nec(limma)
nec()所属R语言包：limma
NormExp Background Correction and Normalization Using Control Probes
NormExp背景校正和规范化使用控制探针
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Perform normexp background correction using negative control probes and quantile normalization using negative and positive control probes.
执行normexp背景校正，用阴性对照探针和位数标准化用阴性和阳性对照探针。
用法----------Usage----------
nec(x, status=NULL, negctrl="negative", regular="regular", offset=16, robust=FALSE,detection.p="Detection")
neqc(x, status=NULL, negctrl="negative", regular="regular", offset=16, robust=FALSE, detection.p="Detection",...)
参数----------Arguments----------
参数：x
object of class EListRaw or matrix containing raw intensities for regular and control probes from a series of microarrays.
类对象EListRaw或matrix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. Defaults to x$genes$Status if x is an EListRaw object.
探针类型的特征向量。默认为x$genes$Status如果x是EListRaw对象。
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, i.e., all probes other than control probes.
字符字符串标识符，即定期探针，控制探针以外的所有探针。
参数：offset
numeric value added to the intensities after background correction.
数值添加到背景校正后的强度。
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？
参数：detection.p
a character string giving the name of the component which contains detection p value information in x or a numeric matrix giving detection p values, Detection by default
一个字符串给组件的名称其中包含x或数字矩阵，检测p值，Detection的检测p值信息默认情况下，
参数：...
any other arguments are passed to normalizeBetweenArrays.
任何其他参数传递normalizeBetweenArrays.的
Details
详情----------Details----------
neqc performs background correction followed by quantile normalization, using negative control probes for background correction and both negative and positive controls for normalization. nec is similar but performs background correction only.
neqc位数标准化，其次是使用背景校正和标准化阴性和阳性对照，阴性对照探针进行背景校正。 nec类似，但只进行背景校正。
When control data are available, these function call normexp.fit.control to estimate the parameters required by normal+exponential(normexp) convolution model with the help of negative control probes, followed by normexp.signal to perform the background correction. If x contains background intensities x$Eb, then these are first subtracted from the foreground intensities, prior to normexp background correction. After background correction, an offset is added to the data.
当控制数据是可用的，这些函数调用normexp.fit.control估计按正常所需的参数+指数（normexp）与阴性对照探针的帮助下，卷积模型，通过normexp.signal执行背景校正。如果x包含背景强度x$Eb，那么这些先减去从前台强度，前normexp背景校正。背景校正后，offset被添加到数据。
When control data are not available, these functions call normexp.fit.detection.p to estimate the normexp parameters. normexp.fit.detection.p infers negative control probe intensities from regular probes by taking advantage of their detection p value information.
当控制数据都无法使用，这些函数调用，normexp.fit.detection.p估计normexp的参数。 normexp.fit.detection.p推断阴性对照从定期探针探针强度，通过利用其检测的p值信息。
For more descriptions to parameters x, status, negctrl, regular and detection.p, please refer to functions normexp.fit.control, normexp.fit.detection.p and read.ilmn.
为更多的参数说明x，status，negctrl，regular和detection.p，请参阅函数normexp.fit.control，normexp.fit.detection.p read.ilmn。
Both nec and neqc perform the above steps. neqc continues on to quantile normalize the background-corrected intensities, including control probes. After normalization, the intensities are log2 transformed and the control probes are removed.
既nec和neqc执行上述步骤。 neqc继续位数标准化的背景校正的强度，包括控制探针。标准化后，强度log2转化和控制探测器将被删除。
值----------Value----------
nec produces a EListRaw-class or matrix object of the same dimensions as x containing background-corrected intensities, on the raw scale. neqc produces a EList-class or matrix object containing normalized log2 intensities, with rows corresponding to control probes removed.
nec生产EListRaw-class载有背景校正强度相同尺寸的x或矩阵对象，对原材料的规模。 neqc生产EList-class或矩阵对象，包含标准化的log2强度，与相应的控制探针删除的行。
作者（S）----------Author(s)----------
Wei Shi and Gordon Smyth
参考文献----------References----------
Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression BeadChips. Nucleic Acids Research 38, e204. http://nar.oxfordjournals.org/content/38/22/e204
参见----------See Also----------
An overview of background correction functions is given in 04.Background.
背景校正功能概述在04.Background。
An overview of LIMMA functions for normalization is given in 05.Normalization.
在05.Normalization标准化LIMMA功能概述。
normexp.fit.control estimates the parameters in the normal+exponential convolution model using the negative control probes.
normexp.fit.control估计在正常+指数卷积使用阴性对照探针模型的参数。
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 parameters in the normal+exponential convolution model using a saddle-point approximation or other methods.
normexp.fit估计在正常+指数卷积利用鞍点逼近或其他方法的模型参数。
neqc performs normexp background correction and quantile normalization aided by control probes.
neqc执行normexp背景校正和控制探针辅助位数标准化。
举例----------Examples----------
## Not run: [＃无法运行：]
# neqc normalization for data which include control probes[neqc标准化的数据，其中包括控制探针]
x <- read.ilmn(files="sample probe profile.txt",ctrlfiles="control probe profile.txt")
y <- neqc(x)
# Same thing but in separate steps:[同样的事情，但在不同的步骤：]
x.b <- nec(x)
y <- normalizeBetweenArrays(x.b,method="quantile")
y <- y[y$genes$Status=="regular",]
# neqc normalization for data which do not include control probes[数据不包括控制探针neqc标准化]
xr <- read.ilmn(files="sample probe profile.txt")
yr <- neqc(xr)
## 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)。
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 23:19:45
