vsn.old(vsn)
vsn.old()所属R语言包：vsn
Variance stabilization and calibration for microarray data.
微阵列数据的方差稳定和校准。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Robust estimation of variance-stabilizing and calibrating transformations for microarray data. This function has been superseded by vsn2. The function vsn remains in the package for backward
鲁棒估计方差稳定和校准的微阵列数据的转换。此功能已被取代vsn2。仍然是在落后的包的功能vsn
用法----------Usage----------
vsn(intensities,
lts.quantile = 0.5,
verbose = interactive(),
niter = 10,
cvg.check = NULL,
describe.preprocessing = TRUE,
subsample,
pstart,
strata)
参数----------Arguments----------
参数：intensities
An object that contains intensity values from a microarray experiment. The intensities are assumed to be the raw scanner data, summarized over the spots by an image analysis program, and possibly "background subtracted". The intensities must not be logarithmically or otherwise transformed, and not thresholded or "floored". NAs are not accepted. See details.
一个对象，它包含从微阵列实验的强度值。强度假定为原料的扫描数据，图像分析程序的斑点，并总结了可能“的背景中减去”。的强度，不能是对数或以其他方式转化，没有阈值值或“地板”。定居不被接受。查看详情。
参数：lts.quantile
Numeric. The quantile that is used for the resistant least trimmed sum of squares regression. Allowed values are between 0.5 and 1. A value of 1 corresponds to ordinary least sum of squares regression.
数字。修剪耐至少位数回归平方。允许的值是0.5和1之间。值为1相当于普通最小二乘回归平方。
参数：verbose
Logical. If TRUE, some messages are printed.
逻辑。如果是TRUE，一些消息被打印出来。
参数：niter
Integer. The number of iterations to be used in the least trimmed sum of squares regression.
整数。至少要使用迭代回归平方修剪。
参数：cvg.check
List. If non-NULL, this allows finer control of the iterative least trimmed sum of squares regression. See details.
名单。如果非NULL，这允许更精细的控制迭代至少修剪的回归平方和。查看详情。
参数：pstart
Array. If not missing, user can specify start values for the iterative parameter estimation algorithm. See vsnh for details.
阵列。如果没有丢失，用户可以指定开始值的迭代参数估计算法。看到vsnh详情。
参数：describe.preprocessing
Logical. If TRUE, calibration and transformation parameters, plus some other information are stored in the preprocessing slot of the returned object. See details.
逻辑。如果TRUE，校准和转换参数，再加上一些其他信息存储在preprocessing返回的对象插槽。查看详情。
参数：subsample
Integer. If specified, the model parameters are estimated from a subsample of the data only, the transformation is then applied to all data. This can be useful for performance reasons.
整数。如果指定了一个唯一的数据子样本估计模型参数，改造，然后应用到所有的数据。性能方面的原因，这可能是有用的。
参数：strata
Integer vector. Its length must be the same as nrow(intensities). This parameter allows for the calibration and error model parameters to be stratified within each array, e.g to take into account probe sequence properties, print-tip or plate effects. If strata is not specified, one pair of parameters is fitted for every sample (i.e. for every column of intensities). If strata is specified, a pair of parameters is fitted for every stratum within every sample. The strata are coded for by the different integer values. The integer vector strata can be obtained from a factor fac through as.integer(fac), from a character vector str through as.integer(factor(fac)).
整数向量。它的长度必须是相同为NROW（强）。此参数校准和误差模型参数允许每个阵列内分层，如考虑到帐户探针序列的特性，打印头或板效果。如果strata没有被指定，一对参数，安装每一个样本（即每列intensities）。如果strata指定，装有一对参数为每个阶层，每个样品内。地层编码由不同的整数值。整数向量strata可以从一个因素facas.integer(fac)，从一个字符向量stras.integer(factor(fac))。
Details
详情----------Details----------
Overview: The function calibrates for sample-to-sample variations through shifting and scaling, and transforms the intensities to a scale where the variance is approximately independent of the mean intensity. The variance stabilizing transformation is equivalent to the natural logarithm in the high-intensity range, and to a linear transformation in the low-intensity range. In an intermediate range, the arsinh function interpolates smoothly between the two. For details on the transformation, please see the help page for vsnh. The parameters are estimated through a robust variant of maximum likelihood. This assumes that for the majority of genes the expression levels are not much different across the samples, i.e., that only a minority of genes (less than a fraction 1-lts.quantile) is differentially expressed.
概述：该函数校准样品样本的变化，通过转移和扩大转换规模方差的平均强度约为独立的强度。方差稳定变换，相当于在高强度的自然对数，并在低强度范围内的线性变换。在中间范围，arsinh功能两者之间的插值顺利。改造的详细信息，请参阅vsnh帮助页。强大的变种，通过最大似然估计的参数。这是假设大多数基因的表达水平没有太大的跨样本的不同，也就是说，只有少数（小于分数1-lts.quantile）差异表达的基因。
Even if most genes on an array are differentially expressed, it may still be possible to use the estimator: if a set of non-differentially expressed genes is known, e.g. because they are external controls or reliable 'house-keeping genes', the transformation parameters can be fitted with vsn from the data of these genes, then the transformation can be applied to all data with vsnh.
即使数组的大多数基因的差异表达，它可能仍然是可以使用的估计：如果一套非差异表达的基因被称为，例如因为它们是外部控制或可靠的“看家基因”，转换参数，可以安装用vsn从这些基因数据，然后转换可以适用于vsnh所有的数据。
Format: The format of the matrix of intensities is as follows: for the two-color printed array technology, each row corresponds to one spot, and the columns to the different arrays and wave-lengths (usually red and green, but could be any number). For example, if there are 10 arrays, the matrix would have 20 columns, columns 1...10 containing the green intensities, and 11...20 the red ones. In fact, the ordering of the columns does not matter to vsn, but it is your responsibility to keep track of it for subsequent analyses. For one-color arrays, each row corresponds to a probe, and each column to an array.
格式：强度矩阵的格式如下：两色印刷阵列技术，每一行对应到一个地方，和不同的阵列和波长度（通常是红色和绿色的列，但可以是任何数）。例如，如果有10个阵列，矩阵将有20列，列1 ... 10包含绿色的强度，11 ... 20红色的。事实上，列的顺序并不重要到vsn，但它是你的责任，以保持它的后续分析的轨道。对于一个颜色数组，每一行对应一个探针，每到一个数组的列。
Performance: This function is slow. That is due to the nested iteration loops of the numerical optimization of the likelihood function and the heuristic that identifies the non-outlying data points in the least trimmed squares regression. For large arrays with many tens of thousands of probes, you may want to consider random subsetting: that is, only use a subset of the e.g. 10-20,000 rows of the data matrix intensities to fit the parameters, then apply the transformation to all the data, using vsnh. An example for this can be seen in the function normalize.AffyBatch.vsn, whose code you can inspect by typing normalize.AffyBatch.vsn on the R command line.
性能：此功能是缓慢的。这是由于似然函数的数值优化和启发式非外围数据点确定至少修剪最小二乘回归的嵌套迭代循环。对于许多几十成千上万的探针阵列，您可能要考虑随机子集，即只使用一个子集，如10-20,000行数据矩阵intensities适合的参数，然后应用转换的所有数据，使用vsnh。一个这样的例子可以看出在功能的normalize.AffyBatch.vsn，其代码，您可以检查输入normalize.AffyBatch.vsnR命令行。
Iteration control: By default, if cvg.check is NULL, the function will run the fixed number niter of iterations in the least trimmed sum of squares regression. More fine-grained control can be obtained by passing a list with elements eps and n. If the maximum change between transformed data values is smaller than eps for n subsequent iterations, then the iteration terminates.
迭代控制：默认情况下，如果cvg.check是NULL，功能将运行固定数量niter总和至少修剪最小二乘回归的迭代。更细粒度的控制，可以通过一个列表元素eps和n。如果转换后的数据值之间的最大变化是小于epsn后续迭代，迭代终止。
Estimated transformation parameters: If describe.preprocessing is TRUE, the transformation parameters are returned in the preprocessing slot of the experimentData slot of the resulting ExpressionSet object, in the form of a list with three elements
估计变换参数：如果describe.preprocessing的TRUE，转换参数返回preprocessing产生的experimentData对象的插槽插槽ExpressionSet，在list有三个元素的形式
vsnParams: the parameter array (see vsnh for details)
vsnParams：参数数组（见vsnh详情）
vsnParamsIter: an array with dimensions c(dim(vsnParams, niter)) that contains the parameter trajectory during the iterative fit process (see also vsnPlotPar).
与尺寸vsnParamsIter：的阵列c(dim(vsnParams, niter))包含参数的轨迹，在迭代拟合过程（也见vsnPlotPar）。
vsnTrimSelection: a logical vector that for each row of the intensities matrix reports whether it was below (TRUE) or above (FALSE) the trimming threshold.
vsnTrimSelection：一个逻辑向量强度矩阵的每一行的报告，无论是以下（TRUE）或以上（FALSE），修剪阈值。
If intensities has class ExpressionSet, and its experimentData slot has class MIAME, then this list is appended to any existing entries in the preprocessing slot. Otherwise, the experimentData object and its preprocessing slot are created.
如果intensities类ExpressionSet，其experimentData槽类MIAME，那么这个名单是追加到任何现有的preprocessing插槽条目。否则，创建experimentData对象及其preprocessing插槽。
值----------Value----------
An object of class ExpressionSet. Differences between the columns of the transformed intensities are "generalized log-ratios", which are shrinkage estimators of the natural logarithm of the fold change. For the transformation parameters, please see the Details.
对象类ExpressionSet。转化强度的列之间的差异是“广义数比率”，这是自然对数的倍数变化的收缩估计。为转换参数，请参阅详细资料。
作者（S）----------Author(s)----------
Wolfgang Huber
参考文献----------References----------
calibration and to the quantification of differential expression, Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, Martin Vingron; Bioinformatics (2002) 18 Suppl.1 S96-S104.
of microarray data, Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, and Martin Vingron; Statistical Applications in Genetics and Molecular Biology (2003) Vol. 2 No. 1, Article 3.
参见----------See Also----------
vsnh, vsnPlotPar, ExpressionSet-class, MIAME-class,
vsnh，vsnPlotPar，ExpressionSet-class，MIAME-class
举例----------Examples----------
data(kidney)
log.na = function(x) log(ifelse(x>0, x, NA))
plot(log.na(exprs(kidney)), pch=".", main="log-log")
vsnkid = vsn(kidney) ## transform and calibrate[＃转换和校准]
plot(exprs(vsnkid), pch=".", main="h-h")
meanSdPlot(vsnkid)
## this should always hold true[＃这应该始终坚持真]
params = preproc(description(vsnkid))$vsnParams
stopifnot(all(vsnh(exprs(kidney), params) == exprs(vsnkid)))
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 2012-2-26 16:01:02
