R语言 TSSi包 normalizeCounts-methods()函数中文帮助文档(中英文对照)

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normalizeCounts-methods(TSSi)
normalizeCounts-methods()所属R语言包：TSSi

                                         Normalize methods
                                         标准化的方法

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Normalize sequence read count data.
标准化序列读取计数数据。


用法----------Usage----------


normalizeCounts(x, fun=mean, offset=10L, basal=1e-4, lambda=c(1, 1),
fit=FALSE, multicore=TRUE, optimizer="all", ...)



参数----------Arguments----------

参数：x
Object of class TssData with raw data to normalize.
对象类TssData与原始数据的标准化。


参数：fun
Function used to average over replicates (default: mean).
平均使用了复制功能（默认：mean）。


参数：offset
Integer defining the number of bases add to the ends of each segment with basal rate.
定义的碱基数量的整数添加到每个段的两端与basal速率。


参数：basal
Numeric specifying the basal rate.
数字指定的基础利率。


参数：lambda
Numeric vector of length two specifying the regulation parameter for each side of the segment.
长度为二的数字矢量指定段两侧的调节参数。


参数：fit
Logical whether the fitting should be performed in addition to the estimation based on the Poisson ratios obtained from all reads.
配件是否逻辑，应在所有读取得到的泊松比的基础上估计除了执行。


参数：multicore
Logical whether to use the multicore package to speed up the fitting. Has only an effect if the package is available and loaded. For details, see the "details" section.
逻辑是否使用multicore包加快拟合。如果包是可用的和装，只有效果。有关详情，请参阅“详细信息”一节。


参数：optimizer
Character string choosing the optimizer for the fit (default: “all”). Possible choices are “optim” for the optim function from the stats package, “bobyqa” for the bobyqa function from the minqu package,or “all” for taking the best fit out of both.
选择合适的优化字符串（默认是：“所有”）。可能的选择是“OPTIM”从optim包，“bobyqa”stats包的功能bobyqa功能minqu，或“所有”同时要考虑最适合的。


参数：...
Additional arguments passed for the multicore package if used. For details, see the "details" section.
multicore包通过，如果使用额外的参数。有关详情，请参阅“详细信息”一节。


Details

详情----------Details----------

The normalization reduces the noise by shrinking the counts towards zero. This step is intended to eliminate false positive counts as well as making further analyzes more robust by reducing the impact of large counts. Such a shrinkage or regularization procedure constitutes a well-established strategy in statistics to make predictions conservative, i.e. to reduce the number of false positive predictions.
标准化，减少噪音，实现零收缩计数。这一步的目的是进一步分析更健壮，减少大型计数的影响以及消除假阳性计数。这种收缩或正规化的过程构成了统计行之有效的战略，使保守的预测，即以减少假阳性的预测数。

An objective function is minimized to estimate the transcription level in a regularized manner. The log-likelihood is given by the product of the probabilities of the counts which is assumed as a Poisson distribution by default.
目标函数是最小化，正规化的方式来估算的转录水平。对数似然的产品，这是默认情况下，假定为泊松分布的计数的概率。

For \sQuote{lambda[1] > 0}, counts unequal to zero are penalized to obtain conservative estimates of the transcription levels with a preferably small number components, i.e. genomic positions, unequal to zero. The larger \sQuote{lambda[1]}, the more conservative is the identification procedure.
\sQuote{lambda[1] > 0}，零计数不平等的处罚，以获得最好少数组件，即基因组的位置，不等于零的转录水平的保守估计。较大的\sQuote{lambda[1]}，较为保守的识别程序。

To enhance the shrinkage of isolated counts in comparison to counts in regions of strong transcriptional activity, the information of consecutive genomic positions in the measurements is regarded by evaluating differences between adjacent count estimates.
要加强收缩孤立计数比较强转录活性区域的计数，连续测量基因位置的信息被视为评估相邻的计数估计之间的差异。

In order to distribute the identification step over multiple processor cores, the mclapply function of the multicore package can be used. For this, the multicore package has to be loaded manually before starting the computation, additional parameters are passed via the ... argument, e.g.as normalizeCounts(x,     mc.ncores=2). The multicore argument can further be used to temporarily disable the parallel estimation by setting it to FALSE.
为了分发可用于多个处理器核心的识别步骤，mclapplymulticore包的功能。对于这一点，在开始计算之前，手动加载multicore包，额外的参数传递通过...说法，EGASnormalizeCounts(x,     mc.ncores=2)。 multicore参数可以进一步用来暂时禁用并行估计设置FALSE。


值----------Value----------

An object of class TssNorm.
对象类TssNorm。


方法----------Methods----------

Normalize read data:
标准化读取数据：

signature(x="TssData")
signature(x="TssData")

normalizeCounts(x, ...)  
normalizeCounts(x, ...)


作者（S）----------Author(s)----------



Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>




参见----------See Also----------

Classes: TssData, TssNorm, TssResult
类别：TssData，TssNorm，TssResult

Methods: segmentizeCounts, normalizeCounts, identifyStartSites, get-methods, plot-methods, asRangedData-methods
方法：segmentizeCounts，normalizeCounts，identifyStartSites，get-methods，plot-methods，asRangedData-methods

Functions: subtract-functions
功能：subtract-functions

Data set: physcoCounts
数据集：physcoCounts

Package: TSSi-package
包装：TSSi-package


举例----------Examples----------


## preceding steps[＃前面的步骤]
example(segmentizeCounts)

## normalize data, w/o and w/ fitting[＃标准化数据，W / O和W /件]
yRatio <- normalizeCounts(x)
yFit <- normalizeCounts(x, fit=TRUE)

yFit

## Not run: [＃无法运行：]
## multicore computation[＃多核计算]
library(multicore)
yFit <- normalizeCounts(x, fit=TRUE, mc.ncores=2)

## End(Not run)[＃结束（不运行）]

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


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