normalizePlates(cellHTS2)
normalizePlates()所属R语言包：cellHTS2
Per-plate data transformation, normalization and variance adjustment
每盘数据的调整改造，规范化和方差
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plate-by-plate normalization of the raw data stored in slot assayData of a cellHTS object. Normalization is performed separately for each plate, replicate and channel. Log2 data transformation can be performed and variance adjustment can be performed in different ways (none, per-plate, per-batch or per-experiment).
板的板槽assayDatacellHTS对象中存储的原始数据的标准化。分别为每块板，复制和渠道进行标准化。 Log2进行数据转换和方差可以以不同的方式进行调整（无，每板，每批次或每实验）。
用法----------Usage----------
normalizePlates(object, scale="additive", log=FALSE, method="median", varianceAdjust="none", posControls, negControls,...)
参数----------Arguments----------
参数：object
a cellHTS object that has already been configured. See details.
cellHTS已配置的对象。查看详情。
参数：scale
a character specifying the scale that the input data are considered to be on: "additive" scale (default) or "multiplicative". The interpretation of this terminology is that data on an additive scale will be normalised by subtraction of a correction offset, whereas data on a multiplicative scale are normalised by division through a correction factor.
一个字符指定输入数据被认为是规模：“添加剂”的规模（默认）或“乘法”。这个术语的解释是，添加剂尺度上的数据将被抵消，而乘法规模的数据通过修正系数由部门归改正减法标准化。
参数：log
logical. If TRUE, data will first be log2 transformed. If data are on an additive scale (i.e. if scale is "additive"), then log is only allowed to be FALSE. The default is log=FALSE.
逻辑。如果TRUE，数据将首先log2转化。如果数据是一种添加剂规模（即如果scale是"additive"），然后log只允许FALSE。默认log=FALSE。
参数：method
character specifying the normalization method to use for the per-plate normalization. Allowed values are "median" (the default), "mean", "shorth", "POC", "NPI", "negatives", Bscore and "locfit". See details.
字符指定的规范化方法，使用的每板标准化。允许的值是"median"（默认），"mean"，"shorth"，"POC"，"NPI"，"negatives"，Bscore和 "locfit"。查看详情。
参数：varianceAdjust
character specifying the variance adjustment to perform. Allowed values are "none" (the default), code"byPlate", "byBatch" and "byExperiment". See details.
字符指定执行的方差调整。允许值是："none"（默认），代码“byPlate”，"byBatch"和"byExperiment"。查看详情。
参数：posControls
a vector of regular expressions giving the name of the positive control(s). See details.
给予阳性对照（S）的名称的正则表达式的向量。查看详情。
参数：negControls
a vector of regular expressions giving the name of the negative control(s). See details.
给予的负面控制（S）的名称的正则表达式的向量。查看详情。
参数：...
Further arguments that get passed on to the function implementing the normalization method chosen by method. Currently, this is only used for Bscore and locfit.
进一步的参数被传递到功能实现标准化的方法method选择。目前，这仅仅是用于Bscore和locfit。
Details
详情----------Details----------
The function normalizePlates uses the content of the assayData slot of object. For dual-channel data, a recommended workflow is (i) to correct for plate effects using the normalizePlates function, (ii) combine the two channels using the function summarizeChannels, and (iii) finally, if necessary, normalize the summarized intensities calling normalizePlates again.
功能normalizePlates使用assayDataobject槽的内容。对于双通道数据，建议的工作流程是：（一）纠正板使用normalizePlates功能，（二）结合使用功能summarizeChannels，及（iii）最后两个通道的影响，如果必要时，标准化调用normalizePlates再次总结强度。
In this function, the normalization is performed in a plate-by-plate fashion, following this workflow:
在这个函数中，标准化进行一盘盘时尚，这个工作流程：
Log transformation of the data (optional)
登录数据转换（可选）
Per-plate normalization
每板标准化
Variance adjustment of the plate intensity corrected data (optional)
方差的钢板强度的调整修正后的数据（可选）
The argument scale defines the scale of the data. If the data are on a multiplicative scale (scale="multiplicative"), the data can be log2 transformed by setting log=TRUE. This then changes the scale of the data to code"additive".
参数scale定义数据的规模。如果数据是一个乘法的规模（scale="multiplicative"），这些数据可以是log2设置log=TRUE转化。这就改变了数据代码“添加剂”的规模。
In the next step of preprocessing, intensities are corrected in a plate-by-plate basis using the chosen normalization method:
在下一步的预处理，强度中板通过板使用所选的标准化方法的基础上得到纠正：
If method="median", plates effects are corrected by the median value across wells that are annotated as sample in wellAnno(object), for each plate and replicate.
如果method="median"，板块的影响是跨越井的中间值，标注为纠正samplewellAnno(object)，为每个板块和复制。
If method="mean", the average in the sample wells is used instead.
如果method="mean"，平均sample井来代替。
If method="shorth", the midpoint of the shorth of the distribution of values in the wells annotated as sample is used.
如果method="shorth"，shorth注明sample使用的水井中的值分布的中点。
If method="negatives", the median of the negative controls is used.
如果method="negatives"，阴性对照组的中位数。
Depending on the scale of the data prior to normalization, the data are divided by the above defined correction factors (scale: "multiplicative"), or the value is subtracted (scale: "additive").
根据数据规模标准化之前，数据被分为上述定义的校正因子（规模："multiplicative"），或该值减去（规模："additive"）。
Further available normalization methods are:
进一步提供规范化方法是：
method="POC" (percent of control): for each plate and replicate, each measurement is divided by the average of the measurements on the plate positive controls, and multiplied by 100.
method="POC"（％）控制：每个板和复制，每个测量所测量的平均分为阳性对照盘子上，再乘以100。
method="NPI" (normalized percent inhibition): each measurement is subtracted from the average of the intensities on the plate positive controls, and this result is divided by the difference between the means of the measurements on the positive and the negative controls.
method="NPI"（归％抑制）：每次测量中减去从平均强度板阳性对照，这一结果是积极的测量手段和阴性对照组之间的差异。
method="Bscore": for each plate and replicate, the B-score method, which is based on a 2-way median polish, is applied to remove row and column biases.
method="Bscore"：B-score method，这是一个2路的中位数为波兰的基础上，为每个板块和复制，删除行和列的偏见。
method="locfit" (robust local fit regression): for each plate and replicate, spatial effects are removed by fitting a bivariate local polynomial regression (see spatialNormalization).
method="locfit"（强大的本地合适的回归）：为每个板和复制，空间效应拟合二元局部多项式回归拆除（见spatialNormalization）。
In the final preprocessing step, variance of plate-corrected intensities can be adjusted as follows:
在最后的预处理步骤，校正板强度的差异，可以作如下调整：
varianceAdjust="byPlate": per plate normalized intensities are divided by the per-plate median absolute deviations (MAD) in "sample" wells. This is done separately for each replicate and channel;
varianceAdjust="byPlate"：每板归强度分为每板中位数绝对偏差（MAD）的“样本”井。这是分别为每个复制和渠道;
varianceAdjust="byBatch": using the content of slot batch, plates are split according to assay batches and the individual normalized intensities in each group of plates (batch) are divided by the per-batch of plates MAD values (calculated based on "sample" wells). This is done separately for each replicate and channel;
varianceAdjust="byBatch"：使用槽的内容batch，板块分裂根据检测批次，并在每个板组（批次）的个人归强度除以板疯狂值每批次（计算“样本”井）的基础上。这是分别为每个复制和渠道;
varianceAdjust="byExperiment": each normalized measurement is divided by the overall MAD of normalized values in wells containing "sample". This is done separately for each replicate and channel;
varianceAdjust="byExperiment"：各归测量分为整体MAD井归值包含“样本”。这是分别为每个复制和渠道;
By default, no variance adjustment is performed (varianceAdjust="none").
默认情况下，没有差异调整（varianceAdjust="none"）。
The arguments posControls and negControls are required for applying the normalization methods based on the control measurements that is, when method="POC", or method="NPI", or method="negatives"). posControls and negControls should be vectors of regular expression patterns specifying the name of the positive(s) and negative(s) controls, respectivey, as provided in the plate configuration file (and accessed via wellAnno(object)). The length of these vectors should be equal to the current number of channels in object (i.e. to the dim(Data(object))[3]). By default, if posControls is not given, pos will be taken as the name for the wells containing positive controls. Similarly, if negControls is missing, by default neg will be considered as the name used to annotate the negative controls. The content of posControls and negControls will be passed to regexpr for pattern matching within the well annotation given in the featureData slot of object (which can be accessed via wellAnno(object)) (see examples for summarizeChannels). The arguments posControls and negControls are particularly useful in multi-channel data since the controls might be reporter-specific, or after normalizing multi-channel data.
的论点posControls和negControls申请的标准化是控制测量方法的要求，当method="POC"或method="NPI"或method="negatives"）。 posControls和negControls应该是正则表达式模式（S）（S）的积极和消极的控制，respectivey板配置文件的规定，（并通过访问指定名称的向量<X >）。这些向量的长度应该等于目前有多少渠道在wellAnno(object)（即到object）。默认情况下，如果dim(Data(object))[3]没有给出，POS将视为包含阳性对照井的名称。同样，如果posControls缺少，默认情况下，NEG将被视为阴性对照用来注释的名称。 negControls和posControls将通过内的featureData插槽，以及注释的模式匹配negControlsregexpr（可通过<X的内容>）（object）看到的例子。论据wellAnno(object)和summarizeChannels是特别有用的，因为在多通道数据的控制可能是特定的记者，或标准化后多通道数据。
See the Examples section for an example on how this function can be used to apply a robust version of the Z score method, whereby, for each plate and replicate, the per-plate median (computed only from sample wells) is subtracted from the measurements, and the result is divided by the per-plate MAD (only from sample wells).
看到此功能可以用来申请的Z评分法的强大的版本，每板中位数（只计算从样本井），即每盘和复制，从测量结果中减去一个例子举例，并把结果除以每板疯狂（只从样品孔）。
值----------Value----------
An object of class cellHTS with the normalized data stored in slot assayData (its previous contents were overridden). The processing status of the object is updated in the slot state to object@state[["normalized"]]=TRUE.
一个类的对象cellHTS规范化的数据存储在插槽assayData（它以前的内容覆盖）。处理状态的object更新插槽stateobject@state[["normalized"]]=TRUE的。
Additional slots of object may be updated if method="Bscore" or method="locfit" are used. Please refer to the help page of the Bscore function and spatialNormalization functions.
object额外插槽如果method="Bscore"或method="locfit"用于可更新。 Bscore函数和spatialNormalization功能，请参阅帮助页面。
作者（S）----------Author(s)----------
Ligia Bras <a href="mailto:ligia@ebi.ac.uk">ligia@ebi.ac.uk</a>, Wolfgang Huber <a href="mailto:huber@ebi.ac.uk">huber@ebi.ac.uk</a>
参考文献----------References----------
参见----------See Also----------
Bscore, spatialNormalization, summarizeChannels
Bscore，spatialNormalization，summarizeChannels
举例----------Examples----------
data(KcViabSmall)
# per-plate median scaling of intensities[每板中位数扩大的强度]
x1 <- normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="none")
# per-plate median subtraction of log2 transformed intensities [每板中位数减法log2转化强度]
x2 <- normalizePlates(KcViabSmall, scale="multiplicative", log=TRUE, method="median", varianceAdjust="none")
## Not run: [＃无法运行：]
x3 <- normalizePlates(KcViabSmall, scale="multiplicative", log=TRUE, method="Bscore", varianceAdjust="none", save.model=TRUE)
## End(Not run)[＃结束（不运行）]
## robust Z score method (plate intensities are subtracted by the per-plate median on sample wells and divided by the per-plate MAD on sample wells):[＃强大的Z评分法（减去钢板强度由每板样品孔位数除以样本井每板MAD）：]
xZ <- normalizePlates(KcViabSmall, scale="additive", log=FALSE, method="median", varianceAdjust="byPlate")
## an example to illustrate the use of slot 'batch':[＃使用槽“批处理”的一个例子来说明：]
## Not run: [＃无法运行：]
try(xnorm <- normalizePlates(KcViabSmall, scale="multiplicative", method="median", varianceAdjust="byBatch"))
# It doesn't work because we need to have slot 'batch'![它不工作，因为我们需要有槽“批处理”！]
# For example, we will suppose that a different lot of reagents was used for plate 1:[例如，我们会想很多不同的试剂板1：]
pp <- plate(KcViabSmall)
fData(KcViabSmall)$"reagent" <- "lot B"
fData(KcViabSmall)$"reagent"[pp==1] <- "lot A"
fvarMetadata(KcViabSmall)["reagent",] <- "Lot of reagent used"
bb <- as.factor(fData(KcViabSmall)$"reagent")
batch(KcViabSmall) <- array(as.integer(bb), dim=dim(Data(KcViabSmall)))
## check number of batches:[＃检查批数量：]
nbatch(KcViabSmall)
x1 <- normalizePlates(KcViabSmall, scale="multiplicative", log = FALSE, method="median", varianceAdjust="byBatch")
## 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。
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发表于 2012-2-25 14:17:08
