rma-methods(oligo)
rma-methods()所属R语言包：oligo
RMA - Robust Multichip Average algorithm
RMA - 鲁棒多芯片平均算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Robust Multichip Average preprocessing methodology. This strategy allows background subtraction, quantile normalization and summarization (via median-polish).
鲁棒多芯片平均预处理方法。这一策略使背景减法，位数标准化和总结（中位数，波兰通过）。
用法----------Usage----------
## S4 method for signature 'ExonFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
## S4 method for signature 'ExpressionFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL)
## S4 method for signature 'GeneFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
## S4 method for signature 'SnpCnvFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL)
参数----------Arguments----------
参数：object
Exon/Expression/Gene/SnpCnv-FeatureSet object.
外显子/表达/基因/ SnpCnv的FeatureSet对象。
参数：background
Logical - perform RMA background correction?
逻辑 - 执行RMA背景校正？
参数：normalize
Logical - perform quantile normalization?
逻辑 - 执行位数标准化？
参数：subset
To be implemented.
要实施。
参数：target
Level of summarization (only for Exon/Gene arrays)
汇总级别（仅适用于外显子/基因阵列）
方法----------Methods----------
When applied to an ExonFeatureSet object, rma can produce summaries at different levels: probeset (as defined in the PGF), core genes (as defined in the core.mps file), full genes (as defined in the full.mps file) or extended genes (as defined in the extended.mps file). To determine the level for summarization, use the target argument.
当ExonFeatureSet对象，rma可以产生不同层次的总结：probeset（定义中的PGF），核心基因的（定义在core.mps文件），全基因（定义在full.mps文件）或（定义在extended.mps文件）扩展的基因。要确定的概括水平，使用target参数。
When used on an ExpressionFeatureSet object, rma produces summaries at the probeset level (as defined in the CDF or NDF files, depending on the manufacturer).
当ExpressionFeatureSet对象时，rma产生的probeset水平（定义在CDF或NDF文件，这取决于制造商）的摘要。
When applied to a GeneFeatureSet object, rma can produce summaries at different levels: probeset (as defined in the PGF) and 'core genes' (as defined in the core.mps file). To determine the level for summarization, use the target argument.
GeneFeatureSet对象时，rma可以产生不同层次摘要：probeset（定义中的PGF）和“核心基因”（在core.mps文件定义）。要确定的概括水平，使用target参数。
If used on a SnpCnvFeatureSet object (ie., SNP 5.0 or SNP 6.0 arrays), rma will produce summaries for the CNV probes. Note that this is an experimental feature for internal (and quick) assessment of CNV probes. We recommend the use of the 'crlmm' package, which contains a Copy Number tool specifically designed for these data.
如果使用SnpCnvFeatureSet对象（即SNP的5.0或6.0的SNP阵列），rma会产生的CNV探针的摘要。请注意，这是一个内部和快速评估的CNV探针的实验性功能。我们建议使用“crlmm”包，其中包含一个拷贝数的工具，专门为这些数据设计。
参考文献----------References----------
M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15
Comparison of Normalization Methods for High Density O ligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193
Scherf, U, Speed, TP (2003) Exploration, Normalizati on, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. Vol. 4, Number 2: 249-264
参见----------See Also----------
snprma
snprma
举例----------Examples----------
if (require(maqcExpression4plex) & require(pd.hg18.60mer.expr)){
xysPath <- system.file("extdata", package="maqcExpression4plex")
xysFiles <- list.xysfiles(xysPath, full.name=TRUE)
ngsExpressionFeatureSet <- read.xysfiles(xysFiles)
summarized <- rma(ngsExpressionFeatureSet)
show(summarized)
}
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 08:17:27
