R语言 xcms包 findPeaks.massifquant-methods()函数中文帮助文档(中英文对照)

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findPeaks.massifquant-methods(xcms)
findPeaks.massifquant-methods()所属R语言包：xcms

                                        Feature detection for high resolution LC/MS data
                                         高分辨率LC / MS数据的功能检测

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Kalman filter based feature detection for high resolution LC/MS data in centroid mode (currently experimental).
卡尔曼滤波高分辨率LC / MS的质心模式中的数据（目前处于试验阶段）基于特征的检测。


参数----------Arguments----------

参数：object
xcmsRaw object
xcmsRaw对象


参数：scanrange
scan range to process scanrange = c(1, lastScan) where lastScan is an integer
扫描范围处理scanrange = c(1, lastScan)其中lastScan是一个整数


参数：minIntensity
All real features should exceed this height.
所有真正的功能应该超过这个高度。


参数：minCentroids
A lower bound for how many scans a feature spans; a feature only incorporates one centroid per scan
下界为多少扫描的功能范围，功能，不仅包括每一个扫描重心


参数：consecMissedLim
As a feature is detected, the Kalman Filter may not find a centroid in every scan; After 1 or more misses, this consecutive missed limit informs massifquant when to stop a Kalman Filter to stop looking for a feature.
卡尔曼滤波作为一个功能检测，可能无法找到一个在每次扫描的重心，经过1个或多个失误，错过这个连续限制通知massifquant的时候停止卡尔曼滤波器停止寻找一个功能。


参数：criticalVal
criticalVal helps determine the error bounds +/- of the Kalman Filter estimate. If the data has very fine mass resolution, a smaller critical val might be better and vice versa. A centroid apart of the feature should fall within these bounds on any given scan. Much like in the construction of a confidence interval, criticalVal loosely translates to be a  multiplier of the standard error estimate reported by the Kalman Filter. It is a relaxed application of the confidence interval because it doesn't change as more data is incorporated into the estimation proces, which would change the degrees of freedom and hence the critical value t.      
criticalVal有助于确定的误差范围+ /  - 卡尔曼滤波估计。如果数据有非常优良的质量分辨率，较小的临界VAL可能是更好的，反之亦然。功能除了一个重心应该落在这些界限内，在任何给定的扫描。就像在置信区间的建设，criticalVal松散转换是一个标准的错误估计卡尔曼滤波报告的乘数。这是一个轻松的置信区间的应用，因为它不会改变，更多的数据被纳入估计议事录，这将改变程度的自由，因此临界值t。


参数：ppm
maximum m/z deviation in consecutive scans by ppm (parts per million).
最大的m / z偏差PPM（百万分之一）的连续扫描。


参数：segs
(segs = 1 #if turned on  segs = 0 #if turned off) With very few data points, sometimes a Kalman Filter "falls off" and stops tracking a feature prematurely. Another Kalman Filter is instantiated and begins following the rest of the signal. Because tracking is done backwards to forwards, this algorithmic defect leaves a real feature divided into two segments (segs for segmentation). With this option turned on, the program identifies segmented features and combines them into one with two sample t-test. The only danger is that samples with time consecutive features that appear conjoined to form a saddle will also be combined.
（SEGS = 1＃如果打开SEGS = 0＃如果关闭）极少数数据点，有时一个卡尔曼滤波器“脱落”，并停止跟踪功能过早。另一个卡尔曼滤波被实例化，并开始后，其余的信号。因为跟踪做向后转发，这个算法的缺陷，留下一个真正的功能分为两部分（分割SEGS）。这个选项打开，方案确定分割的特点，并结合成一个两样本t检验。唯一的危险是出现连体形成一个马鞍与时间连续特性的样品也将被合并。


参数：scanBack
(segs = 1 #if turned on  segs = 0 #if turned off) The convergence of a Kalman Filter to a feature's precise m/z mapping  is very fast, but sometimes it incorporates erroneous centroids as part of a feature (especially early on). The "scanBack" option removes the occasional outlier that lies beyond the converged bounds of the Kalman Filter. The option does not directly affect identification of a feature because it is a postprocessing measure; nonetheless, can potentially improve the quantitation by removing unlikely elements of an established feature.
（SEGS = 1＃如果打开上SEGS = 0＃如果开启关闭），卡尔曼滤波器的收敛性要素的精确的m / z的映射是非常快的，但有时它包含的功能的一部分错误的重心（尤其是早期上） 。的“scanBack”选项删除偶尔离群，卡尔曼滤波融合的边界之外的。选项不直接影响识别的功能，因为它是一个后处理措施;尽管如此，有可能改善消除不太可能建立一个功能元素的定量分析。


Details

详情----------Details----------

This algorithm is most suitable for high resolution LC/{OrbiTrap, TOF}-MS data in centroid mode. Simultaneous kalman filters identify features and calculate their area under the curve.  Originally developed on LTQ Orbitrap data with much less than perfect chromatography, the default parameters are set to that specification. Users will find it useful to do some simple exploratory data analysis to find out where to set a minimum intensity, and identify how many scans an average feature may be. May we suggest using TOPPView as a visualization tool. Historicaly, the consecutiveMissedLim parameter should be set to (2) on Orbitrap data and (1) on TOF data, but never should exceed (4). The criticalVal parameter is perhaps most dificult to dial in appropriately and visual inspection of peak identification is the best suggested tool for quick optimization. The ppm, sets, and scanBack parameters have shown less influence than the other parameters and exist to give users flexibility and better accuracy.
这种算法是最适合高分辨率LC / {的Orbitrap，飞行时间}-MS数据的质心模式。卡尔曼滤波器同时识别功能，并计算曲线下的面积。原定于完美的色谱少得多的LTQ Orbitrap数据而开发的，默认的参数设置该规范。用户会发现它很有用，做一些简单的探索性数据分析，找出在哪里设置的最低强度，并确定平均功能可能多很多扫描。我们可以作为一个可视化工具建议使用TOPPView。 historicaly中，consecutiveMissedLim参数应设置（2）上的Orbitrap数据（1）飞行时间的数据，但不应该超过（4）。 criticalVal参数也许是最dificult拨打峰识别适当的视觉检查是快速优化建议最好的工具。 PPM，套，scanBack参数显示比其他参数的影响较小，并存在向用户提供的灵活性和更好的精度。


值----------Value----------

A matrix with columns:
一个矩阵的列：


参数：mz
weighted mean (by intensity) of feature m/z across scans  
加权平均的特性（强度）米/ž整个扫描


参数：mzmin
m/z peak minimum  
的m / z峰最低


参数：mzmax
m/z peak maximum   
m / z为最大峰值


参数：rt
retention time of peak midpoint estimate  
高峰期的中点估计的保留时间


参数：rtmin
leading edge of peak retention time  
峰的保留时间的领先优势


参数：rtmax
trailing edge of peak retention time  
峰的保留时间后缘


参数：into
integrated peak intensity without any normalization  
综合峰值强度没有任何标准化


参数：maxo
maximum peak intensity   
最大峰值强度


方法----------Methods----------

For Orbitrap Data with poor to acceptable chromatography, suggested default parameters.  findPeaks.massifquant(object, scanrange = c(1, length(object@scantime)),   minIntensity = 6400, minCentroids = 12,   consecMissedLim = 2, criticalVal = 1.7321,   ppm = 10,  segs = 1, scanBack = 1)     
对于穷人接受色谱的Orbitrap数据，建议默认参数。  findPeaks.massifquant(object, scanrange = c(1, length(object@scantime)),   minIntensity = 6400, minCentroids = 12,   consecMissedLim = 2, criticalVal = 1.7321,   ppm = 10,  segs = 1, scanBack = 1)     

For TOF Data with perfect chromatography, suggested default parameters.      findPeaks.massifquant(object, scanrange = c(1, length(object@scantime),   minIntensity = 1800, minCentroids = 6,   consecMissedLim = 1, criticalVal = 0.7111,   ppm = 10,  segs = 1, scanBack = 1)     
完美的色谱飞行时间数据，建议默认参数。      findPeaks.massifquant(object, scanrange = c(1, length(object@scantime),   minIntensity = 1800, minCentroids = 6,   consecMissedLim = 1, criticalVal = 0.7111,   ppm = 10,  segs = 1, scanBack = 1)     


作者（S）----------Author(s)----------


Chris Conley



参考文献----------References----------



参见----------See Also----------

findPeaks-methods xcmsRaw-class
findPeaks-methodsxcmsRaw-class

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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