groupCorr(CAMERA)
groupCorr()所属R语言包：CAMERA
EIC correlation grouping of LC/ESI-MS data
三电相关的LC / ESI-MS的数据分组
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Peak grouping after correlation information into pseudospectrum groups for an xsAnnotate object. Return an xsAnnotate object with grouping information.
山顶分组后伪谱组到为xsAnnotate对象的相关信息。返回一个分组信息xsAnnotate对象。
用法----------Usage----------
groupCorr(object,cor_eic_th=0.75, pval=0.05, graphMethod="hcs",
calcIso = FALSE, calcCiS = TRUE, calcCaS = FALSE, psg_list=NULL, xraw=NULL, ...)
参数----------Arguments----------
参数：object
The xsAnnotate object
xsAnnotate对象
参数：cor_eic_th
Correlation threshold for EIC correlation
三电相关的相关阈值
参数：pval
p-value threshold for testing correlation of significance
p值阈值测试具有重要意义的相关性
参数：graphMethod
Clustering method for resulting correlation graph. See calcPC for more details.
聚类方法产生的关联图。 calcPC看到更多细节。
参数：calcIso
Include isotope detection informationen for graph clustering
包括同位素检测图聚类informationen
参数：calcCiS
Calculate correlation inside samples
计算样本内的相关
参数：calcCaS
Calculate correlation accross samples
计算相关accross样本
参数：psg_list
Vector of pseudospectra indices. The correlation analysis will be only done for those groups
向量的伪谱指数。相关分析将只为这些群体
参数：xraw
Optional xcmsRaw object, which should be used for raw data extraction
可选xcmsRaw对象，应使用原始数据提取
参数：...
Additional parameter
额外的参数
Details
详情----------Details----------
The algorithm calculates different informations for group peaks into so called pseudospectra. This pseudospectra contains peaks, with have a high correlation between each other. So far three different kind of information are available. Correlation of intensities across samples (need more than 3 samples), EIC correlation between peaks inside a sample and additional the informationen about recognized isotope cluster can be included. After calculation of all these informations, they are combined as edge value into a graph object. A following graph clustering algorithm separate the peaks (nodes in the graph) into the pseudospectra.
该算法计算成所谓的不同伪谱组峰的信息。包含此伪谱峰，有一个相互之间的高相关。到目前为止，三个不同种类的信息可用。各样品的强度（需要超过3个样本）的相关性，可以包含内样品的峰之间的和额外的关于确认同位素聚类informationen三电相关。所有这些信息计算后，他们结合成一个图形对象的边缘价值。一个如下图的聚类算法分割成伪谱峰（图中的节点）。
作者（S）----------Author(s)----------
Carsten Kuhl <ckuhl@ipb-halle.de>
参见----------See Also----------
calcCiS calcCaS calcPC xsAnnotate-class
calcCiScalcCaScalcPCxsAnnotate-class
举例----------Examples----------
library(CAMERA)
file <- system.file('mzdata/MM14.mzdata', package = "CAMERA");
xs <- xcmsSet(file, method="centWave", ppm=30, peakwidth=c(5, 10));
an <- xsAnnotate(xs);
an.group <- groupFWHM(an);
an.iso <- findIsotopes(an.group); #optional step for using isotope information[使用同位素信息的可选步骤]
an.grp.corr <- groupCorr(an.iso, calcIso=TRUE);
#For csv output[对于CSV输出]
# write.csv(file="peaklist_with_isotopes.csv",getPeaklist(an))[write.csv（文件=的“peaklist_with_isotopes.csv”，getPeaklist（））]
#Multiple sample [多个样品]
library(faahKO)
xs.grp <- group(faahko)
#With selected sample[随着抽选的样本]
xsa <- xsAnnotate(xs.grp, sample=1)
xsa.group <- groupFWHM(xsa)
xsa.iso <- findIsotopes(xsa.group) #optional step[可选步骤]
xsa.grp.corr <- groupCorr(xsa.iso, calcIso=TRUE)
#With automatic selection[具有自动选择]
xsa.auto <- xsAnnotate(xs.grp)
xsa.grp <- groupFWHM(xsa.auto)
xsa.iso <- findIsotopes(xsa.grp) #optional step[可选步骤]
index <- c(1,4) #Only group one and four will be calculate[只有一和四组将计算]
#We use also correlation across sample[我们还使用跨样本相关]
xsa.grp.corr <- groupCorr(xsa.iso, psg_list=index, calcIso=TRUE, calcCaS=TRUE)
#Note: Group 1 and 4 have no subgroups[注：第1组和4有没有群]
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 14:04:44
