R语言 limma包 normexp.fit.control()函数中文帮助文档(中英文对照)

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normexp.fit.control(limma)
normexp.fit.control()所属R语言包：limma

                                        Normexp Model Parameter Estimation Aided by Negative Controls
                                         阴性对照计算机辅助normexp模型参数估计

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The mean and log-standard-deviation of the background-normal part of the normexp+exponential convolution model is estimated as the mean and log-standard deviation of intensities from negative control probes. The log-mean of the signal-exponential part is estimated as the log of the difference between signal mean and background mean.
均值和log的背景下，正常的一部分，标准偏差normexp +指数卷积模型作为阴性对照探针的强度均值和对数标准偏差估计。估计数平均指数信号部分作为记录信号均值和背景平均值之间的差异。


用法----------Usage----------


normexp.fit.control(x, status=NULL, negctrl="negative", regular="regular", robust=FALSE)



参数----------Arguments----------

参数：x
object of class EListRaw-class or matrix containing raw intensities for regular and control probes for a series of microarrays
类的对象EListRaw-class或matrix包含定期和控制的一系列微阵列探针的原料强度


参数：status
character vector giving probe types.
探针类型的特征向量。


参数：negctrl
character string identifier for negative control probes.
为阴性对照探针字符的字符串标识符。


参数：regular
character string identifier for regular probes.
定期探针字符的字符串标识符。


参数：robust
logical. Should robust estimators be used for the background mean and standard deviation?
逻辑。稳健估计应被用来为背景的均值和标准差？


Details

详情----------Details----------

x has to contain raw expression intensities from both regular probes and negative control probes.
x包含经常探针和阴性对照探针从原始的表达强度。

The probe type information for an object of EListRaw-class is normally saved in the Status column of its genes component. However, it will be overriden by the status parameter if it is explicitly provided to this function. If x is a matrix object, the probe type information has to be provided through the status parameter of this function. Regular probes have the status regular. Negative control probes have the status indicated by negctrl, which is negative by default.
探针类型信息为对象的EListRaw-classStatus组件列genes通常被保存在。然而，这将重写由status参数，如果它明确规定此功能。 x如果是matrix对象，探针类型的信息必须通过status这个函数的参数提供。定期探针有状态regular。阴性对照探针的状态表示negctrl是negative默认。

This function estimates parameters of the normal+exponential convolution model with the help of negative control probes. The mean and log-standard-deviation of the background-normal part of the normexp+exponential(normexp) convolution model are estimated as the mean and log-standard deviation of intensities from negative control probes respectively. The log-mean of the signal-exponential part is estimated as the log of the difference between signal mean and background mean. The signal mean is simply the mean of intensities from regular probes.
此功能与阴性对照探针的帮助下，正常+指数的褶积模型参数估计。估计平均强度分别为阴性对照探针和log标准差的均值和记录标准偏差+指数（normexp）的normexp卷积模型的背景下，正常的一部分。估计数平均指数信号部分作为记录信号均值和背景平均值之间的差异。仅仅是信号的平均值，平均来自定期探针的强度。

When negative control probes are not available, the normexp.fit.detection.p function can be used to estimate the normexp model parameters which infers the negative control probe intensities from regular probes by taking advantage of their detection p value information.
当阴性对照探针都无法使用，normexp.fit.detection.p函数可以使用，来估计的normexp的模型参数推断其检测的p值信息的优势，采取定期探针阴性对照探针强度。


值----------Value----------

A matrix containing estimated parameters with rows being arrays and with columns being parameters. Column names are mu, logsigma and logalpha.
矩阵参数估计是阵列的行和列参数。列名mulogsigma和logalpha。


作者（S）----------Author(s)----------


Wei Shi and Gordon Smyth



参考文献----------References----------



参见----------See Also----------

nec calls this function to get the parameters of the normal+exponential convolution model and then calls normexp.signal to perform the background correction.
nec调用这个函数来获得正常+指数卷积模型的参数，然后调用normexp.signal执行背景校正。

normexp.fit.detection.p estimates the parameters in the normal+exponential convolution model using negative control probe intensities inferred from regular probes by using their detection p values information.
normexp.fit.detection.p估计在指数正常+卷积模型，采用阴性对照探针，从定期探针推断的强度通过检测p值信息的参数。

normexp.fit estimates normexp parameters using a saddle-point approximation or other mothods.
normexp.fit估计使用的鞍点逼近或其他mothods normexp参数。

An overview of background correction functions is given in 04.Background.
背景校正功能概述04.Background的。


举例----------Examples----------


## Not run: [＃无法运行：]
# read in BeadChip probe profile file and control profile file[阅读BeadChip探针配置文件和控制配置文件]
x <- read.ilmn(files="sample probe profile", ctrlfiles="control probe profile")
# estimated normexp parameters[估计normexp参数]
normexp.fit.control(x)
# normalization using control data[使用控制数据的标准化]
y <- neqc(x)

## 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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