R语言 SNPMaP包 cel2ras()函数中文帮助文档(中英文对照)

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cel2ras(SNPMaP)
cel2ras()所属R语言包：SNPMaP

                                        SNPMaP workflows
                                         SNPMaP的工作流程

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Workflow functions to process SNPMaP objects. Called by snpmap().
处理SNPMaP对象的工作流功能。调用方式snpmap()。


用法----------Usage----------


cel2raw(x, cels = x@chps, lowMemory = x@lowMemory, ...)
cel2long(x, cels = x@chps, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, ...)
cel2short(x, cels = x@chps, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, ...)
cel2ras(x, cels = x@chps, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, subtractMismatch = x@useMM, ...)
cel2rasS(x, cels = x@chps, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, subtractMismatch = x@useMM, FUN = x@summary, ...)
raw2long(x, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, tidy = FALSE, ...)
raw2short(x, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, tidy = FALSE, ...)
raw2ras(x, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, subtractMismatch = x@useMM, tidy = FALSE, ...)
raw2rasS(x, lowMemory = x@lowMemory, set = x@set, normalize = x@normalize,
        log.intensities = x@logInt, subtractMismatch = x@useMM, FUN = x@summary,
        tidy = FALSE, ...)
long2short(x, lowMemory = x@lowMemory, tidy = FALSE, ...)
long2ras(x, lowMemory = x@lowMemory, subtractMismatch = x@useMM, tidy = FALSE, ...)
long2rasS(x, lowMemory = x@lowMemory, subtractMismatch = x@useMM, FUN = x@summary,
                tidy = FALSE, ...)
short2ras(x, lowMemory = x@lowMemory, subtractMismatch = x@useMM, tidy = FALSE, ...)
short2rasS(x, lowMemory = x@lowMemory, subtractMismatch = x@useMM, FUN = x@summary,
                tidy = FALSE, ...)
ras2rasS(x, lowMemory = x@lowMemory, FUN = x@summary, tidy = FALSE, ...)



参数----------Arguments----------

参数：x
An object of class SNPMaP.
对象的类SNPMaP。


参数：cels
character; a vector of the CEL files to be included.
字符的矢量以被包含的CEL文件。


参数：lowMemory
logical; should the SNP data be stored on disk rather than in memory?
逻辑，SNP数据存储在磁盘上，而不是存储在内存吗？


参数：tidy
logical; if the data is stored on disk, should the old FileDoubleMatrix be unlinked?
逻辑，如果数据存储在磁盘上，老“FileDoubleMatrix是unlinked？


参数：set
numeric; the set of probesets to include. See SNPMaP.cdm-package.
数字; probesets要包括的组。见SNPMaP.cdm-package。


参数：normalize
logical; quantile normalize probe intensities across arrays.
逻辑位数标准化探测器阵列之间的强度。


参数：log.intensities
logical; take natural log of probe intensities.
逻辑探针强度的自然对数。


参数：subtractMismatch
logical; subtract mismatch probe intensities if available from perfect match intensities.
逻辑，如果有完美的比赛强度，减去错配探针强度。


参数：FUN
function; function to compute summary statistic for RAS on each chip. See SNPMaP-class.
功能;功能计算汇总统计RAS的，每个芯片上。见SNPMaP-class。


参数：...
additional arguments passed to other workflow and internal functions.
额外的参数传递给其他的工作流程和内部功能。


Details

详细信息----------Details----------

The workflow functions work by calling cel2raw(), raw2long(), long2short(), short2ras(), ras2rasS().
工作流功能的工作，调用cel2raw()，raw2long()，long2short()，short2ras()，ras2rasS()。


值----------Value----------

Object of class SNPMaP.
对象类SNPMaP。


参见----------See Also----------

SNPMaP-package, SNPMaP.cdm-package, SNPMaP-class, snpmap(), norm(), logIntensities()
SNPMaP-package，SNPMaP.cdm-package，SNPMaP-class，snpmap()，norm()，logIntensities()


实例----------Examples----------


## Not run: [＃不运行：]
## Getting started[＃入门]
## Creates the 'raw' SNPMaP object x on disk with mismatch probes included[＃创建“原始”SNPMaP对象x磁盘上的错配探针]
x<-snpmap(useMM=TRUE, RUN='cel2raw', lowMemory=TRUE)
## Print a summary of the SNPMaP object[打印摘要的SNPMaP对象的]
summary(x)
## Add a comment (prints in the summary)[＃添加评论（打印摘要）]
comment(x)<-'High and low extreme pools from January'
## View pseudo image to screen for artefacts[＃查看屏幕文物的假象]
image(x)
## Plot probe intensities[＃图探针强度]
plot(x, FUN=log)
boxplot(x, FUN=log)
## tidy=TRUE removes the FileDoubleMatrix from the old x to keep the disk tidy[＃整齐= TRUE消除了从旧的X FileDoubleMatrix保持磁盘整洁]
x<-raw2ras(x, tidy=TRUE)
## Plot Relative Allele Scores[＃图相对等位基因成绩的]
plot(x)
## Default tidy=FALSE does not remove the original FileDoubleMatrix from disk[＃默认整齐= FALSE不从磁盘上删除原来的FileDoubleMatrix]
## Useful if you want to keep x (no side effects)[＃有用的，如果你想保持X（无副作用）]
y<-ras2rasS(x)
## View the first ten rows[＃查看前十行]
as.matrix(y[1:10,])
## View a set of SNPs[＃查看一组的单核苷酸多态性]
as.matrix(y[c("SNP_A-4192909", "SNP_A-4192918"),])
## Transfer the SNPMaP object from disk to memory[从磁盘到内存】＃转移的SNPMaP的的对象]
y<-disk2memory(y, tidy=TRUE)
## Run the analysis again from CEL files to RAS summaries without viewing intermediate stages[＃再次运行分析CEL文件的的RAS摘要不观看的中间阶段]
## This time in memory (may require a lot of RAM)[＃这个时候在内存中（可能需要大量的RAM）]
z<-snpmap(useMM=TRUE, RUN='cel2rasS', lowMemory=FALSE)
plot(z)
## Get the RAS summary scores as a standard matrix[＃取得RAS总结成绩作为标准矩阵]
rasSummaries<-as.matrix(z)
## Read all the sets into a list[＃读取到一个列表中的所有套]
allSets<-msnpmap(set=0)

## End(Not run)[＃（不执行）]

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


注：
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