voom(limma)
voom()所属R语言包：limma
Transform RNA-Seq Data Ready for Linear Modelling
转换RNA序列数据线性模型准备就绪
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Transform count data to log2-counts per million, estimate the mean-variance relationship and use this to compute appropriate observational-level weights. The data are then ready for linear modelling.
转换到log2数数每百万数据，估计的均值 - 方差关系，并以此来计算适当的观测级重量。然后，这些数据是线性模型的准备。
用法----------Usage----------
voom(counts, design = NULL, lib.size = NULL, normalize.method = "none", plot = FALSE, ...)
参数----------Arguments----------
参数：counts
either a numeric matrix containing raw counts or a DGEList object.
无论是数字matrix包含原始计数或DGEList对象的。
参数：design
design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates.
设计矩阵估计系数的样品和列对应的行。默认为单位向量的含义，样本被视为重复。
参数：lib.size
numeric vector containing total librazy sizes for each sample. If NULL, library sizes are calculated as column sums of counts.
数字向量，对每一个样品的的总librazy大小。如果NULL，库大小计算列counts款项。
参数：normalize.method
normalization method to be applied to the log2-counts-per-million. Choices are as for the method argument of normalizeBetweenArrays when the data is single-channel.
标准化的方法被应用到的log2-计数每百万。选择是为methodnormalizeBetweenArrays参数时的数据是单声道。
参数：plot
logical value indicating whether a plot of mean-variance trend is displayed.
logical值，指示是否显示均值 - 方差走势图。
参数：...
other arguments are passed to lmFit.
其他参数被传递到lmFit。
Details
详情----------Details----------
This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
此功能的目的是处理RNA序列或芯片SEQ数据之前在limma的线性建模。
voom is an acronym for mean-variance modelling at the observational level. The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. The weights are then used in the linear modelling process to adjust for heteroscedasticity.
voom是一个均值 - 方差模型在观测水平的缩写。关键问题是，估计在数据的均值 - 方差关系，然后使用适当的权重来计算每个观察。计算数据表明几乎不平凡的均值 - 方差关系。原始计数表明增加数大小越来越多的差异，同时记录计数通常表现出下降的均值 - 方差的趋势。这个功能估计log数的均值 - 方差的趋势，然后分配一个权重根据其预测方差每个观测。线性建模过程中的权重，然后用异方差调整。
In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
在一项实验中，计数的值观察每个样本中的每个标签。一个明智的标签均值 - 方差的趋势是使用lowess计算。标签明智的平均值是跨越一个给定的标签样本，平均偏移0.5的log2计数。标签明智的方差是规范化的log2计数每百万值的四分之一根方差偏移0.5跨越一个给定的标签样本，。零数在所有样品的标签不包括在LOWESS拟合。可选的标准化是使用normalizeBetweenArrays。使用log2计数的拟合值从lmFit，从均值方差趋势差异的线性模型的拟合插值每个观察。这是开展approxfun的。逆差额重量可以用来纠正均值 - 方差计数数据的趋势。
值----------Value----------
An EList object with the following components:
以下组件EList对象：
参数：E
numeric matrix of normalized expression values on the log2 scale
数字矩阵的log2规模的规范化表达式的值
参数：weights
numeric matrix of inverse variance weights
数字矩阵的逆差额重量
参数：design
numeric matrix of experimental design
实验设计的数字矩阵
参数：lib.size
numeric vector of total library sizes
总库大小的数值向量
参数：genes
dataframe of gene annotation, only if counts was a DGEList object
dataframe基因注释，只有counts的是DGEList对象
作者（S）----------Author(s)----------
Charity Law and Gordon Smyth
参见----------See Also----------
normalizeBetweenArrays
normalizeBetweenArrays
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-25 23:29:10
