R语言 girafe包 addNBSignificance()函数中文帮助文档(中英文对照)

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addNBSignificance(girafe)
addNBSignificance()所属R语言包：girafe

                                        assess significance of sliding-window read counts
                                         评估的滑动窗口读计数的意义

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function can be used to assess the significance of sliding-window read counts. The background distribution of read counts in windows is assumed to be a Negative-Binomial (NB) one. The two parameters of the NB distribution, mean "mu" and dispersion "size", are estimated using any of the methods described below (see details). The estimated NB distribution is used to assign a p-value to each window based on the number of aligned reads in the window. The p-values can be corrected for multiple testing using any of the correction methods implemented for p.adjust.
此功能可用于评估的滑动窗口读计数的意义。在Windows读取计数的背景分布被认为是负的二项式（注）。这两个参数的NB分布，平均亩和大小分散，估计使用下面描述的方法（见详情）。用于基于数排列在窗口读取每个窗口分配一个P-值估计NB分布。 P-值可以纠正多个测试，任何使用实施p.adjust的校正方法。


用法----------Usage----------


addNBSignificance(x, estimate="NB.012", correct = "none",  max.n=10L)



参数----------Arguments----------

参数：x
A data.frame of class slidingWindowSummary, as returned by the function perWindow.
一个类data.frame slidingWindowSummary的，功能perWindow返回。


参数：estimate
string; which method to use to estimate the parameters of the NB background distribution; see below for details
字符串方法使用估计NB的背景分布的参数，详情见下文


参数：correct
string; which method to use for p-value adjustment; can be any method that is implemented for p.adjust including “none” if no correction is desired.
字符串; P-值调整的方法来使用;可以是任何p.adjust包括“无”的实施，如果不改正的，所需的方法。


参数：max.n
integer; only relevant if estimate=="NB.ML"; in that case specifies that windows with up to this number of aligned reads should be considered for estimating the background distribution.
整数，唯一相关的，如果estimate=="NB.ML";在这种情况下，指定窗口与这对准读取数量应考虑估计背景分布。


Details

详情----------Details----------

The two parameters of the Negative-Binomial (NB) distribution are: mean "lambda" (or "mu") and size "r" (or "size").<br>
负二项分布（NB）分布的两个参数是：意思是lambda（或亩）和大小r（或大小）。参考

The function knows a number of methods to estimate the parameters of the NB distribution.
函数，知道了许多方法来估计NB的分布参数。




&ldquo;NB.012&rdquo; Solely the windows with only 0, 1, or 2 aligned reads are used for estimating lambda and "r". From the probability mass function g(k)=P(X=k) of the NB distribution, it follows that the ratios
用于估算lambdar“NB.012”仅仅只有0，1或2对齐读取窗口。从概率密度函数g(k)=P(X=k)NB的分布，它遵循的比率

and
和

The observed numbers of windows with 0-2 aligned reads are used to estimate
0-2对齐读取Windows的观测数字来估计

and
和

and from these estimates, one can obtain estimates for 'lambda' and
从这些估计，可以获得'lambda'“估计




&ldquo;NB.ML&rdquo; This estimation method uses the function fitdistr from package "MASS". Windows with up to
“NB.ML”这种估算方法使用功能fitdistr包从“大众”。 Windows与高达




&ldquooisson&rdquo; This estimate also uses the windows the 0-2 aligned reads, but uses these numbers to estimates the parameter lambda of a Poisson distribution. The parameter "r" is set to a very large number, such that the estimated NB distribution actually is a Poisson distribution with
“泊松”这个估计也使用Windows 0-2对齐读，但使用这些数字来估计参数lambda泊松分布。参数r设置为一个很大的数字，这样，估计NB分布实际上是一个泊松分布，


值----------Value----------

A data.frame of class slidingWindowSummary, which is the the supplied argument x extended by an additional column p.value which holds the p-value for each window. The attribute NBparams of the result contains the list of the  estimated parameters of the Negative-Binomial background distribution.
一个data.frame类slidingWindowSummary，是所提供的参数x延长一个额外的列p.value而持有的每个窗口的p值。属性NBparams包含列表的负二项式背景，分布参数估计的结果。


作者（S）----------Author(s)----------


Joern Toedling



参考文献----------References----------

described in the paper:<br> Ji et al.(2008) An integrated system CisGenome for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol. 26(11):1293-1300.

参见----------See Also----------

perWindow, p.adjust
perWindow，p.adjust


举例----------Examples----------


exDir <- system.file("extdata", package="girafe")
exA   <- readAligned(dirPath=exDir, type="Bowtie",
   pattern="aravinSRNA_23_no_adapter_excerpt_mm9_unmasked.bwtmap")
exAI  <- as(exA, "AlignedGenomeIntervals")
exPX  <- perWindow(exAI, chr="chrX", winsize=1e5, step=0.5e5)
exPX  <- addNBSignificance(exPX, correct="bonferroni")
str(exPX)
exPX[exPX$p.value <= 0.05,]

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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