R语言 SNPMaP包 SNPMaP-package()函数中文帮助文档(中英文对照)

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SNPMaP-package(SNPMaP)
SNPMaP-package()所属R语言包：SNPMaP

                                         SNP Microarrays and Pooling in R
                                         SNP芯片技术和Pooling在R

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Pooling DNA on SNP microarrays is a cost-effective way to carry out genome-wide association studies for heritable disorders or traits. The SNPMaP package provides formal SNPMaP objects and methods in R as a base for these analyses using Affymetrix genotyping arrays.
池DNA的SNP芯片技术是具有成本效益的方式，为遗传性疾病或性状进行全基因组关联研究。 SNPMaP包提供正规SNPMaP的对象和方法在R利用Affymetrix基因分型阵列进行这些分析为基础。


Details

详细信息----------Details----------

</table>  A SNPMaP study involves selecting individuals based on their disease status (case or control) or their score on a quantitative trait (high or low extremes). DNA from these individuals is pooled in biological replicate pools (20 pools of 50 different individuals each, for example). Each pool is then genotyped according to the standard protocols on genotyping microarrays. The probe intensities from these arrays can be extracted from the CEL files into SNPMaP objects using the snpmap() function and Relative Allele Scores (RAS) can be calculated as estimates of allele frequency in each pool. By comparing the allele frequencies in high and low or case and control pools, hundreds of thousands of SNPs across the genome can be screened for association with a trait or disorder. The SNPMaP package also provides methods for visualising the data at each stage of the analysis. Using the lowMemory option allows this to be done on a standard desktop computer (albeit slower than if all data is kept in memory). This package is an evolution of the scripts referred to in Meaburn et al (2006) and is described in Davis, Plomin and Schalkwyk (2009); please cite this paper if you find the package useful. Additional supporting material is available at http://sgdp.iop.kcl.ac.uk/snpmap/.
</表>一个SNPMaP研究涉及选择个人根据自己的疾病状态（情况或控制）的数量性状（高或低极端）或他们的得分。从这些人的DNA汇集在的生物复制游泳池（20池50个不同的个体，例如）。然后，每个池的基因根据基因分型芯片的标准协议。从这些阵列可以提炼出的探针强度的CEL文件到SNPMaP使用snpmap()函数和的相对等位基因分数（RAS）的对象，可以计算出估计的等位基因频率在每个池中。“数十万在整个基因组的单核苷酸多态性的等位基因频率比较高，低及病例组和对照池，可以筛选与性状的关联或障碍。 SNPMaP包还提供了用于可视化的数据，在每个阶段的分析的方法。 lowMemory使用选项，可以做一个标准的桌上型电脑（虽然速度比，如果所有的数据都保存在内存中）。这个包是一种进化中提到Meaburn等人（2006年）和戴维斯，普罗明和范斯卡尔奎克（2009年）中描述的脚本，请引用这个文件，如果你找到有用的套件。额外的辅助材料是提供http://sgdp.iop.kcl.ac.uk/snpmap/。


数组类型----------Array types----------

Details of the arrays supported by the current version of SNPMaP can be found in the SNPMaP.cdm package.
详情SNPMaPSNPMaP.cdm包中可以找到的当前版本支持的阵列。


（作者）----------Author(s)----------



Oliver SP Davis and Leo C Schalkwyk


Maintainer: SNPMaP team <a href="mailto:snpmap@iop.kcl.ac.uk">snpmap@iop.kcl.ac.uk</a>




参考文献----------References----------

Davis, OSP, Plomin, R, and Schalkwyk, LC. (2009) The SNPMaP package for R: A framework for genome-wide association using DNA pooling on microarrays. Bioinformatics, 25, 281-283. http://dx.doi.org/10.1093/bioinformatics/btn587<br> <br> Meaburn E, Butcher LM, Schalkwyk LC, and Plomin R. (2006) Genotyping pooled DNA using 100K SNP microarrays: a step towards genomewide association scans. Nucleic Acids Research, 34(4):e28. http://dx.doi.org/10.1093/nar/gnj027

参见----------See Also----------

snpmap() to set up a SNPMaP analysis.<br> SNPMaP-class to represent a SNPMaP study.<br> SNPMaP.cdm-package for the cdm matrices that interpret the 'raw' format SNPMaP objects.<br> affxparser-package that reads the CEL files.<br> R.huge-package that provides FileDoubleMatrices for the lowMemory option.<br> methods-package for S4 formal classes.
snpmap()设置了一个SNPMaP的分析。<BR>SNPMaP-class代表SNPMaP的研究。<BR>的SNPMaP.cdm-packagecdm矩阵的解释“原始”格式 SNPMaP对象。<BR> affxparser-package读取CEL文件。<BR> R.huge-package提供了FileDoubleMatrices的lowMemory选项。<BR><X >S4正规类。


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）]

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