getVarianceStabilizedData(DESeq)
getVarianceStabilizedData()所属R语言包：DESeq
Perform a variance stabilising transformation (VST) on the count data
执行方差稳定计数数据转换（伟仕）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates a variance stabilising transformations (VST) from the fitted dispersion-mean reltions and then transforms the count data (after normalization by division by the size factor), yielding a matrix of values which are now approximately homoskedastic. This is useful as input to statistical analyses requiring homoskedasticity.
此函数计算方差，稳定的拟合分散平均reltions的转换（VST）的，然后转换计数数据（规模因素后，按分工标准化），产生一个矩阵现在大约homoskedastic的价值观。作为输入需要homoskedasticity的统计分析，这是有用的。
用法----------Usage----------
getVarianceStabilizedData(cds)
参数----------Arguments----------
参数：cds
a CountDataSet with estimated variance functions
与估计方差职能的CountDataSet，
Details
详情----------Details----------
For each sample (i.e., column of counts(cds)), the full variance function is calculated from the raw variance (by scaling according to the size factor and adding the shot noise). The function requires a blind estimate of the variance function, i.e., one ignoring conditions, and hence, you need to call estimateDispersions with method="blind" before calling it.
对于每个样本（即列counts(cds)），方差函数计算从原材料的方差（按比例根据规模因素和加入散粒噪声）。函数需要一个盲估计方差函数，即，一个不顾条件，因此，你需要调用estimateDispersionsmethod="blind"之前调用它。
If estimateDispersions was called with fitType="parametric", the following variance stabilizing transformation is used on the normalized count data, using the coefficients asymptDisp and extraPois from the dispersion fit:
如果estimateDispersions被称为fitType="parametric"，以下方差稳定改造规范化计数数据，利用系数asymptDisp和extraPois从色散适合：
vst( q ) = 2/log(2) * log( asymptDisp * sqrt(q) + asymptDisp * sqrt( 1 + extraPois + asymptDisp * q ) )
vst( q ) = 2/log(2) * log( asymptDisp * sqrt(q) + asymptDisp * sqrt( 1 + extraPois + asymptDisp * q ) )
This transformation is scaled such that for large counts, it become asymptotically equal to log2.
这种转变缩放等大型计数，它成为渐近等于到log2。
If estimateDispersions was called with fitType="locfit", the reciprocal of the square root of the variance of the normalized counts, as derived from the dispersion fit, is then numerically integrated up, and the integral (approximated by a spline function) evaluated for each count value in the column, yielding a transformed value. Note that the asymptotic property mentioned above does not hold in this case.
如果estimateDispersionsfitType="locfit"，归计数，从分散适合派生，方差的平方根的倒数，然后数值积分，积分（样条逼近被称为列中的每项罪名的价值评估功能），产生了转化的值。请注意，上面提到的渐近性质并不在这种情况下举行。
Limitations: In order to preserve normalization, the same transformation has to be used for all samples. This results in the variance stabilizition to be only approximate. The more the size factors differ, the more residual dependence of the variance on the mean you will find in the transformed data. (Compare the variance of the upper half of your transformed data with the lower half to see whether this is a problem in your case.)
限制：为了维护标准化，使用相同的转换，必须对所有样品。结果在方差stabilizition这到是唯一的近似。更多的大小的因素不同，平均方差剩余的依赖，你会发现在转换后的数据。 （比较方差的上半部下半部看到这是否是在你的情况中存在的问题与转化的数据。）
值----------Value----------
A matrix of the same dimension as the count data, containing the transformed data.
作为计数数据的同一维度的矩阵，包含转换后的数据。
作者（S）----------Author(s)----------
Simon Anders, sanders@fs.tum.de
举例----------Examples----------
cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateDispersions( cds, method="blind" )
vsd <- getVarianceStabilizedData( cds )
colsA <- conditions(cds) == "A"
plot( rank( rowMeans( vsd[,colsA] ) ), genefilter::rowVars( vsd[,colsA] ) )
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 16:32:27
