dglmStdResid(edgeR)
dglmStdResid()所属R语言包：edgeR
Visualize the mean-variance relationship in DGE data using standardized residuals
胃排空的数据可视化的均值 - 方差关系，使用标准化残差
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Appropriate modelling of the mean-variance relationship in DGE data is important for making inferences about differential expression. However, the standard approach to visualizing the mean-variance relationship is not appropriate for general, complicated experimental designs that require generalized linear models (GLMs) for analysis. Here are functions to compute standardized residuals from a Poisson GLM and plot them for bins based on overall expression level of tags as a way to visualize the mean-variance relationship. A rough estimate of the dispersion parameter can also be obtained from the standardized residuals.
适当的建模在胃排空数据的均值 - 方差关系是非常重要的差异表达的推论。然而，标准的做法，以可视化的均值 - 方差关系是不适合一般情况下，复杂的，需要进行分析的广义线性模型（GLMs）的实验设计。这里是从泊松的GLM计算标准化残差，并绘制这些桶上的标签作为一种可视化的均值 - 方差关系的整体表达水平的功能。一个粗略估计的分散参数，也可以从标准化残差。
用法----------Usage----------
dglmStdResid(y, design, dispersion=0, offset=0, nbins=100, make.plot=TRUE, xlab="Mean", ylab="Ave. binned standardized residual", ...)
getDispersions(binned.object)
参数----------Arguments----------
参数：y
numeric matrix of counts, each row represents one tag, each column represents one DGE library.
计数的数字矩阵，每一行代表一个标签，每一列代表一个胃排空库。
参数：design
numeric matrix giving the design matrix of the GLM. Assumed to be full column rank.
数字矩阵提供的GLM的设计矩阵。假定为列满秩。
参数：dispersion
numeric scalar or vector giving the dispersion parameter for each GLM. Can be a scalar giving one value for all tags, or a vector of length equal to the number of tags giving tag-wise dispersions.
数字标量或矢量的GLM为每个分散参数。可以给予所有标签的价值，是一个标量或向量的长度等于给标签明智的分散的标签的数量。
参数：offset
numeric vector or matrix giving the offset that is to be included in teh log-linear model predictor. Can be a vector of length equal to the number of libraries, or a matrix of the same size as y.
数值向量或矩阵给予抵消，是要在格兰数线性模型预测。可以是一个长度等于库的数量，或为y同样大小的矩阵向量。
参数：nbins
scalar giving the number of bins (formed by using the quantiles of the genewise mean expression levels) for which to compute average means and variances for exploring the mean-variance relationship. Default is 100 bins
标提供的箱数计算平均的均值和方差探索均值 - 方差关系（使用2-6。平均表达水平的位数所形成）。默认是100箱
参数：make.plot
logical, whether or not to plot the mean standardized residual for binned data (binned on expression level). Provides a visualization of the mean-variance relationship. Default is TRUE.
逻辑，是否绘制分级数据（分级表达水平）的平均标准化残差。提供了一个可视化的均值 - 方差关系。默认TRUE。
参数：xlab
character string giving the label for the x-axis. Standard graphical parameter. If left as the default, then the x-axis label will be set to "Mean".
字符串x轴的标签。标准的图形参数。如果作为默认的离开，然后将X轴标签设置为“中庸”。
参数：ylab
character string giving the label for the y-axis. Standard graphical parameter. If left as the default, then the y-axis label will be set to "Ave. binned standardized residual".
字符串y轴的标签。标准的图形参数。如果离开作为默认，然后Y轴标签将被设置为“大道。分级标准化的残余“。
参数：...
further arguments passed on to plot
通过进一步的论据plot
参数：binned.object
list object, which is the output of dglmStdResid.
列表中的对象，这是dglmStdResid输出。
Details
详情----------Details----------
This function is useful for exploring the mean-variance relationship in the data. Raw or pooled variances cannot be used for complex experimental designs, so instead we can fit a Poisson model using the appropriate design matrix to each tag and use the standardized residuals in place of the pooled variance (as in plotMeanVar) to visualize the mean-variance relationship in the data. The function will plot the average standardized residual for observations split into nbins bins by overall expression level. This provides a useful summary of how the variance of the counts change with respect to average expression level (abundance). A line showing the Poisson mean-variance relationship (mean equals variance) is always shown to illustrate how the genewise variances may differ from a Poisson mean-variance relationship. A log-log scale is used for the plot.
此功能是有益的探索在数据的均值 - 方差关系。原料或汇集的差异不能被用于复杂的实验设计，所以我们可以使用适当的设计矩阵，每个标签符合泊松模型和使用汇集方差标准化残差（plotMeanVar），可视化在数据的均值 - 方差关系。函数将绘制分割成nbins箱整体表达水平的平均观测标准化残差。这提供了一个如何计数的方差与平均表达水平（丰）改变了有用的总结。泊松均值 - 方差关系（平均等于方差）A线始终显示2-6。差异来说明如何从泊松均值 - 方差关系可能会有所不同。图用于log记录的规模。
The function mglmLS is used to fit the Poisson models to the data. This code is fast for fitting models, but does not compute the value for the leverage, technically required to compute the standardized residuals. Here, we approximate the standardized residuals by replacing the usual denominator of ( 1 - leverage ) by ( 1 - p/n ) , where n is the number of observations per tag (i.e. number of libraries) and p is the number of parameters in the model (i.e. number of columns in the full-rank design matrix.
功能mglmLS用于符合泊松模型的数据。此代码是快拟合模型，但不计算价值的杠杆作用，在技术上需要计算标准化残差。在这里，我们近似代替通常的分母的标准化残差 ( 1 - leverage ) ( 1 - p/n ) ，其中n是观察每个标签的数量（即图书馆）和p是模型中的参数数量（即在设计矩阵满秩的列数。
值----------Value----------
dglmStdResid produces a mean-variance plot based on standardized residuals from a Poisson model fitfor each tag for the DGE data. dglmStdResid returns a list with the following elements:
dglmStdResid产生一个基于从泊松模型fitfor的每个标签胃排空数据的标准化残差的均值 - 方差的图。 dglmStdResid返回一个包含下列元素的列表：
参数：ave.means
vector of the average expression level within each bin of observations
每个观测的垃圾桶内的平均表达水平的向量
参数：ave.std.resid
vector of the average standardized Poisson residual within each bin of tags
每个标签的垃圾桶内平均标准化泊松剩余的向量
参数：bin.means
list containing the average (mean) expression level (given by the fitted value from the given Poisson model) for observations divided into bins based on amount of expression
列表，其中包含的平均水平（平均）的表达水平（从给定的泊松模型的拟合值）划分成箱的意见的基础上表达量
参数：bin.std.resid
list containing the standardized residual from the given Poisson model for observations divided into bins based on amount of expression
从给定的泊松模型划分成箱的意见表达量列表，其中包含的标准化残差
参数：means
vector giving the fitted value for each observed count
给每个观察计数的拟合值的向量
参数：standardized.residuals
vector giving approximate standardized residual for each observed count
向量给每个观察到的数量的近似标准化残差
参数：bins
list containing the indices for the observations, assigning them to bins
指数列表，其中包含的意见，指派他们到垃圾桶
参数：nbins
scalar giving the number of bins used to split up the observed counts
标给分裂观察计数的垃圾箱
参数：ngenes
scalar giving the number of genes/tags in the dataset
标给DataSet中的基因/标记
参数：nlibs
scalar giving the number of libraries in the dataset
标给库中的数据集
getDispersions computes the dispersion from the standardized residuals and returns a list with the following components:
getDispersions计算标准化残差的分散性和返回列表以下组件：
参数：bin.dispersion
vector giving the estimated dispersion value for each bin of observed counts, computed using the average standardized residual for the bin
向量色散值估计为每个观察计数的垃圾桶，使用垃圾桶的平均标准化残差计算
参数：bin.dispersion.used
vector giving the actual estimated dispersion value to be used. Some computed dispersions using the method in this function can be negative, which is not allowed. We use the dispersion value from the nearest bin of higher expression level with positive dispersion value in place of any negative dispersions.
要使用的实际色散值估计的向量。一些计算分散使用此功能的方法可以是负数，这是不允许的。我们从最近的bin表达水平较高的正色散值的任何负面分散的地方使用的色散值。
参数：dispersion
vector giving the estimated dispersion for each observation, using the binned dispersion estimates from above, so that all of the observations in a given bin get the same dispersion value.
向量给每个观察估计分散，分级分散的估计，从上面的使用，使所有的意见，在一个给定的bin得到相同的色散值。
作者（S）----------Author(s)----------
Davis McCarthy
参见----------See Also----------
plotMeanVar, plotMDS.DGEList, plotSmear and maPlot provide more ways of visualizing DGE data.
plotMeanVar，plotMDS.DGEList，plotSmear和maPlot胃排空数据的可视化提供了更多的途径。
举例----------Examples----------
y <- matrix(rnbinom(1000,mu=10,size=2),ncol=4)
design <- model.matrix(~c(0,0,1,1)+c(0,1,0,1))
binned <- dglmStdResid(y, design, dispersion=0.5)
getDispersions(binned)$bin.dispersion.used # Look at the estimated dispersions for the bins[估计分散在寻找垃圾箱]
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 2012-2-25 17:05:16
