RIcorrect(TargetSearch)
RIcorrect()所属R语言包：TargetSearch
Peak picking from CDF files and RI correction
从山顶采摘CDF文件和RI校正
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function reads from CDF files, finds the apex intensities, converts the retention time to retention time index (RI), and writes RI corrected text files.
此功能读取CDF文件，发现心尖强度，转换的保留时间，保留时间指数（RI），并将其写入国际扶轮校正的文本文件。
用法----------Usage----------
RIcorrect(samples, rimLimits = NULL, massRange, Window, IntThreshold,
pp.method = "ppc", showProgressBar = FALSE, baseline = FALSE,
baseline.opts = NULL )
参数----------Arguments----------
参数：samples
A tsSample object created by ImportSamples function.
一个tsSampleImportSamples函数创建的对象。
参数：rimLimits
A tsRim object. If set to NULL, no retention time will be performed. See ImportFameSettings.
一个tsRim对象。如果设置为NULL，没有保留时间将被执行。看到ImportFameSettings。
参数：massRange
A two component vector of m/z range used by the GC-MS machine.
m / z范围的两个组件向量的GC-MS机使用。
参数：Window
The window used for smoothing. The number of points actually used is 2*Window + 1.
该窗口用于平滑。实际使用的点数是2*Window + 1。
参数：IntThreshold
Apex intensities lower than this value will be removed from the RI files.
从RI文件将被删除顶点强度低于此值。
参数：pp.method
Peak picking method. Options are either "smoothing" or "ppc". See details.
山顶采摘方法。选项是“平滑”或“竞价排名”。查看详情。
参数：showProgressBar
Logical. Should the progress bar be displayed?
逻辑。应该显示进度条？
参数：baseline
Logical. Should baseline correction be performed?
逻辑。应进行基线校正？
参数：baseline.opts
A list of options passed to baselineCorrection </table>
一个传递给baselineCorrection</ TABLE>的奖励。选项列表
Details
详情----------Details----------
There are two pick picking methods available: "ppc" and "smoothing".
有两个挑捡可用的方法：“竞价排名”和“平滑”。
The "ppc" method (default) implements the peak detection method described in the ppc package. It looks for the local maxima within a 2*Window + 1 scans for every mass trace.
“竞价排名”的方法（默认）实现ppc包中描述的峰值检测方法的。它看起来2*Window + 1每一个质量跟踪扫描内的局部最大值。
The "smoothing" method calculates a moving average of 2*Window + 1 points for every mass trace. Then it looks for a change of sign (from positive to negative) of the difference between two consecutive points. Those points will be returned as detected peaks.
“平滑”的方法计算了2*Window + 1每一个质量跟踪点的移动平均线。然后，它看起来为标志的变化（从正到负）连续两个点之间的差异。将这些点返回作为检测山峰。
值----------Value----------
A retention time matrix of the found retention time markers. Every column represents a sample and rows RT markers.
一个发现的保留时间标记的保留时间矩阵。每一列代表一个样本，行逆转录标志。
作者（S）----------Author(s)----------
Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig
参见----------See Also----------
ImportSamples, ImportFameSettings, NetCDFPeakFinding, FAMEoutliers,
ImportSamples，ImportFameSettings，NetCDFPeakFinding，FAMEoutliers
举例----------Examples----------
require(TargetSearchData)
# import refLibrary, rimLimits and sampleDescription.[进口refLibrary，rimLimits和sampleDescription。]
data(TargetSearchData)
# get the CDF files[得到CDF文件]
cdfpath <- file.path(.find.package("TargetSearchData"), "gc-ms-data")
cdfpath
list.files(cdfpath)
# update the CDF path[更新CDF路径]
CDFpath(sampleDescription) <- cdfpath
# run RIcorrect (massScanRange = 85-320; Intensity Threshold = 50;[运行RIcorrect（massScanRange = 85-320;强度阈值= 50;]
# peak detection method = "ppc", window = 15)[峰值检测方法=“竞价排名”，“窗口= 15）]
RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320),
Window = 15, pp.method = "ppc", IntThreshold = 50)
# you can try other parameters and other peak picking algorithm.[你可以尝试其他参数和其他高峰采摘算法。]
RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320),
Window = 15, pp.method = "smoothing", IntThreshold = 10)
RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320),
Window = 15, pp.method = "ppc", IntThreshold = 100)
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 2012-2-26 15:24:16
