meanvar(edgeR)
meanvar()所属R语言包:edgeR
Explore the mean-variance relationship for DGE data
探索胃排空数据的均值 - 方差关系
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. Here are functions to compute tag/gene means and variances, as well at looking at these quantities when data is binned based on overall expression level.
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。下面是函数来计算标签/基因手段和差异,以及在这些数量数据时,分级的基础上整体表达水平。
用法----------Usage----------
plotMeanVar(object, meanvar=NULL, show.raw.vars=FALSE, show.tagwise.vars=FALSE, show.binned.common.disp.vars=FALSE, show.ave.raw.vars=TRUE, scalar=NULL, NBline=FALSE, nbins=100, log.axes="xy", xlab=NULL, ylab=NULL, ...)
binMeanVar(x, conc=NULL, group, nbins=100, common.dispersion=FALSE, object=NULL)
参数----------Arguments----------
参数:object
DGEList object containing the raw data and dispersion value. According the method desired for computing the dispersion, either CRDisp or estimateCommonDisp and (possibly) estimateTagwiseDisp should be run on the DGEList object before using plotMeanVar. The argument object must be supplied in the function binMeanVar if common dispersion values are to be computed for each bin.
DGEList对象,其中包含原始数据和色散值。根据所需的计算分散,要么CRDisp或estimateCommonDisp和estimateTagwiseDisp应运行之前使用DGEListplotMeanVar对象(可能)。方法参数object必须提供的功能binMeanVar常见的色散值,如果要计算每个容器。
参数:meanvar
list (optional) containing the output from binMeanVar or the returned value of plotMeanVar. Providing this object as an argument will save time in computing the tag/gene means and variances when producing a mean-variance plot.
列表(选购)包含输出从binMeanVar或plotMeanVar的返回值。提供这个对象作为参数,将节省时间标记/基因的均值和方差计算时产生一个均值 - 方差图。
参数:show.raw.vars
logical, whether or not to display the raw (pooled) gene/tag variances on the mean-variance plot. Default is FALSE.
逻辑,是否不显示原料(池)的基因/标记差异的均值 - 方差的图。默认FALSE。
参数:show.tagwise.vars
logical, whether or not to display the estimated genewise/tagwise variances on the mean-variance plot. Default is FALSE.
逻辑,是否或不显示的均值 - 方差的图估计2-6。/ tagwise差异。默认FALSE。
参数:show.binned.common.disp.vars
logical, whether or not to compute the common dispersion for each bin of tags and show the variances computed from those binned common dispersions and the mean expression level of the respective bin of tags. Default is FALSE.
逻辑,是否计算为每个标签的垃圾桶共同分散和显示这些分级的共同分散和各自的标签斌的平均表达水平计算的差异。默认FALSE。
参数:show.ave.raw.vars
logical, whether or not to show the average of the raw variances for each bin of tags plotted against the average expression level of the tags in the bin. Averages are taken on the square root scale as regular arithmetic means are likely to be upwardly biased for count data, whereas averaging on the square scale gives a better summary of the mean-variance relationship in the data. The default is TRUE.
逻辑,不论是否在bin标签的平均表达水平显示,平均每个标签的垃圾桶原料差异暗算。平均数是采取定期算术平均值的平方根规模有可能被向上偏向计数数据,而平均规模广场上,给出了一个更好的汇总数据的均值 - 方差关系。默认TRUE。
参数:scalar
vector (optional) of scaling values to divide counts by. Would expect to have this the same length as the number of columns in the count matrix (i.e. the number of libraries).
缩放值除以计数由向量(可选)。希望有这相同长度的计数矩阵(即图书馆)数列。
参数:NBline
logical, whether or not to add a line on the graph showing the mean-variance relationship for a NB model with common dispersion.
逻辑,是否或不添加上显示为一个共同分散NB模型的均值 - 方差关系图线。
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:log.axes
character vector indicating if any of the axes should use a log scale. Default is "xy", which makes both y and x axes on the log scale. Other valid options are "x" (log scale on x-axis only), "y" (log scale on y-axis only) and "" (linear scale on x- and y-axis).
特征向量表示,如果任何的轴应该使用一个log规模。默认是"xy"log规模,这使得Y和X轴。其他有效选项"x"(登录X-轴的规模只),"y"(log规模只对Y轴)和(x和y轴的线性刻度)"" 。
参数:xlab
character string giving the label for the x-axis. Standard graphical parameter. If left as the default NULL, then the x-axis label will be set to "logConc".
字符串x轴的标签。标准的图形参数。如果保留为默认的NULL,然后在X轴标签将被设置为“logConc”。
参数:ylab
character string giving the label for the y-axis. Standard graphical parameter. If left as the default NULL, then the x-axis label will be set to "logConc".
字符串y轴的标签。标准的图形参数。如果保留为默认的NULL,然后在X轴标签将被设置为“logConc”。
参数:...
further arguments passed on to plot
通过进一步的论据plot
参数:x
matrix of count data, with rows representing tags/genes and columns representing samples
矩阵的计数数据,与代表标记基因和行/列代表样本
参数:conc
vector (optional) of values for the concentration (i.e. abundance) of each tag
向量(可选)浓度值(即丰度),每个标签
参数:group
factor giving the experimental group or condition to which each sample (i.e. column of x or element of y) belongs
因素给实验组或每个样品(xy的元素,即列)所属的条件
参数:common.dispersion
logical, whether or not to compute the common dispersion for each bin of tags.
逻辑,是否或不常见的分散计算每个标签的垃圾桶。
Details
详情----------Details----------
This function is useful for exploring the mean-variance relationship in the data. Raw variances are, for each gene, the pooled variance of the counts from each sample, divided by a scaling factor (by default the effective library size). The function will plot the average raw variance for tags split into nbins bins by overall expression level. The averages are taken on the square-root scale as for count data the arithmetic mean is upwardly biased. Taking averages on the square-root scale provides a useful summary of how the variance of the gene counts change with respect to 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. Optionally, the raw variances and estimated tagwise variances can also be plotted. Estimated tagwise variances can be calculated using either qCML estimates of the tagwise dispersions (estimateTagwiseDisp) or Cox-Reid conditional inference estimates (CRDisp). 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。原料的差异,对每一个基因,从每个样品计数汇集方差除以一个比例因子(默认情况下,有效库容量)。该函数将绘制平均分割成nbins箱整体表达水平的标签原料的差异。的平均平方根规模的计数数据的算术平均数向上偏向。以的平方根规模上的平均基因计数的变化如何改变表达水平(丰)提供了一个有用的总结。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。或者,原料的差异,估计tagwise差异也可以被绘制。可以计算出使用要么qCML的tagwise分散的估计数(estimateTagwiseDisp)或COX-里德有条件的推理估计(CRDisp)的预计tagwise差异。图用于log记录的规模。
值----------Value----------
plotMeanVar produces a mean-variance plot for the DGE data using the options described above. plotMeanVar and binMeanVar both return a list with the following components:
plotMeanVar产生的的胃排空使用上述选项的数据为均值 - 方差的图。 plotMeanVar和binMeanVar都返回以下组件的列表:
参数:avemeans
vector of the average expression level within each bin of genes, with the average taken on the square-root scale
每个基因的垃圾桶内的平均表达水平的向量,上平方根规模的平均
参数:avevars
vector of the average raw pooled gene-wise variance within each bin of genes, with the average taken on the square-root scale
向量的平均原料汇集明智的基因每个基因的垃圾桶内方差,平均与平方根规模采取
参数:bin.means
list containing the average (mean) expression level for genes divided into bins based on amount of expression
列表,其中包含的平均水平(平均)的表达水平划分成箱的基因表达量的基础上
参数:bin.vars
list containing the pooled variance for genes divided into bins based on amount of expression
列表,其中包含汇集划分成箱的基因表达量的方差
参数:means
vector giving the mean expression level for each gene
给每个基因的平均表达水平的向量
参数:vars
vector giving the pooled variance for each gene
向量给每个基因的合并方差
参数:bins
list giving the indices of the tags in each bin, ordered from lowest expression bin to highest
给该指数在每个容器的标签列表,责令表达斌从最低到最高的
作者(S)----------Author(s)----------
Davis McCarthy
参见----------See Also----------
plotMDS.DGEList, plotSmear and maPlot provide more ways of visualizing DGE data.
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)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
plotMeanVar(d) # Produce a straight-forward mean-variance plot[产生直接的均值 - 方差图]
meanvar <- plotMeanVar(d, show.raw.vars=TRUE) # Produce a mean-variance plot with the raw variances shown and save the means and variances for later use[生产与原料差异的均值 - 方差的图,并保存供以后使用的均值和方差]
## If we want to show estimated tagwise variances on the plot, we must first estimate them![#如果我们要显示在图上估计tagwise差异,我们必须先估计他们!]
d <- estimateCommonDisp(d) # Obtain an estimate of the dispersion parameter[获得色散参数的估计]
d <- estimateTagwiseDisp(d) # Obtain tagwise dispersion estimates[获得tagwise色散估计]
plotMeanVar(d, meanvar=meanvar, show.tagwise.vars=TRUE, NBline=TRUE) # Use previously saved object to speed up plotting;[使用以前保存的对象,以加快绘图;]
## We could also estimate common/tagwise dispersions using the Cox-Reid methods with an appropriate design matrix[#我们也可以使用适当的设计矩阵的COX-里德方法估计共同/ tagwise的分散]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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