normalizeBetweenArrays(limma)
normalizeBetweenArrays()所属R语言包：limma
Normalize Between Arrays
标准化阵列之间
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Normalizes expression intensities so that the intensities or log-ratios have similar distributions across a set of arrays.
规范化的表达强度，这样的强度或数比率有类似的分布横跨一组阵列。
用法----------Usage----------
normalizeBetweenArrays(object, method=NULL, targets=NULL, cyclic.method="pairs", ...)
参数----------Arguments----------
参数：object
a numeric matrix, EListRaw, RGList or MAList object.
数字matrix，EListRaw，RGList或MAList对象。
参数：method
character string specifying the normalization method to be used. Choices are "none", "scale", "quantile", "Aquantile", "Gquantile", "Rquantile" or "Tquantile" or "cyclicloess". A partial string sufficient to uniquely identify the choice is permitted. Default is "Aquantile" for two-color data objects or "quantile" for single-channel objects.
字符串指定要使用的规范化方法。选择是"none"，"scale"，"quantile"，"Aquantile"，"Gquantile"，"Rquantile"或"Tquantile"或"cyclicloess"。部分字符串足以唯一标识的选择是允许的。默认是"Aquantile"两色数据对象或"quantile"单通道的对象的。
参数：targets
vector, factor or matrix of length twice the number of arrays, used to indicate target groups if method="Tquantile"
向量长度的因素或矩阵阵列数的两倍，用来指示目标群体如果method="Tquantile"
参数：cyclic.method
character string indicating the variant of normalizeCyclicLoess to be used if method=="cyclicloess", see normalizeCyclicLoess for possible values.
字符串表示变异normalizeCyclicLoess如果method=="cyclicloess"，看到的可能值normalizeCyclicLoess。
参数：...
other arguments are passed to normalizeQuantiles or normalizeCyclicLoess
其他参数传递给normalizeQuantiles或normalizeCyclicLoess
Details
详情----------Details----------
normalizeWithinArrays normalizes expression values to make intensities consistent within each array. normalizeBetweenArrays normalizes expression values to achieve consistency between arrays. For two-color arrays, normalization between arrays usually occurs after normalization within arrays. For single-channel arrays, within array normalization is not usually relevant.
normalizeWithinArrays标准化表达式的值，使每个阵列内强度一致。 normalizeBetweenArrays规范化表达式的值来实现阵列之间的一致性。为双色阵列，在阵列之间标准化后，通常会出现在阵列标准化。对于单通道数组，数组内的标准化通常不相关。
If object is a matrix then the scale, quantile or cyclic loess normalization will be applied to the columns. Trying to apply other normalization methods when object is a matrix will produce an error. If object is an EListRaw object, then normalization will be applied to the matrix object$E of expression values, which will then be log2-transformed. Scale (method="scale") scales the columns to have the same median. Quantile and cyclic loess normalization was originally proposed by Bolstad et al (2003) for Affymetrix-style single-channel arrays. Quantile normalization forces the entire empirical distribution of each column to be identical. Cyclic loess normalization applies loess normalization to all possible pairs of arrays, usually cycling through all pairs several times. Cyclic loess is slower than quantile, but allows probe-wise weights and is more robust to unbalanced differential expression.
如果object是matrix然后规模化，位数循环黄土标准化将被应用到列。尝试申请其他标准化方法时object会产生错误的matrix。 object如果是EListRaw对象，然后标准化将应用矩阵object$E表达式的值，这将是转化的log2。级表（method="scale"）鳞列有相同的中位数。最初是由Affymetrix公司风格的单声道阵列Bolstad等人（2003）分量和循环黄土标准化。位数标准化迫使每一列的整个经验分布是相同的。循环黄土标准化适用于黄土标准化，对所有可能的阵列，通常是通过所有对循环几次。环黄土比位数慢，但允许探针明智的重量和更强大的不平衡差异表达。
The other normalization methods are for two-color arrays. Scale normalization was proposed by Yang et al (2001, 2002) and is further explained by Smyth and Speed (2003). The idea is simply to scale the log-ratios to have the same median-abolute-deviation (MAD) across arrays. This idea has also been implemented by the maNormScale function in the marray package. The implementation here is slightly different in that the MAD scale estimator is replaced with the median-absolute-value and the A-values are normalized as well as the M-values.
其他标准化的方法是两色的阵列。规模标准化，提出了由杨等人（2001年，2002年）和Smyth和高速（2003）进一步解释。这个想法简直是扩展log比有相同的中位数abolute偏差（MAD）的跨阵列。这个想法也得到了在功能的marray包maNormScale实施。这里的实施是在MAD规模估计与中位数的绝对价值和标准化的A值以及m值取代略有不同。
Quantile normalization was explored by Yang and Thorne (2003) for two-color cDNA arrays. method="quantile" ensures that the intensities have the same empirical distribution across arrays and across channels. method="Aquantile" ensures that the A-values (average intensities) have the same empirical distribution across arrays leaving the M-values (log-ratios) unchanged. These two methods are called "q" and "Aq" respectively in Yang and Thorne (2003).
杨和Thorne（2003）探讨两色的cDNA阵列位数标准化。 method="quantile"确保强度具有相同的跨阵列，跨渠道的经验分布。 method="Aquantile"确保的A值（平均强度）具有相同的跨越留下的M值（log比率）不变阵列的经验分布。这两种方法被称为“Q”和“AQ”分别在杨和Thorne（2003）。
method="Tquantile" performs quantile normalization separately for the groups indicated by targets. targets may be a target frame such as read by readTargets or can be a vector indicating green channel groups followed by red channel groups.
method="Tquantile"分别表示targets组执行位数的标准化。 targets可能是如读readTargets也可以是一个向量表示绿色通道，红色通道团体组的目标框架。
method="Gquantile" ensures that the green (first) channel has the same empirical distribution across arrays, leaving the M-values (log-ratios) unchanged. This method might be used when the green channel is a common reference throughout the experiment. In such a case the green channel represents the same target throughout, so it makes compelling sense to force the distribution of intensities to be same for the green channel on all the arrays, and to adjust to the red channel accordingly. method="Rquantile" ensures that the red (second) channel has the same empirical distribution across arrays, leaving the M-values (log-ratios) unchanged. Both Gquantile and Rquantile normalization have the implicit effect of changing the red and green log-intensities by equal amounts.
method="Gquantile"确保绿色通道（第一），有相同的经验分布在阵列，留下的M值（数比率）不变。这种方法可能被用来当绿色通道是整个实验过程中的共同参考。绿色通道，在这种情况下，代表整个相同的目标，所以它是令人信服的意识，强制的强度分布是相同的绿色通道上所有阵列，并相应调整红色通道。 method="Rquantile"确保红色（二）通道具有相同的经验分布在阵列，留下的M值（log的比率）保持不变。既Gquantile和Rquantile标准化改变等量的红色和绿色的登陆，强度隐效果。
See the limma User's Guide for more examples of use of this function.
使用此功能的更多例子参见的limma用户指南。
值----------Value----------
If object is a matrix then normalizeBetweenArrays produces a matrix of the same size. If object is an EListRaw object, then an EList object with expression values on the log2 scale is produced. For two-color data, normalizeBetweenArrays produces an MAList object with M and A-values on the log2 scale.
如果object是一个矩阵，那么normalizeBetweenArrays产生一个同样大小的矩阵。 object如果是EListRaw对象，然后EList表达式的值对象的log2规模生产。两色数据，normalizeBetweenArrays生产MAList M和A值的log2规模的对象。
作者（S）----------Author(s)----------
Gordon Smyth
参考文献----------References----------
In: D. R. Goldstein (ed.), Science and Statistics: A Festschrift for Terry Speed, IMS Lecture Notes - Monograph Series, Volume 40, pp. 403-418.
参见----------See Also----------
An overview of LIMMA functions for normalization is given in 05.Normalization.
在05.Normalization标准化LIMMA功能概述。
Note that vsn normalization, previously offered as a method of this function, is now performed by the normalizeVSN function.
注意：提供此功能的方法，是的VSN标准化，以前是现在执行normalizeVSN功能。
See also maNormScale in the marray package and normalize in the affy package.
也见maNormScale在marray包和normalize在affy包。
举例----------Examples----------
ngenes <- 100
narrays <- 4
x <- matrix(rnorm(ngenes*narrays),100,4)
y <- normalizeBetweenArrays(x)
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。
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发表于 2012-2-25 23:19:53
