in the data object.
数据对象object解释作为lmFit。 ，特别是参数design和weights将提取的数据object如果有不正常的需要设置，在通话中明确;如果其中任何载于叫那么他们将骑槽或数据object组件。

arrayWeightsSimple is a fast version of arrayWeights with method="reml", no prior weights and no missing values.
arrayWeightsSimple是一个快速版本的arrayWeightsmethod="reml"，事先没有重量，没有缺失值。


值----------Value----------

A vector of array weights.
阵列权重向量。


作者（S）----------Author(s)----------


Matthew Ritchie and Gordon Smyth



参考文献----------References----------



参见----------See Also----------

An overview of linear model functions in limma is given by 06.LinearModels.
线性模型功能概述limma由06.LinearModels给出。


举例----------Examples----------


if(require("sma")) {
# Subset of data from ApoAI case study in Limma User's Guide[在Limma用户指南apoAⅠ的案例研究的数据子集]
# Can be loaded from sma package if it is installed[如果它安装，可装载SMA封装]
data(MouseArray)
# Avoid non-positive intensities[避免非积极的强度]
RG <- backgroundCorrect(mouse.data, method="half")
MA <- normalizeWithinArrays(RG, mouse.setup)
MA <- normalizeBetweenArrays(MA, method="Aq")
targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
design <- modelMatrix(targets, ref="Pool")
arrayw <- arrayWeightsSimple(MA, design)
fit <- lmFit(MA, design, weights=arrayw)
fit2 <- contrasts.fit(fit, contrasts=c(-1,1))
fit2 <- eBayes(fit2)
# Use of array weights increases the significance of the top genes[的阵列使用权增加顶端基因的意义]
topTable(fit2)
}

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


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