norm(MANOR)
norm()所属R语言包：MANOR
Normalize an object of type arrayCGH
标准化型arrayCGH对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Normalize an object of type arrayCGH using a list of criteria specified as (temporary or permanent) flags. If a replicate identifier (clone name) is provided, a single target value is computed for all the replicates.
标准化类型arrayCGH使用（临时或永久）标志指定的标准列表的对象。如果复制的的标识符（克隆姓名）是提供一个单一的目标价值为所有的重复计算。
The normalization coefficient is computed as a function, and is applied to all good quality spots of the array.
作为一个函数的归一化系数计算，并应用到所有的阵列的质量好点。
用法----------Usage----------
## S3 method for class 'arrayCGH'
norm(arrayCGH, flag.list=NULL, var="LogRatio", printTime=FALSE, FUN=median, ...)
参数----------Arguments----------
参数：arrayCGH
an object of type arrayCGH
对象类型arrayCGH
参数：flag.list
a list of objects of type flag
一个类型标志对象名单
参数：var
a variable name (from arrayCGH$arrayValues) from which normalization coefficient has to be computed; default is "LogRatio"
一个变量的名称（从arrayCGH$arrayValues）从标准化系数计算;默认是“对数比”
参数：printTime
boolean value; if TRUE, the time taken by each step of the normalization process is displayed
布尔值;如果TRUE，标准化进程的每一步所需的时间显示
参数：FUN
an aggregation function (e.g. mean, median) to compute a normalization coefficient; default is median
聚合函数（如平均数，中位数）计算归一化系数;默认是中位数
参数：...
further arguments to be passed to FUN
要通过进一步玩转参数
Details
详情----------Details----------
The two flag types are treated differently : - permanent flags detect poor quality spots, which are removed from further analysis - temporary flags detect good quality spots that would bias the normalization coefficient if they were not excluded from its computation, e.g. amplicons or sexual chromosomes. Thus they are not taken into account for the computation of the coefficient, but at the end of the analysis they are normalized as any good quality spots of the array.
这两个标志的类型区别对待： - 永久性标志检测质量低劣景点，从进一步的分析中删除 - 临时标志检测质量好点，偏见的标准化系数，如果他们不排除其计算，如扩增或性染色体。因此，他们不会考虑为系数计算，但在分析结束时，他们作为任何数组的质量好点的规范化。
The normalization coefficient is computed as a function (e.g. mean or median) of the target value (e.g. log-ratios); it is then applied to all good quality spots (including temporary flags), i.e. substracted from each target value.
标准化系数计算作为目标值的功能（如均值或中位数）（如log的比率），然后它适用于所有质量好点（包括临时标志），即从每个目标值加减。
If clone level information is available (i.e. if arrayCGH$cloneValues is not null), a normalized mean target value and basic statistics (such as the number of replicates per clone) are calculated for each clone.
如果克隆级信息是可用的（即如果arrayCGH$cloneValues不为空），归一化平均目标值和基本统计（如每克隆复制的次数）计算每个克隆。
值----------Value----------
an object of type arrayCGH
对象类型arrayCGH
注意----------Note----------
People interested in tools for array-CGH analysis can
阵列比较基因组杂交分析工具有兴趣的人可以
作者（S）----------Author(s)----------
Pierre Neuvial, <a href="mailto:manor@curie.fr">manor@curie.fr</a>.
参考文献----------References----------
A. Aurias, F. Radvanyi, and E. Barillot. Spatial normalization of array-CGH data. BMC Bioinformatics, 7(1):264. May 2006.
参见----------See Also----------
flag
flag
举例----------Examples----------
data(spatial)
data(flags)
### 'edge': local spatial bias[＃“边缘”：局部空间偏见]
## define a list of flags to be applied[＃定义一个用于标志的列表]
flag.list1 <- list(spatial=local.spatial.flag, spot=spot.corr.flag,
ref.snr=ref.snr.flag, dapi.snr=dapi.snr.flag, rep=rep.flag,
unique=unique.flag)
flag.list1$spatial$args <- alist(var="ScaledLogRatio", by.var=NULL,
nk=5, prop=0.25, thr=0.15, beta=1, family="symmetric")
flag.list1$spot$args <- alist(var="SpotFlag")
flag.list1$spot$char <- "O"
flag.list1$spot$label <- "Image analysis"
## normalize arrayCGH[＃规范化arrayCGH服务]
## Not run: edge.norm <- norm(edge, flag.list=flag.list1,[＃无法运行：edge.norm < - 范（边flag.list = flag.list1]
var="LogRatio", FUN=median, na.rm=TRUE)
## End(Not run) [＃结束（不运行）]
print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)[＃现货级标志摘要（flag.summary计算）]
## aggregate arrayCGH without normalization[＃总arrayCGH没有标准化]
edge.nonorm <- norm(edge, flag.list=NULL, var="LogRatio",
FUN=median, na.rm=TRUE)
## sort genomic informations [＃基因组信息进行排序。]
edge.norm <- sort(edge.norm, position.var="PosOrder")
edge.nonorm <- sort(edge.nonorm, position.var="PosOrder")
## plot genomic profiles[＃绘制基因组剖面]
layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
report.plot(edge.nonorm, chrLim="LimitChr", layout=FALSE,
main="Pangenomic representation (before normalization)", zlim=c(-1,1),
ylim=c(-3,1))
report.plot(edge.norm, chrLim="LimitChr", layout=FALSE,
main="Pangenomic representation (after normalization)", zlim=c(-1,1),
ylim=c(-3,1))
### 'gradient': global array Trend[＃“梯度”：全局数组趋势]
## define a list of flags to be applied[＃定义一个用于标志的列表]
flag.list2 <- list(
spot=spot.flag, global.spatial=global.spatial.flag, SNR=SNR.flag,
val.mark=val.mark.flag, position=position.flag, unique=unique.flag,
amplicon=amplicon.flag, replicate=replicate.flag,
chromosome=chromosome.flag)
## normalize arrayCGH[＃规范化arrayCGH服务]
## Not run: gradient.norm <- norm(gradient, flag.list=flag.list2, var="LogRatio", FUN=median, na.rm=TRUE) [＃不运行：gradient.norm < - 规范（梯度，flag.list = flag.list2，VAR =“对数比”，玩转中位数，na.rm = TRUE时）]
## aggregate arrayCGH without normalization[＃总arrayCGH没有标准化]
gradient.nonorm <- norm(gradient, flag.list=NULL, var="LogRatio", FUN=median, na.rm=TRUE)
## sort genomic informations [＃基因组信息进行排序。]
gradient.norm <- sort(gradient.norm)
gradient.nonorm <- sort(gradient.nonorm)
## plot genomic profiles[＃绘制基因组剖面]
layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
report.plot(gradient.nonorm, chrLim="LimitChr", layout=FALSE,
main="Pangenomic representation (before normalization)", zlim=c(-2,2),
ylim=c(-3,2))
report.plot(gradient.norm, chrLim="LimitChr", layout=FALSE,
main="Pangenomic representation (after normalization)", zlim=c(-2,2),
ylim=c(-3,2))
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。
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发表于 2012-2-26 00:13:29
