R语言 AgiMicroRna包 rmaMicroRna()函数中文帮助文档(中英文对照)

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rmaMicroRna(AgiMicroRna)
rmaMicroRna()所属R语言包：AgiMicroRna

                                         Getting the Total Gene Signal by RMA algorithm
                                         获取RMA算法共有的基因信号

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

The function creates an uRNAList containing the TotalGeneSignal computed by the RMA algorithm. This signal can be used for the statistical analysis.  
该函数创建一个uRNAList包含由RMA算法TotalGeneSignal计算。此信号可用于统计分析。


用法----------Usage----------


rmaMicroRna(dd, normalize, background)



参数----------Arguments----------

参数：dd
uRNAList, containing the output from readMicroRnaAFE  
uRNAList，包含从readMicroRnaAFE输出


参数：normalize
logical, if TRUE the signal is normalized between arrays using the 'quantile' method
逻辑，如果TRUE信号标准化阵列之间使用的“分量”的方法，


参数：background
logical, if TRUE the signal is background corrected  by fitting a normal + exponential convolution model to a vector of  observed intensities
逻辑，如果TRUE指数正常+卷积模型拟合观测到的强度矢量背景纠正的信号


Details

详情----------Details----------

The function creates an uRNAList output that contains in the uRNAList$TGS, uRNAList$TPS, uRNAList$meanS & uRNAList$procS slots the Total Gene Signal (TGS) computed by the RMA algorithm.  The function uses the robust multiarray average (RMA) method from the 'affy' package.  RMA obtains an estimate of the expression measure for each gene using all the replicated probes for that gene. First, RMA obtains a background corrected intensity by fitting a normal + exponential convolution model to a vector of observed intensities. The normal part represents the background and the exponential part represents the signal intensities. Then the arrays are normalized using 'quantile' normalization. Finally, for each probe set that interrogates the same microRNA, RMA fits a linear model to the background-corrected, normalized and log2 transformed probe intensities. This model produces an estimate of the gene signal taking into account the probe effect. The model parameters estimates are obtained by median polish. The estimates of the gene expression signals are referred as RMA estimates. Normally, each microRNA is interrogated by 16 probes either using 2 different probes, each of them replicated 8 times, or using 4 differnt probes  replicated 4 times.  First, function 'rmaMicroRna' obtains a background corrected signal using the 'rma.background.correct' function of the package 'preprocessCore' , then the signal is normalized bewtween arrays using the 'limma' function 'normalizeBetweenArrays' with the 'quantile' method.  Then, the median of the replicated probes is obtained, leading to either 2 or 4 different measures for each gene. These measures correspond to different probes for the same genes that are summarized into a single RMA linear model described above.
函数创建uRNAList的的输出包含的uRNAList美元TGS的，uRNAList元租置计划，uRNAList $手段和uRNAList插槽特效美元的共有基因信号（TGS）由RMA算法计算。该函数使用从“affy包强大的MultiArray平均（RMA）的方法。 RMA获得估计为每个使用所有的探针，基因复制基因的表达措施。首先，RMA获得了背景校正强度指数正常+卷积模型拟合观测到的强度矢量。正常的部分代表的背景和指数部分代表信号强度。然后使用“分量”标准化标准化阵列。最后，每个探针一套，询问相同的microRNA，RMA符合线性模型，背景校正，规范化和log2转化探针强度。这种模式产生的基因信号，考虑到探针的影响的估计。得到的模型参数估计中位数波兰。被称为RMA估计基因表达信号的估计。通常情况下，每个小分子RNA被审问16使用2个不同的探针，他们每个重复8次，或使用复制4次不同的充探针探针。首先，函数rmaMicroRna“获得了背景校正信号使用的”rma.background.correct“包preprocessCore功能，信号标准化bewtween的阵列使用的”分量“的方法limma”功能“normalizeBetweenArrays” 。然后，复制探针的中位数，导致2个或4每一个基因的不同措施。这些措施对应不同的探针总结上面描述成一个单一的RMA的线性模型相同的基因。


值----------Value----------

uRNAList containing the Total Gene Signal computed by the RMA algorithm in log 2 scale.  
uRNAList包含在log2规模的RMA算法计算的总基因信号。


作者（S）----------Author(s)----------


Pedro Lopez-Romero



参考文献----------References----------

Scherf,U., Speed,T. (2003) Exploration, normalization, and summaries  of high density oligonucleotide array probe level data. Biostatistics.  4, 249-264
of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 3, 307-315.
R package version 1.4.0
and Computational Biology Solutions using R and Bioconductor'. R. Gentleman,  V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397 - 420

举例----------Examples----------


data(dd.micro)
ddTGS.rma=rmaMicroRna(dd.micro, normalize=TRUE, background=TRUE)
dim(ddTGS.rma)
RleMicroRna(ddTGS.rma$TGS,"RLE TGS.rma","blue")

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


注：
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