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

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tranestAffyProbeLevel(LMGene)
tranestAffyProbeLevel()所属R语言包：LMGene

                                         Glog transformation parameter estimation function for probe-level Affymetrix expression data
                                         Affymetrix的表达探针级数据glog变换参数估计函数

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Estimates parameters for the glog transformation on probe-level Affymetrix expression data, by maximum likelihood  or by minimizing the stability score.
Affymetrix的表达探针级的数据，最大的可能性或减少的稳定得分，glog改造估计参数。


用法----------Usage----------


tranestAffyProbeLevel(eS, ngenes = 5000, starting = FALSE, lambda = 1000,
alpha = 0, gradtol = 0.001,lowessnorm = FALSE, method = 1, mult = FALSE,
model = NULL, SD = FALSE, rank = TRUE, model.based = TRUE,
rep.arrays = NULL)



参数----------Arguments----------

参数：eS
An AffyBatch object
AffyBatch对象


参数：ngenes
Number of randomly sampled probesets to be used in estimating the transformation parameter
转换参数估计的随机抽样probesets数


参数：starting
If TRUE, user-specified starting values for lambda and alpha are input to  the optimization routine
如果TRUE，用户指定lambda和alpha输入的优化程序的开始值


参数：lambda
Starting value for parameter lambda. Ignored unless starting = TRUE
启动参数lambda的价值。除非starting = TRUE忽略


参数：alpha
Starting value for parameter alpha. Ignored unless starting = TRUE
启动参数alpha的价值。除非starting = TRUE忽略


参数：gradtol
A positive scalar giving the tolerance at which the scaled  gradient is considered close enough to zero to terminate the algorithm  
一个正标量，规模梯度被认为是足够接近零误差，以终止该算法


参数：lowessnorm
If TRUE, lowess normalization (using lnorm) is used in calculating  the likelihood.
如果TRUE，LOWESS标准化（使用lnorm）用于计算的可能性。


参数：method
Determines optimization method. Default is 1,  which corresponds to a Newton-type method (see nlm and details.)
确定的优化方法。默认值是1，这相当于牛顿型方法（见nlm和细节。）


参数：mult
If TRUE, tranest will use a vector alpha with one (possibly different) entry per sample.  Default is to use same alpha for every sample.  SD and mult may not both be TRUE.
如果TRUE，tranest将使用一个（可能是不同的）每个样品进入一个向量阿尔法。默认是使用相同的字母，每个样品。 SD和mult不得同时TRUE。


参数：model
Specifies model to be used. Default is to use all variables from eS without interactions. See details.
要使用指定的模型。默认是使用所有变量从ES无相互作用。查看详情。


参数：SD
If TRUE, transformation parameters are estimated by minimizing the stability score.  See details.
如果TRUE，变换参数估计减少的稳定得分。查看详情。


参数：rank
If TRUE, the stability score is calculated by regressing the replicate standard deviation on the rank of the probe/row means (rather than on the means themselves).  Ignored unless SD = TRUE
TRUE如果，稳定的得分计算探针/行方式（而不是对自己的手段）上的排名倒退的复制标准偏差。除非SD = TRUE忽略


参数：model.based
If TRUE, the stability score is calculated using the standard deviation of residuals from the linear model in model.  Ignored unless SD = TRUE
如果TRUE，稳定的得分是使用从model的线性模型的残差标准差计算。除非SD = TRUE忽略


参数：rep.arrays
List of sets of replicate arrays.  Each element of rep.arrays should be a vector with entries corresponding to arrays (columns) in exprs(eS) conducted under the same experimental conditions, i.e., with identical rows in pData(eS). Ignored unless SD = TRUE and model.based = FALSE
复制阵列套的名单。 rep.arrays的每个元素应该与相应的阵列（列）项向量exprs(eS)进行相同的实验条件下，与相同pData(eS)行即，下。忽略除非SD = TRUE和model.based = FALSE


Details

详情----------Details----------

The model argument is an optional character string, constructed like the right-hand side of a formula for lm. It specifies which of the variables in the ExpressionSet will be used in the model and whether interaction terms will be included. If model=NULL, it uses all variables from the ExpressionSet without interactions. Be careful of using interaction terms with factors; this often leads to overfitting, which will yield an error.
model参数是一个可选的字符串，像一个lm公式右边的构建。它指定了ExpressionSet变量将用于在模型是否将包括互动方面。如果model=NULL，它使用ExpressionSet无互动的所有变量。小心使用与因素之间的相互作用方面，这往往导致过度拟合，这将产生一个错误。

The default estimation method is maximum likelihood.  The likelihood is derived by assuming that there exist values for lambda and alpha such that the residuals from the linear model in model, fit to glog-transformed data using those values  for lambda and alpha, follow a normal distribution.  See Durbin and Rocke (2003) for details.
默认的估计方法是最大的可能性。假设存在lambda和alpha这样，从线性模型中的残差model，适合glog转化数据，使用这些值<X值推导出的可能性>和lambda，服从正态分布。见的细节德宾和Rocke的（2003）。

If SD = TRUE, lambda and alpha are estimated by minimizing the stability score rather than by maximum likelihood. The stability score is defined as the absolute value of the slope coefficient from the regression of the replicate/residual  standard deviation on the probe/row means, or on the rank of the probe/row means.  If model.based = TRUE, the stability score is calculated using the standard deviation of residuals from the linear model in model.  Otherwise, the stability score is  calculated using the pooled standard deviation over sets of replicates in rep.arrays. See Wu and Rocke (2009) for details.
如果SD = TRUE，lambda和alpha的稳定得分，而不是通过减少最大似然估计。从回归探针/行复制/剩余标准差，或探针/行的排名斜率系数的绝对值定义为稳定得分。如果model.based = TRUE，稳定的得分是使用从model的线性模型的残差标准差计算。否则，稳定的得分计算使用多套重复rep.arrays汇集的标准偏差。详情参见吴Rocke（2009）。

A random sample of probsets (of size ngene) is sampled from featureNames(eS).  Expression data from all probes in the sampled  probesets is used in estimating the transformation parameters.
随机抽样的probsets（大小ngene）featureNames(eS)采样。表达，由在采样probesets的所有探针的数据用于估计转换参数。

Optimization methods in method are as follows:
优化方法在method如下：




1 =  Newton-type method, using nlm
1 =牛顿型方法，使用nlm




2 =  Nelder-Mead, using optim
2 =内尔德米德使用optim




3 =  BFGS, using optim
3 =的BFGS，使用optim




4 =  Conjugate gradients, using optim
4 =共轭梯度，使用optim




5 =  Simulated annealing, using optim (may only be used when mult = TRUE)
5 =模拟退火，使用optim（仅可用于当mult = TRUE）


值----------Value----------

A list with components:
与组件列表：


参数：lambda
Estimate of transformation parameter lambda
转换参数的lambda的估计


参数：alpha
Estimate of transformation parameter alpha
估计参数α的转变


作者（S）----------Author(s)----------



Lei Zhou, David Rocke, Geun-Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu




参考文献----------References----------

Bioinformatics, 19, 1360&ndash;1367.

Bioinformatics, 21, 3983&ndash;3989.


参见----------See Also----------

tranest, lnorm, psmeans, glog
tranest，lnorm，psmeans，glog


举例----------Examples----------


library(LMGene)
library(affy)
library(Biobase)
library(affydata)

data(Dilution)

tranpar.Dilution <- tranestAffyProbeLevel(Dilution, model = "liver",
ngenes = 3000, method = 2)

# transform data[转换数据]
trans.Dilution <- transeS(Dilution, tranpar.Dilution$lambda,
                tranpar.Dilution$alpha)

# extract transformed perfect matches[提取转化的完美比赛]
exprs(trans.Dilution) <- pm(trans.Dilution)

# lowess normalize transformed data[LOWESS标准化转换后的数据]
lnorm.Dilution <- lnormeS(trans.Dilution)
## Not run: [＃无法运行：]
# Average over probesets[平均超过probesets]
# First, create index of probes[首先，创建探针指数]
fnames <- featureNames(Dilution)
p <- length(featureNames(Dilution))
ind <- vector()
for (i in 1:p){
        nprobes <- dim(pm(Dilution,fnames[i]))[1]
        ind <- c(ind, rep(i,nprobes))   
}

avg.Dilution <- psmeans(lnorm.Dilution, ind)

## End(Not run)[＃结束（不运行）]

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


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