R语言 dyebias包 dyebias.apply.correction()函数中文帮助文档(中英文对照)

loveR

dyebias.apply.correction(dyebias)
dyebias.apply.correction()所属R语言包：dyebias

                                        Perform dye bias correction using the GASSCO method
                                         使用GASSCO方法执行染料偏置校正

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Corrects the gene- and slide specific dye bias in a data set, using the GASSCO method by Margaritis et al.
纠正基因和滑动在特定的数据集染料偏见，使用由Margaritis等GASSCO方法。


用法----------Usage----------


                         iGSDBs,
                         estimator.subset=TRUE,
                         application.subset=TRUE,
                         dyebias.percentile=5,
                         minmaxA.perc=25,
                         minA.abs=NULL,                        
                         maxA.abs=NULL,
                         verbose=FALSE)



参数----------Arguments----------

参数：data.norm
A marrayNorm object containing the data whose dye bias should be corrected. This object must be a complete marrayNorm object. In particular, maLabels(maGnames(data.norm)) should be set and indicate the identities of the spots. Spots with the same ID should contain the same oligo or cDNA sequence, and will receive the same dye bias correction.  
一个marrayNorm对象，其中包含的数据，其染料偏见应予以纠正。这个对象必须是一个完整的marrayNorm对象。特别是，maLabels(maGnames(data.norm))应设置并注明景点的身份。具有相同的ID点应该包含相同的寡核苷酸或cDNA序列，将收到相同染料偏置校正。


参数：iGSDBs
A data frame with the intrinsic gene specific dye bias per reporter (i.e., oligo or cDNA).  The data frame would typically have come from a call to dyebias.estimate.iGSDBs, but this is  not necessary; other estimates can also be used.  The data frame must have (at least) the following columns:   
与每记者具体的内在基因染料偏见的数据框（即寡核苷酸或cDNA）。数据框通常会来调用dyebias.estimate.iGSDBs从，但这是没有必要的，也可用于其他的估计。数据框必须有（至少）以下的列：

reporterIdThe name of the reporter. This must match the IDs in   maLabels(maGnames(data.norm))   
记者reporterIdThe名称。这必须匹配的IDmaLabels(maGnames(data.norm))

dyebias An estimate of the dye bias   
dyebias的染料偏见的估计

A  The average expression value A of this reporter. (A =   (log_2(R)+log_2(G))/2 = (log_2(Cy5)+log_2(Cy3))/2 ). The A-value is used to base exclusions on. If you don't have it, you can use any value (but realize that the minmaxA.perc,   minA.abs, maxA.abs arguments are still applied).   
A的平均表现值A记者。 （A =   (log_2(R)+log_2(G))/2 = (log_2(Cy5)+log_2(Cy3))/2 ）。 A值用于基本排除。如果你没有它，你可以使用任何值（但实现的minmaxA.perc,   minA.abs, maxA.abs参数仍然适用）。

           The order of the rows in this data frame is irrelevant. There must be no rows with duplicate reporterId in this frame.   For any reporter in data.norm that is not in the iGSDBs data frame, an iSGDB of 0.00 is used, i.e. data from such reporters is not dye bias-corrected.  
这个数据框中的行的顺序是无关紧要的。必须是有重复的reporterId在这个框架没有任何行。对于记者的任何data.norm不iGSDBs数据框，使用0.00 iSGDB，即数据从这些记者是不染偏置校正。


参数：estimator.subset
An index indicating which reporters are fit to be used as estimators of the slide bias. This set of reporters is used throughout the whole data set. Reporters that are typically excluded are those corresponding to parasitic DNA elements or mitochondrial genes.  
索引表示记者是适合被视为用于幻灯片偏差估计。这一套的记者是用来贯穿整个数据集。记者，通常是排除寄生虫的DNA分子或线粒体基因。


参数：application.subset
An index indicating which values must be dye bias-corrected. It should be either a vector with as many values as spots, or a matrix  of the same dimensions as maM(data.norm). In former case, the selected spots on all slides with be dye bias-corrected; in the latter, selected spots on selected slides will corrected.  Often it is prudent not to dye bias-correct measurements that are close to the detection limit or close to signal saturation.  A convenience function for this is provided; see <br> dyebias.application.subset.  
索引值必须是染料偏置校正。它应该是一个向量斑点尽可能多的价值，或maM(data.norm)尺寸相同的矩阵。在前者情况下，所有幻灯片上选定点与染料偏置校正，在后者中，选定的幻灯片选定点纠正。往往是审慎的，不染，接近检测限或接近信号饱和的偏见，正确的测量。提供一个方便的功能;见参考dyebias.application.subset。


参数：dyebias.percentile
The slide bias estimation uses a small subset of reporters having the strongest green or red iGSDB, as specified by this percentile. The default should suffice in practically all cases.  
幻灯片偏差估计使用一小部分具有最强的绿色或红色的iGSDB，由本百分指定的记者。默认情况下，应足以在几乎所有情况下。


参数：minmaxA.perc
To obtain a robust estimate of the slide bias, the range of the average expression A is trimmed by minmaxA.perc percent on both sides; only reporters lying inside this trimmed range are considered as estimators of the slide bias. The default value is 25, meaning that only probes with an average expression within the interquartile range are considered as estimator genes (from these, the top dyebias.percentile red- and green-biased are then actually used).  The default value should suffice in practically all cases.  
为了获得强大的幻灯片偏见的估计，平均表达A范围minmaxA.perc％修整两侧;幻灯片偏差估计认为躺在这修剪范围内的唯一记者。默认值是25，也就是说，只有四分范围内的平均表达探针认为估计基因（从这些，顶端dyebias.percentile红色和绿色的偏见，然后实际使用）。在几乎所有情况下的默认值应该足以。


参数：minA.abs
If specified, reporters with an average expression (A) lower than this value are never considered as estimators of the slide bias. If not specified, reporters with an A-percentile < minmaxA.perc are not considered.  
如果指定的话，记者的平均表达（A），低于此值是从来没有考虑幻灯片偏差估计。如果未指定，A记者百分<minmaxA.perc不考虑。


参数：maxA.abs
If specified, reporters with an average expression (A) greater than this are never considered as estimators of the slide bias. If not specified, reporters with an A-percentile < 100-minmaxA.perc are not considered.
如果指定，记者平均表达（A）大于这个从来没有考虑幻灯片偏差估计。如果未指定，A记者百分<100-minmaxA.perc不考虑。


参数：verbose
Logical speficying whether to be verbose or not  
逻辑speficying是否详细或不


Details

详情----------Details----------

This function corrects the gene-specific dye bias of two-colour microarrays with the GASSCO method. This method is general, robust and fast, and is based on the observation that the total bias per gene is the product of a slide-specific factor (strongly related to the labeling percentage) and an intrinsic gene-specific factor (iGSDB), which is strongly related to the probe sequence.
此功能纠正两色与GASSCO方法微阵列的基因特定的染料偏见。这种方法一般情况下，稳健和快速，根据观察，每个基因的总偏差是的幻灯片特定因素（密切相关的标签百分比）和内在的特定基因因子（iGSDB）的，产品上是密切相关的探针序列。

The slide bias is estimated from the total bias of the dyebias.percentile percentage of reporters having the strongest iGSDB. The iGSDBs can be estimated with <br> dyebias.estimate.iGSDBs.
幻灯片偏见估计从dyebias.percentile有最强iGSDB记者百分比的总偏差。可以预计的iGSDBs与参考的dyebias.estimate.iGSDBs。

If the signal of certain oligos is too weak, or in contrast, tends to be saturated, they are no good estimator of the slide bias. Therefore, only reporters with an average expression level A that is not too extreme are allowed to be slide bias estimators. (This is the reason for the A-column in the iGSDBs data frame).
如果某些寡核苷酸的信号太弱，或与此相反，趋于饱和，他们是没有偏见的幻灯片的良好估计。因此，只用一个平均的表达水平的记者A，是不是太极端幻灯片偏差估计。 （这是为AiGSDBs数据框列的原因）。

Full control over which reporters to allow as slide bias estimators is given by the arguments minmaxA.perc, minA.abs, and maxA.abs; see there for details. To not exclude any reporter (e.g., when A is not available and therefore artificially set), you can use minA.abs= -Inf and maxA.abs = Inf.
记者让幻灯片偏差估计是完全控制的论点minmaxA.perc，minA.abs和maxA.abs;详见。不排除任何记者（例如，当A是不是可用，因此，人为地设置），你可以使用minA.abs= -Inf和maxA.abs = Inf。

For further details concerning the method, see the dyebias vignette and the publication. If your research benefits from using this package, we kindly request that you cite this work.
对于有关该方法的进一步详情，请参阅dyebias暗角和出版物。如果您使用此程序包研究的好处，我们恳请您举出这项工作。


值----------Value----------

The data returned is a list wit the following elements
返回的数据是一个列表机智以下元素


参数：data.corrected
A marrayNorm object of the same 'shape' as the input data.norm, but with corrected M values.  
一个marrayNorm纠正data.norm，但M值作为输入相同的“形”的对象。


参数：estimators
Another list, containing the details of the reporters that were used to obtain an estimate of the slide bias.  The contents of the estimators list are:   
另一份列表，其中包含的记者被用来获得一个幻灯片偏见的估计细节。 estimators列表的内容是：

green.idsThe IDs of the reporters having the strongest green effect.  
有记者最强的绿色效果green.idsThe标识。

green.cutoffAll reporters in green.ids have an iGSDB below this value.  
在green.ids green.cutoffAll记者iGSDB低于此值。

green.subsetAn index into the reporters having the strongest green effect.  
green.subsetAn指数的记者有最强的绿色效果。

green.iGSDBsThe corresponding iGSDBs  
green.iGSDBsThe相应iGSDBs

red.idsThe IDs of the reporters having the strongest red effect.  
有红色效果最强的记者red.idsThe标识。

red.cutoffAll reporters in green.ids have an iGSDB above this value.  
在green.ids red.cutoffAll记者iGSDB高于此值。

red.subsetAn index into the reporters having the strongest red effect.  
red.subsetAn指数将有红色效果最强的记者。

red.iGSDBsThe corresponding iGSDBs  
red.iGSDBsThe相应iGSDBs


参数：summary
A data frame summarizing the correction process per slide. It consist of the following columns:   
一个数据框总结每张幻灯片的校正过程。它包括以下几列：

slideThe slide number  
slideThe幻灯片编号

fileWhich file it came from  
来自fileWhich文件

green.correctionThe slide bias based on only the green bias of this slide  
green.correctionThe幻灯片偏见的基础上仅此幻灯片的绿色偏见

red.correctionThe slide bias based on only the red bias of this slide  
根据这张幻灯片只有红色的偏见red.correctionThe幻灯片偏见

avg.correctionThe total correction factor of this slide. This is in fact the slide bias  
avg.correctionThe幻灯片总校正因子。这其实是在幻灯片偏见

var.ratioThe ratio of the variance of M after and before the correction. The smaller this number, the smaller the variance of M around the mean has become, providing a measure of the success of the dye bias correction. Only data points that were in the application.subset are considered.  
var.ratioThe方差比M校正后和前。这个数字越小，规模较小的方差M平均已成为各地，提供了一个成功的染料偏置校正的措施。只有数据点在application.subset被视为。

reduction.percAs var.ratio, but expressed as a percentage. The larger this value, the greater the correction.  
reduction.percAsvar.ratio，但以百分比表示。这个值越大，更大的修正。

p.valueThe p-value for the signficance of the reduction in variance (F-test; H_0: variances before and after correction are identical)  
p.valueThe方差减少的建设的重大意义（F测H_0：差异校正前后是相同的p值）


参数：data.uncorrected
The uncorrected input marrayNorm, for convenience
裸marrayNorm，为了方便输入


注意----------Note----------

Note that the input data should be normalized, and that the dye swaps should <STRONG>not</STRONG> have been swapped back (if needed, this can of course be done afterwards).
注意，输入数据应标准化，并染料互换<strong>不会</强>已交换（如果需要的话，这当然可以进行事后）。


作者（S）----------Author(s)----------


Philip Lijnzaad <a href="mailto:p.lijnzaad@umcutrecht.nl">p.lijnzaad@umcutrecht.nl</a>



参考文献----------References----------

Kemmeren, P., van Hooff, S.R and Holstege, F.C.P. (2009). Adaptable gene-specific dye bias correction for two-channel DNA microarrays. Molecular Systems Biology, 5:266, 2009. doi: 10.1038/msb.2009.21.

参见----------See Also----------

dyebias.estimate.iGSDBs, dyebias.application.subset, dyebias.rgplot, dyebias.maplot, dyebias.boxplot, dyebias.trendplot
dyebias.estimate.iGSDBs，dyebias.application.subset，dyebias.rgplot，dyebias.maplot，dyebias.boxplot，dyebias.trendplot


举例----------Examples----------


  ## First load data and estimate the iGSDBs[＃第一负荷数据和估计的iGSDBs]
  ## (see dyebias.estimate.iGSDBs)[＃（见dyebias.estimate.iGSDBs）]

                                      

  ### choose the estimators and which spots to correct:[＃选择的估计和景点，以纠正：]
  estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm)))

  ### choose which genes to dye bias correct:[＃选择哪些基因染色偏见正确：]
  application.subset <- (maW(data.norm) == 1 &amp;
               dyebias.application.subset(data.raw=data.raw, use.background=TRUE))

  ### do the correction:[＃做更正：]
  correction <- dyebias.apply.correction(data.norm=data.norm,
                                         iGSDBs = iGSDBs.estimated,
                                         estimator.subset=estimator.subset,
                                         application.subset = application.subset,
                                         verbose=FALSE)
  
  ## Not run: [＃无法运行：]
     edit(correction$summary)
  
## End(Not run)[＃结束（不运行）]

  ## give overview:[＃给予概述：]
  correction$summary[,c("slide", "file", "avg.correction", "reduction.perc", "p.value")]

  ## and summary:[＃和总结：]
  summary(as.numeric(correction$summary[, "reduction.perc"]))

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。