R语言 Agi4x44PreProcess包 BGandNorm()函数中文帮助文档(中英文对照)

loveR

BGandNorm(Agi4x44PreProcess)
BGandNorm()所属R语言包：Agi4x44PreProcess

                                        Background Correction and Normalization Between Arrays
                                         背景校正和阵列之间的标准化

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

For Background correction it uses the 'backgroundCorrect' function of 'limma' package ('half','normexp'). For Normalization between arrays it uses 'limma' function 'normalizeBetweenArrays' ('quantile','vsn').
校正背景，它使用“backgroundCorrect”功能包“limma”（“一半”，“normexp）。阵列之间使用limma“功能”normalizeBetweenArrays（分量，VSN）标准化。


用法----------Usage----------


        BGandNorm(RGlist, BGmethod, NORMmethod, foreground,
        background, offset, makePLOTpre, makePLOTpost)



参数----------Arguments----------

参数：RGlist
an 'RGList' object  
RGList“对象


参数：BGmethod
Method for the BG corection. Possible values are:  'none','half','normexp'. See ?backgroundCorrect in limma package for details  
法保函2004/4/26。可能的值是：没有，一半，normexp。看到了什么？backgroundCorrect limma包详情


参数：NORMmethod
Method for Norm between arrays. Possible values can be: 'none','quantile',vsn'. See ?normalizeBetweenArrays in limma package  
阵列之间的规范方法。可能的值可以是：没有，分量，VSN“。 limma包normalizeBetweenArrays看到了什么？


参数：foreground
Foreground Signal to be used for the analysis.  Possible values are 'MeanSignal','ProcessedSignal'  
前景信号可用于分析。可能的值是“MeanSignal，ProcessedSignal


参数：background
Background Signal to be used for the BG correction. The values can be: 'BGMedianSignal','BGUsed'  
背景信号使用的BG校正。值可以是：“BGMedianSignal，BGUsed


参数：offset
numeric value to add to the intensities before log transforming. The offset shrunks the log ratios towards zero at the lower intensities. See limma user guide for details  
数值添加log改造前的强度。偏移shrunks的比率接近零的低强度的log。有关详细信息，请参阅limma用户指南


参数：makePLOTpre
density Plots, box plots, MVA plots and RLE plots with the raw signal
密度图，箱线图，MVA的图和RLE的图与原始信号


参数：makePLOTpost
density Plots, box plots, MVA plots and RLE plots with the normalized signal  
密度图，箱线图，MVA的图和RLE的图归信号


Details

详情----------Details----------

In order to make direct comparisons of data coming from different chips it  is important to remove sources of variation of non biological  nature that may exists between arrays. Systematic non-biological  differences between chips become relevant in several obvious ways  especially during labeling and hybridization, and bias the relative  measures on any two chips when we want to quantify differences due to different treatment between two samples. Normalization is the attempt to compensate for systematic technical differences between chips, to see more clearly the systematic biological  differences between samples. First data are background corrected. We produce a Background Subtracted  Signal. The Background Signal Used depends on the AFE settings for the type  of background method calculation and the settings for spatial detrend. Usually,  the Background Signal Used is the sum of the Local Background Signal + the  Spatial Detrending Surface Value computed by the AFE software.  For the Background correction we use the 'backgroundCorrect' function of 'limma' package with options <'half','normexp'>         This function is designed to produce positive corrected intensities.  First, any intensity value lower than 0.5 is reset to be equal to 0.5.  Besides, and offset value (normally 50) is used. This offset value adds  a constant to the intensity values before log-transforming, so that the log  ratios are shrunk towards zero at the lower intensities. After background correction, data are normalized between arrays using  'limma' function 'normalizeBetweenArrays' with options <'quantile','vsn'>
为了使来自不同的芯片的数据直接比较重要的是要消除可能阵列之间存在的非生物性质的变化来源。系统的非生物芯片之间的差异成为在标签和杂交和偏见任何两个芯片上的相对措施，特别是在几个明显的方式有关，当我们要量化的差异，由于两个样本之间的不同待遇。规范化是企图弥补系统芯片之间的技术差异，更清楚地看到系统生物样本之间的差异。第一个数据，背景纠正。我们生产的背景消减信号。 AFE的设置为背景的方法计算和空间detrend的设置的类型取决于所使用的背景信号。通常情况下，所使用的背景信号是本地背景信号+ AFE的软件价值计算的空间去趋势表面的总和。背景校正，我们使用的“”limma包backgroundCorrect功能与选项<一半，normexp“>此功能设计产生积极的纠正强度。首先，任何强度值低于0.5复位等于0.5。此外，使用偏移值（通常为50）。这个偏移值增加了一个恒定的log转化前的强度值，使log比缩水接近零的低强度。背景校正后，数据标准化之间阵列使用选项“limma”功能“normalizeBetweenArrays”<分量，VSN>

For foreground signal,the user can choose between the 'MeanSignal' and the 'ProcessedSignal' and between the 'BGMedianSignal' and the 'BGUsed' for  background correction.                 The user may want to have a look at different graphics (density plots, etc ...)  in order to decide what signal is more suitable to use. For details about signal processing see AFE User Guide.  'MeanSignal' is the spot Raw mean signal. 'ProcessedSignal' is the signal processed by the Agilent Feature Extraction image analysis software (AFE). It contains the  Multiplicatively Detrend Bacground Substracted Signal if the detrending is selected and it helps. If the detrending does not help, the 'ProcessedSignal' will be the Bacground Subtracted Signal.  'BGMedianSignal' is the Median local background signal.  'BGUsed' depends on the AFE software settings for the type of background method calculation and the setting for the spatial detrend. Usually, the Background Signal Used is the sum  of the local bacground + the spatial detrending surface value computed by  the AFE software. To view the values used to calculate this variable using different bakground signals and settings of spatial detrend and global background adjust, see Table 33 on page 213 of the AFE User Guide.  Limma function 'backgroundCorrect' is used for the BG correction. This function is designed to produce positive intensities. Any intensity value lower less than 0.5 is reset to be equal to 0.5. Additionally, a constant of 50 (normally) is used as a offset that it is added to the intensity values before the log transformation.  The propouse of this calculation is to shrunk the log ratios to zero at the lower intensities and thus to reduce the variability of log-ratios for low intensity spots. The optimal choice for the offset is the one which makes the variability of the log-ratios as constant as possible accross the range of intensity values (Smyth, G. in BioC mailing List).   If the 'half' method is chosen for Background Correction, the method will substract the chosen BACKGROUND signal to the chosen FOREGROUND signal, to produce positive corrected intensities according to the 'half' method. If the 'normexp' method  is selected, then a convolution of normal and exponential distributions is fitted to foreground intensities using  background intensities as a covariate, and the expected signal given the observed foreground becomes the corrected intensity. See 'limma' user guide for details.  
对于前景的信号，用户可以选择之间的“MeanSignal”和“ProcessedSignal”之间的“BGMedianSignal”和“BGUsed背景校正。用户可能想看看在不同的图形（密度图等...），以决定什么样的信号更适合使用。有关信号处理的细节，请参阅AFE用户指南。 “MeanSignal是现货原料的平均信号。 “ProcessedSignal”是由安捷伦的特征提取图像分析软件（AFE），信号处理。它包含加减乘法Detrend的Bacground的信号，如果被选中去趋势，它可以帮助。如果去趋势并没有帮助，“ProcessedSignal”将消减Bacground信号。 “BGMedianSignal”是当地的背景信号中位数。 “BGUsed取决于AFE软件设置为背景的方法计算和空间detrend的设置的类型的。通常情况下，所使用的背景信号是当地bacground + AFE的软件计算的空间去趋势的表面价值的总和。要查看使用的不同bakground信号和空间detrend和全球背景的设置，调整用于计算该变量的值，见AFE的用户指南“213页表33。 limma功能的backgroundCorrect用于BG的校正。此功能设计产生积极的强度。降低任何强度值小于0.5，复位等于0.5。此外，50（正常）的常数被用来作为一个偏移，它被添加到log改造前的强度值。这一计算propouse是缩水的log比率为零的低强度，从而减少log比率为低强度点的变异。偏移的最佳选择是其中的log比强度值的范围（史密斯，在G. BioC邮件列表）可能accross不断的变化。如果“一半”的方法是选择背景校正，该方法将所选择的前景信号减去所选择的背景信号，根据“一半”的方法产生积极的纠正强度。如果“normexp”方法被选中，然后安装正常和指数分布的卷积前景强度作为协的背景强度，观测到的前景预期的信号成为纠正的力度。查看详情limma“用户指南。


值----------Value----------

a 'RGList' object, containing in 'RGList\$G' the log-2 normalized intensities
“RGList”的对象，在“RGList \~$ G包含log2归强度


作者（S）----------Author(s)----------


Pedro Lopez-Romero



参考文献----------References----------

high density oligonucleotide array data. Unpublished Manuscript: http://bmbolstad.com/stuff/qnorm.pdf       
(2003), A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185-193.
'Bioinformatics and Computational Biology Solutions Using R and  Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry,  W. Huber (eds), Springer, New York, pages 397 - 420

参见----------See Also----------

Agilent Feature Extraction Reference Guide http://www.Agilent.com See also 'backgroundCorrect' and 'normalizeBetweenArrays' in the limma package and 'vsn' in the vsn package.
安捷伦特征提取参考指南http://www.Agilent.com也backgroundCorrect和normalizeBetweenArrays在limma包和VSN“在VSN包的。


举例----------Examples----------


## Not run: [＃无法运行：]
         data(dd)
         ddNORM=BGandNorm(dd,BGmethod='half',NORMmethod='quantile',
                        foreground='MeanSignal',background='BGMedianSignal',
                        offset=50,makePLOTpre=TRUE,makePLOTpost=TRUE)

## 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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。