imputePeaks(flagme)
imputePeaks()所属R语言包：flagme
Imputatin of locations of peaks that were undetected
imputatin的峰的位置，未被发现
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Using the information within the peaks that are matched across several runs, we can impute the location of the peaks that are undetected in a subset of runs
跨越几个运行相匹配的是峰内的信息，我们可以归咎于运行的一个子集未被发现的峰的位置
用法----------Usage----------
imputePeaks(pD, obj, type = 1, obj2 = NULL, filterMin = 3, verbose = TRUE)
参数----------Arguments----------
参数：pD
a peaksDataset object
peaksDataset对象
参数：obj
the alignment object, either multipleAlignment or progressiveAlignment, that is used to infer the unmatched peak locations
对齐对象，无论是multipleAlignment或progressiveAlignment，用来推断无与伦比的峰值位置
参数：type
type of imputation to do, 1 for simple linear interpolation (default), 2 only works if obj2 is a clusterAlignment object
归集做简单的线性插值（默认），1型，2只工程obj2如果是clusterAlignment对象
参数：obj2
a clusterAlignment object
clusterAlignment对象
参数：filterMin
minimum number of peaks within a merged peak to impute
在合并后的峰值推诿最低峰
参数：verbose
logical, whether to print out information
逻辑，无论是打印出来的信息
Details
详情----------Details----------
If you are aligning several samples and for a (small) subset of the samples in question, a peak is undetected, there is information within the alignment that can be useful in determining where the undetected peak is, based on the surrounding matched peaks. Instead of moving forward with missing values into the data matrices, this procedures goes back to the raw data and imputes the location of the apex (as well as the start and end), so that we do not need to bother with post-hoc imputation or removing data because of missing components.
如果您调整几个样品，一个问题的样本（小）的子集，一个高峰是未被发现的，是在对齐，可能是有用的，在确定未被发现的高峰，根据对周围山峰匹配的信息。移动数据矩阵缺失值迈进，而不是这个程序可以追溯到原始数据和责难的顶点的位置（以及开始和结束），所以我们不需要打扰事后归集或删除数据，因为缺少的组件。
We realize that imputation is prone to error and prone to attributing intensity from neighbouring peaks to the unmatched peak. We argue that this is still better than having to deal with these in statistical models after that fact. This may be an area of future improvement.
我们认识到，归集是容易出错，容易归因于从邻国以无与伦比的峰的峰强度。我们认为这仍然是比在统计模型来处理这些事实后更好。这可能是未来的改善方面。
值----------Value----------
list with 3 elements apex, start and end, each masked matrices giving the scan numbers of the imputed peaks.
list3个元素apex，start和end，每个给予蒙面矩阵扫描数字估算峰。
作者（S）----------Author(s)----------
Mark Robinson
参考文献----------References----------
PhD dissertation University of Melbourne.
参见----------See Also----------
multipleAlignment, progressiveAlignment, peaksDataset
multipleAlignment，progressiveAlignment，peaksDataset
举例----------Examples----------
require(gcspikelite)
# paths and files[路径和文件]
gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/")
cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
eluFiles<-dir(gcmsPath,"ELU",full=TRUE)
# read data, peak detection results[读取数据，峰值检测结果]
pd<-peaksDataset(cdfFiles[1:3],mz=seq(50,550),rtrange=c(7.5,8.5))
pd<-addAMDISPeaks(pd,eluFiles[1:3])
# alignments[路线]
ca<-clusterAlignment(pd, gap = .5,D=.05,df=30)
pa<-progressiveAlignment(pd, ca, gap = .6, D=.1,df=30)
v<-imputePeaks(pd,pa,filterMin=1)
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 17:36:04
