dispBinTrend(edgeR)
dispBinTrend()所属R语言包：edgeR
Estimate Dispersions with an Abundance-Dependent Trend for Negative Binomial GLMs
与丰度的依赖性趋势负二项式GLMs的预算分散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate a dispersion parameter for each of many negative binomial generalized linear models by computing the common dispersion for genes sorted into bins based on overall abundance and then using splines or a loess fit to interpolate a dispersion value for each gene, dependent on overall abundance of the gene.
由分散的基础上整体丰盈，然后使用样条线或插的黄土适合为每个基因的色散值，依赖于整体丰度计算成箱排序的基因共同分散了许多负二项式广义线性模型的每个参数估计基因。
用法----------Usage----------
dispBinTrend(y, design, offset=NULL, df = 5, span=2/3, min.n=500, method.bin="CoxReid", method.trend="spline", trace=0, ...)
参数----------Arguments----------
参数：y
numeric matrix of counts
数字矩阵的计数
参数：design
numeric matrix giving the design matrix for the GLM that is to be fit.
数字矩阵提供的GLM是适合的设计矩阵。
参数：offset
numeric scalar, vector or matrix giving the offset (in addition to the log of the effective library size) that is to be included in the NB GLM for the transcripts. If a scalar, then this value will be used as an offset for all transcripts and libraries. If a vector, it should be have length equal to the number of libraries, and the same vector of offsets will be used for each transcript. If a matrix, then each library for each transcript can have a unique offset, if desired. In adjustedProfileLik the offset must be a matrix with the same dimension as the table of counts.
数字标量，向量或矩阵给抵消（除了的有效库容量的log）是被包括在NB的GLM的成绩单。如果一个标量，那么这个值将被用作所有成绩单和库中的偏移量。如果一个向量，它应该有长度等于数字图书馆，将每个成绩单使用相同的偏移向量。如果一个矩阵，然后每个谈话的每个库可以有独特的偏移，如果需要的话。在adjustedProfileLikoffset必须与计数表的同一维度的矩阵。
参数：df
scalar, the degrees of freedom for the natural cubic splines fit, used to determine the placement of the knots (number of knots is df - 1. Default is 5.
标，自然三次样条拟合的自由度，用于确定安置的节（节数是df - 1。默认是5。
参数：span
scalar, passed to loess to determine the amount of smoothing for the loess fit. Default is 2/3.
标，通过loess平滑为黄土合适的金额确定。默认2/3。
参数：min.n
scalar, minimim number of genes in each of the bins into which genes are sorted to form the basis for interpolating the dispersions. Setting a minimum value ensures that there will be sufficient genes in each bin to allow reliable estimation of the common dispersion for each bin.
标，基因minimim的数量到其中的基因进行排序，形成插在分散的基础上的每个垃圾箱。设置一个最低值，保证会有足够的基因在每个垃圾桶，让每个垃圾桶的共同分散可靠估计。
参数：method.bin
character, passed to binGLMDispersion, to specify the method used to compute the common dispersion within each bin of genes. Default is "CoxReid", other options are "Pearson" and "deviance".
字符，通过binGLMDispersion，指定用于计算每个基因的垃圾桶内常见的色散的方法。默认是"CoxReid"，其他选项"Pearson"和"deviance"。
参数：method.trend
character, specifies method to produce a smooth fit through the binned common dispersions in order to interpolate the trended dispersions. Default is "spline" to use natural cubic splines, other option is "loess" to use a loess fit.
字符，指定的方法，通过分级常见的分散生产顺利适合以插值的趋势化分散。默认是"spline"使用自然三次样条，其他选项"loess"用黄土合适。
参数：trace
logical, should iteration information be output?
逻辑，应该迭代信息输出呢？
参数：...
option arguments to be passed to lower-level function binGLMDispersion.
选项参数被传递到较低级别的功能binGLMDispersion。
Details
详情----------Details----------
This function takes the binned common dispersion and abundance from binGLMDispersion and fits a smooth curve through these binned values using either natural cubic splines or loess. From this smooth curve it predicts the dispersion value for each gene based on the gene's overall abundance. This results in estimates for the NB dispersion parameter which have a dependence on the overall expression level of the gene, and thus have an abundance-dependent trend. This function is called by estimateGLMTrendedDisp.
此函数采用分级常见的色散从binGLMDispersion和丰富，适合使用任何自然三次样条或黄土通过这些分级值的平滑曲线。从这个光滑的曲线，它预测，基于基因的整体丰富，每个基因的色散值。这一结果在NB色散参数的估计，其中有一个整体的基因表达水平的依赖，从而有丰富的依赖性趋势。此功能被称为estimateGLMTrendedDisp。
值----------Value----------
list with the following components:
名单如下组件：
参数：abundance
numeric vector containing the overall abundance for each gene
数字向量整体丰度为每一个基因
参数：dispersion
numeric vector giving the trended dispersion estimate for each gene
给每一个基因的趋势化的分散估计的数字向量
参数：bin.abundance
numeric vector of length equal to nbins giving the average (mean) abundance for each bin
数字矢量的长度等于nbins给平均每个垃圾桶（平均）丰
参数：bin.dispersion
numeric vector of length equal to nbins giving the estimated common dispersion for each bin
数字矢量长度等于nbins给每个垃圾桶估计常见的色散
作者（S）----------Author(s)----------
Davis McCarthy and Gordon Smyth
参考文献----------References----------
参见----------See Also----------
binGLMDispersion, estimateGLMTrendedDisp
binGLMDispersion，estimateGLMTrendedDisp
举例----------Examples----------
ntags <- 1000
nlibs <- 4
means <- seq(5,10000,length.out=ntags)
y <- matrix(rnbinom(ntags*nlibs,mu=rep(means,nlibs),size=0.1*means),nrow=ntags,ncol=nlibs)
keep <- rowSums(y) > 0
y <- y[keep,]
group <- factor(c(1,1,2,2))
lib.size <- colSums(y)
design <- model.matrix(~group) # Define the design matrix for the full model[定义完整的模型设计矩阵]
disp <- dispBinTrend(y, design, offset=log(lib.size), min.n=100, span=0.3)
plot(disp$abundance, disp$dispersion)
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 17:05:36
