vsn2(vsn)
vsn2()所属R语言包：vsn
Fit the vsn model
适合VSN模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
vsn2 fits the vsn model to the data in x and returns a vsn object with the fit parameters and the transformed data matrix. The data are, typically, feature intensity readings from a microarray, but this function may also be useful for other kinds of intensity data that obey an additive-multiplicative error model. To obtain an object of the same class as x, containing the normalised data and the same metdata as x, use
vsn2适合x数据的VSN模型，并返回一个vsn对象的拟合参数和转换后的数据矩阵。数据是，通常情况下，从一个芯片的功能强度读数，但这个功能也可能是有用的其他种类服从添加剂乘误差模型的强度数据。要获得x，规范化的数据和相同metdata的含有x，使用相同的类的对象
用法----------Usage----------
vsnMatrix(x,
reference,
strata,
lts.quantile = 0.9,
subsample = 0L,
verbose = interactive(),
returnData = TRUE,
calib = "affine",
pstart,
minDataPointsPerStratum = 42L,
optimpar = list(),
defaultpar = list(factr=5e7, pgtol=2e-4, maxit=60000L,
trace=0L, cvg.niter=7L, cvg.eps=0))
## S4 method for signature 'ExpressionSet'
vsn2(x, reference, strata, ...)
## S4 method for signature 'AffyBatch'
vsn2(x, reference, strata, subsample, ...)
## S4 method for signature 'NChannelSet'
vsn2(x, reference, strata, backgroundsubtract=FALSE,
foreground=c("R","G"), background=c("Rb", "Gb"), ...)
## S4 method for signature 'RGList'
vsn2(x, reference, strata, ...)
参数----------Arguments----------
参数：x
An object containing the data to which the model is fitted.
一个对象，其中包含该模型拟合数据。
参数：reference
Optional, a vsn object from a previous fit. If this argument is specified, the data in x are normalized "towards" an existing set of reference arrays whose parameters are stored in the object reference. If this argument is not specified, then the data in x are normalized "among themselves". See Details for a more precise explanation.
可选的，一个vsn对象从以前的契合。如果此参数指定，x的数据是标准化“走向”参考阵列的参数存储对象reference在现有的一套。如果这个参数没有被指定，那么x数据进行归一“彼此”。看到一个更精确的解释详情。
参数：strata
Optional, a factor or integer whose length is nrow(x). It can be used for stratified normalization (i.e. separate offsets a and factors b for each level of strata). If missing, all rows of x are assumed to come from one stratum. If strata is an integer, its values must cover the range 1,…,n, where n is the number of strata.
可选的，一个factor或integer长度nrow(x)的。它可用于分层标准化（即独立的抵消a和b每strata级）因素。如果丢失，所有的行x假设来自一个阶层。 strata如果是一个整数，其值必须覆盖范围1,…,n，n是阶层的数量。
参数：lts.quantile
Numeric of length 1. 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.
长度为1的数字。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：subsample
Integer of length 1. If its value is greater than 0, the model parameters are estimated from a subsample of the data of size subsample only, yet the fitted transformation is then applied to all data. For large datasets, this can substantially reduce the CPU time and memory consumption at a negligible loss of precision. Note that the AffyBatch method of vsn2 sets a value of 30000 for this parameter if it is missing from the function call - which is different from the behaviour of the other methods.
长度为1的整数。如果其值大于0，从一个子样本大小的数据模型参数估计subsample，但装改造，然后应用到所有的数据。对于大型数据集，这样可以大大减少CPU时间和内存消耗可以忽略不计的精度损失。请注意，AffyBatch vsn2方法设置一个值30000这个参数，如果它是从函数调用中缺少 - 这是不同于其他方法的行为。
参数：backgroundsubtract
Logical of length 1: should local background estimates be subtracted before fitting vsn?
长度为1的逻辑：当地的背景估计应减去前拟合VSN？
参数：foreground, background
Aligned character vectors of the same length, naming the channels of x that should be used as foreground and background values.
不结盟相同长度的特征向量，命名的渠道x应作为前景和背景值。
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：returnData
Logical. If TRUE, the transformed data are returned in a slot of the resulting vsn object. Setting this option to FALSE allows saving memory if the data are not needed.
逻辑。如果是TRUE，返回转换后的数据在生成的vsn对象的槽。将此选项设置为FALSE可以节省内存，如果不需要的数据。
参数：calib
Character of length 1. Allowed values are affine and none. The default, affine, corresponds to the behaviour in package versions <= 3.9, and to what is described in references [1] and [2]. The option none is an experimental new feature, in which no affine calibration is performed and only two global variance stabilisation transformation parameters a and b are fitted. This functionality might be useful in conjunction with other calibration methods, such as quantile normalisation - see the vignette Introduction to vsn.
字符长度为1。允许的值是affine和none。默认情况下，affine，包版本对应的行为<= 3.9，什么是在参考文献中描述的[1] [2]。选项none是一个实验性的新功能，在没有进行仿射校准和全球只有两个方差稳定转换参数a和b装有。此功能可能是有用的，在与其他标定方法，如位数标准化，一起 - 看到的小插曲介绍VSN。
参数：pstart
Optional, a three-dimensional numeric array that specifies start values for the iterative parameter estimation algorithm. If not specified, the function tries to guess useful start values. The first dimension corresponds to the levels of strata, the second dimension to the columns of x and the third dimension must be 2, corresponding to offsets and factors.
可选的，一个立体的数字阵列，指定迭代参数估计算法的初始值。如果没有指定，函数试图猜测有益的开端值。第一维对应的水平strata，第二维列x“第三维必须是2，相应的偏移和因素。
参数：minDataPointsPerStratum
The minimum number of data points per stratum. Normally there is no need for the user to change this; refer to the vignette for further documentation.
每个阶层的数据点的最低数量。通常没有用户的需要，要改变这种进一步的文件的小插曲。
参数：optimpar
Optional, a list with parameters for the likelihood optimisation algorithm. Default parameters are taken from defaultpar. See details.
可选的，带有参数列表的可能性优化算法。从defaultpar默认参数。查看详情。
参数：defaultpar
The default parameters for the likelihood optimisation algorithm. Values in optimpar take precedence over those in defaultpar. The purpose of this argument is to expose the default values in this manual page - it is not intended to be changed, please use optimpar for that.
默认参数的可能性优化算法。 optimpar接管那些在defaultpar优先的价值。这种说法的目的是为了揭露在本手册页的默认值 - 它的目的不是要改变，请使用optimpar。
参数：...
Arguments that get passed on to vsnMatrix.
获得通过vsnMatrix的论据。
值----------Value----------
An object of class vsn.
对象类vsn。
The data are returned on a glog scale to base 2. More precisely, the transformed data are subject to the transformation glog_2(f(b)*x+a) + c, where the function glog_2(u) = log_2(u+√{u*u+1}) = asinh(u)/\log(2) is called the generalised logarithm, the offset a and the scaling parameter b are the fitted model parameters (see references), and f(x)=\exp(x) is a parameter transformation that allows ensuring positivity of the factor in front of x while using an unconstrained optimisation over b [4]. The overall offset c is computed from the b's such that for large x the transformation approximately corresponds to the \log_2 function. This is done separately for each stratum, but with the same value across arrays. More precisely, if the element b[s,i] of the array b is the scaling parameter for the s-th stratum and the i-th array, then c[s] is computed as log2(2*f(mean(b[,i]))). The offset c is inconsequential for all differential expression calculations, but many users like to see the data in a range that they are familiar with.
数据返回碱基2glog规模。更精确，转换后的数据改造glog_2(f(b)*x+a) + c，其中的功能glog_2(u) = log_2(u+√{u*u+1}) = asinh(u)/\log(2)被称为广义的对数，偏移a和缩放参数b拟合模型参数（请参阅参考资料），f(x)=\exp(x)是一个参数转换，允许而使用无约束优化，确保积极的因素，在前面的xb[4]。整体偏移的cb等，计算大x改造约相当于\log_2函数。这是各阶层，但与整个数组的值相同。更确切地说，如果该元素b[s,i]数组b-TH阶层s缩放参数和-TH i阵列，然后c[s]计算 log2(2*f(mean(b[,i])))。偏移c是所有差异表达计算无关紧要，但许多用户希望看到他们所熟悉的在一定范围内的数据。
不同方法的具体行为----------Specific behaviour of the different methods----------
vsn2 methods exist for ExpressionSet, NChannelSet, AffyBatch (from the affy package), RGList (from the limma package), matrix and numeric. If x is an NChannelSet, then vsn2 is applied to the matrix that is obtained by horizontally concatenating the color channels. Optionally, available background estimates can be subtracted before. If x is an RGList, it is converted into an NChannelSet using a copy of Martin Morgan's code for RGList to NChannelSet coercion, then the NChannelSet method is called.
存在vsn2方法ExpressionSet，NChannelSet，AffyBatch（affy包），RGList（limma包），matrix和numeric。如果x是NChannelSet，vsn2应用水平串联颜色通道得到的矩阵。可选，可用的背景估计可以减去前。如果x是RGList，它被转换成一个NChannelSetRGListNChannelSet胁迫的马丁·摩根的代码的副本，然后<X >方法被调用。
独立与参考标准化----------Standalone versus reference normalisation----------
If the reference argument is not specified, then the model parameters μ_k and σ are fit from the data in x. This is the mode of operation described in [1] and that was the only option in versions 1.X of this package. If reference is specified, the model parameters μ_k and σ are taken from it.
如果没有指定reference参数，然后对模型参数μ_k和σx数据适合。这是[1]和唯一的选择是在1.X版本的此包中所描述的运作模式。 reference如果指定，模型参数μ_k和σ采取。
收敛迭代的可能性优化----------Convergence of the iterative likelihood optimisation----------
L-BFGS-B uses three termination criteria:
L-BFGS-B使用三种终止条件：
(f_k - f_{k+1}) / max(|f_k|, |f_{k+1}|, 1) <= factr * epsmch where epsmch is the machine precision.
(f_k - f_{k+1}) / max(|f_k|, |f_{k+1}|, 1) <= factr * epsmchepsmch是机器精度。
|gradient| < pgtol
|gradient| < pgtol
iterations > maxit
iterations > maxit
These are set by the elements factr, pgtol and maxit of optimpar. The remaining elements are
这些元素设置factr，pgtol和maxitoptimpar。剩余的元素
trace An integer between 0 and 6, indicating the verbosity level of L-BFGS-B, higher values
trace0和6之间的整数，说明L-BFGS-B，值越高的冗赘级别
cvg.niter The number of iterations to be used in the least
cvg.niter至少要使用迭代次数
cvg.eps Numeric. A convergence threshold for the least
cvg.eps数字。最不收敛阈值
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. http://www.bepress.com/sagmb/vol2/iss1/art3.
Optimization, C. Zhu, R.H. Byrd, P. Lu and J. Nocedal, Technical Report, Northwestern University (1996).
参见----------See Also----------
justvsn, predict
justvsn，predict
举例----------Examples----------
data("kidney")
fit = vsn2(kidney) ## fit[＃适合]
nkid = predict(fit, newdata=kidney) ## apply fit[＃适用于适合]
plot(exprs(nkid), pch=".")
abline(a=0, b=1, col="red")
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 2012-2-26 16:01:18
