dyebias.estimate.iGSDBs(dyebias)
dyebias.estimate.iGSDBs()所属R语言包：dyebias
Estimate intrinsic gene specific dye biases (part of the GASSCO method)
估计内在基因的特定染料的偏见（在GASSCO方法的一部分）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Obtain estimates for the instrinsic gene-specific dye bias (iGSDB) using a set of normalized data, as part of the GASSCO method.
获得禀基因特定的染料偏见（iGSDB）估计，使用了一套规范化的数据，作为的GASSCO方法。
用法----------Usage----------
dyebias.estimate.iGSDBs(data.norm, is.balanced=TRUE, reference="ref", verbose=FALSE)
参数----------Arguments----------
参数：data.norm
A marrayNorm object containing the data for estimating the dye bias. This object is supposed to be complete. In particular, <br> maLabels(maGnames(data.norm)) must be set and must indicate the identities of the reporter sequence (i.e., oligo or cDNA sequence) of each spot. This helps identify replicate spots, which are averaged as part of the estimation. If the data is unbalanced (so is.balanced is FALSE), <br> maInfo(maTargets(data.norm)) is also required, and should contain at least two attributes: Cy5 and Cy3. Both should indicate the factor value for the respective channel.
一个marrayNorm对象，其中包含的数据估算，染料偏见。这个对象应该是完整的。尤其是参考maLabels(maGnames(data.norm))必须设置必须表明身份的记者序列的每一个点（即，寡核苷酸或cDNA序列）。这有助于识别复制的斑点，这是平均估计。如果数据是不平衡的（所以is.balanced是FALSE），参考maInfo(maTargets(data.norm))也是必需的，而且应该包含至少两个属性：Cy5和Cy3 。双方应标明各通道的因子值。
参数：is.balanced
The use of this argument is discouraged, since designs should generally be balanced. The values other than TRUE will become illegal in the future. Logical indicating whether the data set represents a balanced design (which is by far the most common case). A design is balanced if all factor values are present an equal number of times in both the forward and reverse dye orientations. A self-self design is by definition balanced (even if the number of slides is uneven). If is.balanced is TRUE, the iGSDB estimate is obtained by simply averaging, per reporter, all M values (and the value of the reference argument is ignored). If is.balanced==FALSE, the design is inferred from the reference argument, and subsequently the limma package is used to model the dye effect. This is typically done for an unbalanced data set, but there is no harm in setting is.balanced=FALSE for a design that by itself is already balanced. If there are no missing values in the data, the results of using the simple average and the limma procedure are identical (although LIMMA takes longer to compute the iGSDBs). If the data set contains many missing data points (NA's), the limma estimates differ slightly from the simple averaged estimates (although it is not clear which ones are better).
使用这种说法是不鼓励，因为设计一般应平衡。值比TRUE将成为在非法未来。逻辑表示数据集是否代表一个平衡的设计（这是迄今为止最常见的情况）。一个设计是平衡的，如果所有因素值都存在正向和反向的染料取向倍的人数相等。自行设计自定义均衡（即使数量是不平衡的幻灯片）。如果is.balanced的TRUE中，iGSDB估计是由简单平均获得，每记者，所有M的值（reference参数将被忽略的价值）。如果is.balanced==FALSE设计reference参数推断，随后limma包用于模拟染料的效果。这通常是一个不平衡的数据集，但没有什么坏处设置is.balanced=FALSE设计本身已经平衡。如果有使用简单平均limma的过程中的数据没有缺失值，结果是相同（，虽然LIMMA需要更长的时间来计算的iGSDBs的）。如果数据集包含了许多丢失的数据点（北美），limma估计从简单的平均估计略有不同（虽然目前尚不清楚哪些是更好的）。
参数：reference
If the design contains a single common reference, the reference argument should be this common reference (which may not be empty). If the design contains multiple common references, reference should be a vector listing all the common references, and the name of the factor value that is not the common reference should have its own common reference as a prefix. E.g., if two mutant strains mutA and mutB were assayed, each against a separate common reference ref1 and ref2, the reference-argument would be c("ref1", "ref2"), and the Cy3 and Cy5 attributes of maInfo(maTargets(data.norm)) would be values from "ref1:mutA", "ref2:mutA", "ref1:mutB", "ref2:mutB". The colon is not important, but the prefix is, as it allows the association of each sample with its 'own' common reference.
如果设计中包含一个共同的参考，reference参数应该是这个共同的参考（可能不为空）。如果设计中包含了多个共同的参照标准，reference应该是一个向量上市的所有共同引用，是不是共同的参考因素值的名称应该有自己的作为前缀共同的参考。例如，如果两个突变株mutA和mutB进行检测，对每个单独的共同参考ref1和ref2，reference参数将是c("ref1", "ref2")，Cy3和Cy5属性maInfo(maTargets(data.norm))是"ref1:mutA", "ref2:mutA", "ref1:mutB", "ref2:mutB"值。结肠并不重要，但前缀是，因为它允许每个样品的关联与它自己的“共同参考。
参数：verbose
Logical, indicating wether or not to be verbose.
逻辑，表明不论有无不详细。
Details
详情----------Details----------
This function implements the first step of the GASSCO method: estimating the so-called intrinsic gene specific dye biases, or briefly iGSDB. They can be estimated from a (preferably large) data set containing either self-self experiments, or dye-swapped slides.
此功能实现了第一步：的GASSCO方法估算所谓的内在基因的具体染料偏见，或简要iGSDB。他们可估计（最好大）设置数据，要么自我自我实验或染料交换的幻灯片。
The assumption underlying this approach is that with self-selfs, or with pairs of dye swaps, the only effect that can lead to systematic changes between Cy5 and Cy3, is in fact the dye effect.
这种方法的假设是，与自我selfs，或对染料互换，唯一的影响，可能导致系统的变化之间Cy5和Cy3标记，在事实上染料的效果。
There are two cases to distinguish, the balanced case, and the unbalanced case. In the balanced case, the iGSDB estimate is simply the average M (where <br> M = log_2(R/G) = log_2(Cy5/Cy3)) over all slides. A set of slides is balanced if all factor values are present in as many dye-swapped as non-dye-swapped slides. A set of self-self slides is in fact a degenerate form of this, and is therefore also balanced.
有两个区分的情况下，均衡的情况下，不平衡的情况。在平衡的情况下，iGSDB估计是简单平均M（其中参考M = log_2(R/G) = log_2(Cy5/Cy3)）对所有幻灯片。一组幻灯片是平衡的，如果所有因素值是目前在许多非染料交换幻灯片染料交换。一套自我自我幻灯片的，是在事实上退化这种形式，因此也是平衡的。
In the unbalanced case, one could omit slides until the data set is balanced. However, this is wasteful as we can use linear modelling to obtain estimates. We use the limma package for this (Smyth, 2005). The only unbalanced designs currently supported are a common reference design, and a set of common reference designs.
在不平衡的情况下，可以省略，直到平衡数据集的幻灯片。然而，这是一种浪费，因为我们可以用线性模型获得估计。我们使用limma包（史密斯，2005）。目前支持的唯一的不平衡的设计，是一个共同的参考设计，以及一套共同的参考设计。
There are no weights or subset argument to this function; the estimation is done for all reporters found. If there are replicate spots, they are averaged prior to the estimation (the reason being that we are not interested in p-values for the estimate)
有没有重量或此功能的子集参数;估计是所有记者发现。如果有重复的点，他们平均估计之前（原因是，我们不感兴趣的p-值估计）
Having obtained the iGSDB estimates, the corrections can be applied to either to the hybridizations given by the data.norm argument, or to a different set of slides that is thought to have very similar iGSDBs. Applying the corrections is done with <br> dyebias.apply.correction.
在获得iGSDB估计，可以申请更正要么data.norm的说法，或以一组不同的幻灯片，被认为有非常类似的iGSDBs的杂交的。申请更正完成参考dyebias.apply.correction。
值----------Value----------
A data frame is returned with as many rows as there are reporters (replicate spots have been averaged), and the following columns: <br>
有记者（复制点已被平均），下面的列：参考一个数据框返回许多行
参数：reporterId
The name of the reporter
记者名称
参数：dyebias
The intrinsic gene-specific dye bias (iGSDB) of this reporter
内在的特定基因的染料偏见（iGSDB）记者
参数：A
The average expression level of this reporter in the given data set
记者在给定的数据集的平均表达水平
参数：p.value
The p-value for the dyebias ($H_0$: dyebias = 0). All p-value are set to NA if they were not estimated (i.e., if limma was not run because is.balanced was TRUE)
dyebias（H_0 $：$dyebias= 0），p值。所有的p值都设置为NA如果他们没有估计（即如果limma无法运行，因为is.balanced是真的）
This data frame is typically used as input to dyebias.apply.correction.
通常被用作输入dyebias.apply.correction这个数据框。
注意----------Note----------
Note that the input data should be normalized, and that the dye swaps should <STRONG>not</STRONG> have been swapped back. After all, we're interested in the difference of Cy5 over Cy3, <STRONG>not</STRONG> the difference of experiment over reference.
请注意，输入数据应标准化，染料互换<strong>不会</ STRONG>已换回来。毕竟，我们在Cy5的差异超过Cy3标记感兴趣，<STRONG>不会</ strong>超过参考实验的区别。
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.
exploratory analysis and normalization of cDNA microarray data. In: Parmigiani, G., Garrett, E.S. , Irizarry, R.A., and Zeger, S.L. (eds.) The Analysis of Gene Expression Data: Methods and Software, Springer, New York.
In: Gentleman, R., Carey, V., Dudoit, S., Irizarry, R. and Huber, W. (eds). Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New York.
参见----------See Also----------
dyebias.apply.correction
dyebias.apply.correction
举例----------Examples----------
iGSDBs.estimated <- dyebias.estimate.iGSDBs(data.norm,
is.balanced=TRUE,
verbose=FALSE)
summary(iGSDBs.estimated)
## Not run: [＃无法运行：]
hist(iGSDBs.estimated$dyebias, breaks=50)
## End(Not run)[＃结束（不运行）]
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注：
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发表于 2012-2-25 16:49:50
