rpa(RPA)
rpa()所属R语言包：RPA
RPA for preprocessing.
爱国的预处理。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns an expressionSet object preprocessed with RPA. If 'cind' is not specified, uses the first array of affybatch as the reference. With rpa.online one can run preprocessing in online-mode
返回爱国预处理expressionSet对象。如果未指定“cind作为参考，使用第一affybatch阵列。与rpa.online可以在联机模式运行的预处理
用法----------Usage----------
rpa(abatch = NULL, sets = NULL, priors = NULL, epsilon = 1e-2, cind = 1, sigma2.method = "robust", d.method = "fast", verbose = FALSE, bg.method = "rma", normalization.method = "quantiles.robust", cdf = NULL, cel.files = NULL, cel.path = NULL)
rpa.online(cel.path = NULL, cel.files = NULL, sets = NULL, priors=
list(alpha = 2, beta = 1), epsilon = 0.01, cind = 1, verbose = FALSE, bg.method = "rma", normalization.method = "quantiles", cdf = NULL, batch.size = 10, quantile.basis = NULL)
参数----------Arguments----------
参数：abatch
An AffyBatch object.
一个AffyBatch对象。
参数：sets
Probesets for which RPA will be computed. Default: all probe sets.
其中爱国将计算probesets的。默认：所有探针集。
参数：priors
An 'rpa.priors' object. Can be used to set user-specified priors for the model parameters. Not used sigma2.method = "var". The prior parameters alpha and beta are prior parameters for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with sigma2.method 'var'. Scalar alpha and beta specify an identical inverse Gamma prior for all probes, which regularizes the solution. Can be also specified as lists, each element corresponding to one probeset.
一个rpa.priors“对象。可以用来为用户指定的先验模型参数。不使用sigma2.method =“VAR”。前参数α和β是前探针的具体差额逆Gamma分布参数。无信息之前得到α，β - > 0。不使用sigma2.methodVAR。标α和β指定相同的逆Gamma之前，所有的探针，而规范的解决方案。也可以指定为列表，每个元素对应一个probeset。
参数：epsilon
Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
收敛公差。被视为融合时，所有参数的变化是<小量的迭代。
参数：cind
Specify reference array for computing probe-level differential expression. Default: cind = 1. Note that if exclude.reference.array = TRUE the expression value for the reference array (cind) will be excluded in the output. Note that all values of the reference array are 0 since they indicate the differential expression of the reference array against itself.
指定参考阵列计算探针水平的差异表达。默认：cind = 1。请注意参考阵列（cind）如果exclude.reference.array = TRUE，表达式的值将被排除在输出。请注意参考阵列中的所有值都为0，因为它们表明对自身的引用数组中的差异表达。
参数：sigma2.method
Optimization method for sigma2 (probe-specific variances). This parameter is denoted by tau^2 in the vignette and manuscript. "robust": (default) update sigma2 by posterior mean, regularized by informative priors that are identical for all probes (user-specified by setting scalar values for alpha, beta). This regularizes the solution, and avoids overfitting where a single probe obtains infinite reliability. This is a potential problem in the other sigma2 update methods with non-informative variance priors. The default values alpha = 2; beta = 1 are used if alpha and beta are not specified. "mode": update sigma2 with posterior mean "mean": update sigma2 with posterior mean "var": update sigma2 with variance around d. Applies the fact that sigma2 cost function converges to variance with large sample sizes.
优化方法sigma2（探针的具体差异）。此参数表示头^ 2中的小插曲和手稿。 “稳健”：（默认）更新后平均sigma2，正规化的先验信息是相同的所有探针（α，β的标值设置为用户指定）。本规范的解决方案，避免过拟合一个探针获得无限的可靠性。这是一个潜在的问题在其他sigma2更新方法与非信息方差先验。默认值，α= 2，β= 1，如果未指定alpha和beta。 “模式”：sigma2更新后平均“的意思”：更新后平均“VAR”sigma2：更新sigma2与周围ð方差。适用的事实，sigma2成本函数收敛大样本的方差。
参数：d.method
Method to optimize d. "fast": (default) weighted mean over the probes, weighted by probe variances The solution converges to this with large sample size. "basic": optimization scheme to find a mode used in Lahti et al. TCBB/IEEE; relatively slow; this is the preferred method with small sample sizes.
方法优化ð。 “快”：（默认）以上的探针，探针解决方案收敛到这个大样本方差加权加权平均。 “基本”：优化方案，以找到在拉赫蒂等使用模式。 TCBB / IEEE相对缓慢，这是小样本的首选方法。
参数：verbose
Print progress information during computation.
在计算打印进度信息。
参数：bg.method
Specify background correction method. Default: "rma". See bgcorrect.methods() for other options.
指定背景校正方法。默认：“RMA”。请参阅其他选项bgcorrect.methods的（）。
参数：normalization.method
Specify quantile normalization method. Default: "pmonly". See normalize.methods(Dilution) for other options.
指定分量的归一化法。默认“pmonly”。看到其他选项normalize.methods（稀释）。
参数：cdf
Specify an alternative CDF environment. Default: none.
指定替代的民防部队环境。默认：无。
参数：cel.files
List of CEL files to preprocess.
名单CEL文件进行预处理。
参数：cel.path
Path to CEL file directory.
CEL的文件目录的路径。
参数：batch.size
Batch size for online mode (rpa.online); the complete list of CEL files will be preprocessed in batches with this size using Bayesian online-updates for probe-specific parameters.
一批在线模式（rpa.online）的大小; CEL文件的完整列表，将分批进行预处理，使用贝叶斯在线更新的大小为探针的具体参数。
参数：quantile.basis
Pre-calculated quantile vector for quantile normalization.
预先计算的位数位数标准化的向量。
Details
详情----------Details----------
RPA preprocessing function. Gives an estimate of the probeset-level mean parameter d of the RPA model, and returns these in an expressionSet object. In online-mode (rpa.online), the CEL files are handled in batches to obtain Bayesian updates for probe-specific hyperpriors; after scanning through the database the results are combined. The online mode is useful for preprocessing very large expression data sets where ordinary preprocessing
爱国预处理功能。给出的probeset级RPA的模型平均参数d的估计，并返回这些在expressionSet对象。在在线模式（rpa.online），CEL文件分批处理，以获取探针具体hyperpriors的贝叶斯更新;通过扫描数据库后的结果相结合。联机模式是非常有用的预处理非常大的基因表达数据集普通预处理
值----------Value----------
An instance of the 'expressionSet' class.
“expressionSet”类的一个实例。
注意----------Note----------
rpa.online is still an experimental version.
rpa.online仍然是一个实验性的版本。
注意----------Note----------
sigma2.method = "robust" and d.method = "fast" are recommended. With small sample size and informative prior, d.method =
sigma2.method =“稳健”和d.method =“快”的建议。小样本的大小和内容之前，d.method =
作者（S）----------Author(s)----------
Leo Lahti <a href="mailto:leo.lahti@iki.fi">leo.lahti@iki.fi</a>
参考文献----------References----------
<h3>See Also</h3> RPA.pointestimate, set.priors, AffyBatch, ExpressionSet,
举例----------Examples----------
# Not run:[不运行：]
## Load example data set[＃加载示例数据集]
#require(affydata)[要求（affydata）]
#data(Dilution)[数据（稀释）]
## Compute RPA for specific probesets[＃计算特定probesets爱国]
#sets <- geneNames(Dilution)[1:2] [套< - geneNames（稀释）[1:2]]
#set <- "33572_at"[集< - “33572_at”]
#eset <- rpa(Dilution, sets)[ESET < - RPA（稀释，套）]
## Compute RPA for whole data set [＃计算整个数据集的爱国]
## ... slow, not executed here [＃...速度慢，不执行]
## eset <- rpa(Dilution)[＃ESET < - RPA（稀释）]
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 13:21:46
