printtipWeights(limma)
printtipWeights()所属R语言包：limma
Sub-array Quality Weights
子数组质量重量
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates relative quality weights for each sub-array in a multi-array experiment.
估计每个子阵列在多阵列实验的质量相对权重。
用法----------Usage----------
printtipWeights(object, design = NULL, weights = NULL, method = "genebygene", layout, maxiter = 50, tol = 1e-10, trace=FALSE)
参数----------Arguments----------
参数：object
object of class numeric, matrix, MAList, marrayNorm, or ExpressionSet containing log-ratios or log-values of expression for a series of spotted microarrays.
对象的类numeric，matrix，MAList，marrayNorm或ExpressionSet含数比率或表达的一系列发现芯片log值。
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：layout
list specifying the dimensions of the spot matrix and the grid matrix. For details see PrintLayout-class.
列出指定的点矩阵和网格矩阵的尺寸。有关详情，请参阅PrintLayout-class。
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：trace
logical variable. If true then output diagnostic information at each iteration of '"reml"' algorithm.
逻辑变量。如果为true，则在每次迭代“REML”算法输出的诊断信息。
Details
详情----------Details----------
The relative reliability of each sub-array (print-tip group) is estimated by measuring how well the expression values for that sub-array follow the linear model.
通过测量子数组的表达式的值按照线性模型估计每个子阵列（打印头组）的相对可靠性。
The method described in Ritchie et al (2006) and implemented in the arrayWeights function is adapted for this purpose. 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 sub-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 the sub-array variances are converted to weights.
里奇等人（2006）描述的方法，并实施了arrayWeights功能是为适应这一目的。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，并成立有特定的子阵系数，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。转换到权重的子数组的差异最终估计。
The data object object is interpreted as for lmFit. In particular, the arguments design, weights and layout 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，weights和layout将提取的数据object如果有不正常的需要设置，在通话中明确;如有这些都是在通话设置，然后他们将过骑槽或组件中的数据object。
值----------Value----------
A matrix of sub-array weights which can be passed to lmFit.
一个子数组的权重矩阵可以通过lmFit。
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----------
## Not run: [＃无法运行：]
# This example is designed for work on a subset of the data[这个范例是专为数据的一个子集的工作]
# from ApoAI case study in Limma User's Guide[载脂蛋白AI在Limma用户的指南案例研究]
# This example was formerly loaded from sma package using[这个例子是以前从SMA使用包装]
# library(sma)[库（SMA）]
# data(MouseArray)[数据（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")
subarrayw <- printtipWeights(MA, design, layout=mouse.setup)
fit <- lmFit(MA, design, weights=subarrayw)
fit2 <- contrasts.fit(fit, contrasts=c(-1,1))
fit2 <- eBayes(fit2)
# Use of sub-array weights increases the significance of the top genes[使用子数组的重增加顶端基因的意义]
topTable(fit2)
# Create an image plot of sub-array weights from each array[从每个阵列创建子数组重形象图]
zlim <- c(min(subarrayw), max(subarrayw))
par(mfrow=c(3,2), mai=c(0.1,0.1,0.3,0.1))
for(i in 1:6)
imageplot(subarrayw[,i], layout=mouse.setup, zlim=zlim, main=paste("Array", i))
## End(Not run)[＃结束（不运行）]
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 2012-2-25 23:23:27
