estimateCommonDisp(edgeR)
estimateCommonDisp()所属R语言包：edgeR
Estimates the Negative Binomial Common Dispersion by Maximizing the Negative Binomial Conditional Common Likelihood
估计由负二项分布有条件的普通可能性最大化负二项分布常见的色散
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Maximizes the negative binomial conditional common likelihood to give the estimate of the common dispersion across all tags for the unadjusted counts provided.
最大化负二项分布条件共同的可能性，以共同分散在所有标签提供的未经调整数的估计。
用法----------Usage----------
estimateCommonDisp(object, tol=1e-06, rowsum.filter=5)
参数----------Arguments----------
参数：object
DGEList object with (at least) elements counts (table of unadjusted counts), and samples (vector indicating group) and lib.size (vector of library sizes)
DGEList对象（至少）元素counts（未经调整数表），samples（矢量显示组）和lib.size（库大小的向量）
参数：tol
numeric scalar providing the tolerance to be passed to optimize; default value is 1e-06
数字标提供的宽容，要传递给optimize，默认值是1e-06的
参数：rowsum.filter
numeric scalar giving a value for the filtering out of low abundance tags in the estimation of the common dispersion. Only tags with total sum of counts above this value are used in the estimation of the common dispersion. Low abundance tags can adversely affect the estimation of the common dispersion, so this argument allows the user to select an appropriate filter threshold for the tag abundance.
数字标给人一种常见的色散估计，筛选出低丰度的标签值。高于此值的计数总和的唯一标签使用中常见的色散估计。低丰度的标签，可以产生不利影响常见的色散估计，所以这个参数允许用户选择适当的过滤器标签丰度阈值。
Details
详情----------Details----------
The method of conditional maximum likelihood assumes that library sizes are equal, which is not true in general, so pseudocounts (counts adjusted so that the library sizes are equal) need to be calculated. The function equalizeLibSizes is called to adjust the counts using a quantile-to-quantile method, but this requires a fixed value for the common dispersion parameter. To obtain a good estimate for the common dispersion, pseudocounts are calculated under the Poisson model (dispersion is zero) and these pseudocounts are used to give an estimate of the common dispersion. This estimate of the common dispersion is then used to recalculate the pseudocounts, which are used to provide a final estimate of the common dispersion.
有条件的最大似然法假定库的大小是相等的，一般这是不正确的，所以pseudocounts（计数调整，使图书馆的大小都是平等的），需要计算。功能equalizeLibSizes被称为调整使用位数到位数的方法计数，但是这需要一个共同的色散参数的固定值。为了获得一个共同的分散性良好的估计，pseudocounts下的泊松模型（色散为零）计算和这些pseudocounts是用来给一个共同的分散估计。这种常见的色散估计，然后使用重新计算的pseudocounts，这是用来提供一个共同的分散的最终估计。
值----------Value----------
estimateCommonDisp produces an object of class DGEList with the following components.
estimateCommonDisp类DGEList以下组件产生一个对象。
参数：common.dispersion
estimate of the common dispersion; the value for phi, the dispersion parameter in the NB model, that maximizes the negative binomial common likelihood on the phi scale
估计常见的色散;值phi，分散在NB模型参数，负二项分布共同可能性phi规模最大
参数：counts
table of unadjusted counts
未经调整数表
参数：group
vector indicating the group to which each library belongs
向量表示每个库属于组
参数：lib.size
vector containing the unadjusted size of each library
每个库未调整大小的向量
参数：pseudo.alt
table of adjusted counts; quantile-to-quantile method (see q2qnbinom) used to adjust the raw counts so that library sizes are equal; adjustment here done under the alternative hypothesis that there is a true difference between groups
调整数的位数，位数的方法（见表q2qnbinom）用于调整的原始计数，使图书馆的大小都是平等的;调整替代假设下，有一个群体之间的真正区别
参数：conc
list containing the estimates of the concentration of each tag in the underlying sample; conc$p.common gives estimates under the null hypothesis of no difference between groups; conc$p.group gives the estimate of the concentration for each tag within each group; concentration is a measure of abundance and thus expression level for the tags
列表，其中包含每个标签的浓度估计在底层样品;conc$p.common组之间无差异的虚无假设下的估计;conc$p.group给人的浓度为每一组内的每个标签的估计;浓度是标签的丰度和表达水平的措施
参数：common.lib.size
the common library size to which the count libraries have been adjusted
公共库的大小来计数库已作调整
作者（S）----------Author(s)----------
Mark Robinson, Davis McCarthy
参考文献----------References----------
binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
参见----------See Also----------
estimateTagwiseDisp can be used to estimate a value for the dispersion parameter for each tag/transcript. The estimates are stabilized by squeezing the estimates towards the common value calculated by estimateCommonDisp.
estimateTagwiseDisp可以用来估计每个标签/成绩单色散参数的值。估计是稳定挤实现estimateCommonDisp计算的共同价值的估计。
举例----------Examples----------
# True dispersion is 1/5=0.2[真正的分散是1/5 = 0.2]
y <- matrix(rnbinom(1000,mu=10,size=5),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
cmdisp <- estimateCommonDisp(d)
cmdisp$common.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。
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发表于 2012-2-25 17:06:22
