nudge2(nudge)
nudge2()所属R语言包：nudge
Function for normalizing data, fitting a normal-uniform mixture and estimating probabilities of differential expression in the case where the two samples are being compared indirectly through a common reference sample
功能标准化数据，拟合一个正常的均匀混合物和估计的情况下差异表达的概率，其中两个样本进行比较，通过一个共同的参考样本间接
译者：生物统计家园网 机器人LoveR
描述----------Description----------
After a mean and variance normalization a two component mixture model is fitted to the data. The normal component represents the genes that are not differentially expressed and the uniform component represents the genes that are differentially expressed. Posterior probabilities for differential expression are computed from the fitted model.
安装两个组件的混合模型的均值和方差的标准化后的数据。正常的部分代表是没有差异表达和统一组件代表的差异表达基因的基因。差异表达的后验概率计算的拟合模型。
用法----------Usage----------
nudge2(control.logratio, txt.logratio, control.logintensity, txt.logintensity,
span1 = 0.2, quant = 0.99, z = NULL, tol = 0.00001,iterlim=500)
参数----------Arguments----------
参数：control.logratio
A multiple-column matrix of replicates of log (base 2) ratios of gene expressions for the control versus reference slides.
一个多列的矩阵复制log（基数为2）基因表达与参考幻灯片的控制比例。
参数：txt.logratio
A multiple-column matrix of replicates of log (base 2) ratios of gene expressions for the treatment versus reference slides.
一个多列的矩阵复制log（基数为2）基因表达的治疗与参考幻灯片比例。
参数：control.logintensity
A multiple-column matrix of replicates of log (base 2) total intensities (defined as the product) of gene expressions for the control versus reference slides.
一个多列的矩阵复制log（基数为2）总强度与参考幻灯片控制基因表达（定义为产品）。
参数：txt.logintensity
A multiple-column matrix of replicates of log (base 2) total intensities (defined as the product) of gene expressions for the treatment versus reference slides.
一个多列的矩阵复制log（基数为2）（定义为产品）的基因表达，为治疗与参考幻灯片的总强度。
参数：span1
Proportion of data used to fit the loess regression of the (average-across-replicates) log ratio differences on the (average-across-replicates) log intensities for the mean normalization.
比例的数据，以适应局部加权回归（平均跨-复制）log上的平均标准化（平均对面的复制）log强度比差异。
参数：quant
Quantile to be used from the distribution of standard deviations of log ratio differences across replicates for all genes whose standard deviation was smaller than their absolute (mean normalized) average-across-replicates log ratio difference.
位数分布的标准偏差log的比例差异跨重复基因，其标准偏差小于绝对（平均归一化）平均全面复制log的比例差异。
参数：z
An optional 2-column matrix with each row giving a starting estimate for the probability of the gene (in the corresponding row of the log ratio matrix/vector) not being differentially expressed and a starting estimate for the probability of the gene being differentially expressed. Each row should add up to 1.
可选2列矩阵每行一个基因的概率估计（在相应的行数比矩阵/矢量）没有差异表达基因的概率为估计差异表达。每一行加起来应该为1。
参数：tol
A scalar tolerance for relative convergence of the loglikelihood.
一个标量相对收敛的loglikelihood宽容。
参数：iterlim
The maximum number of iterations the EM is run for.
最大迭代次数的EM运行。
值----------Value----------
A list including the following components
包括以下组件的列表
参数：pdiff
A vector with the estimated posterior probabilities of being in the group of differentially expressed genes.
与中差异表达的基因组后验概率估计的向量。
参数：lRnorm
A vector with the normalized (average-across-replicates) log ratio differences.
与向量归（平均全面复制）log的比例差异。
参数：mu
The estimated mean of the group of genes that are not differentially expressed.
估计是没有差异表达的基因组平均。
参数：sigma
The estimated variance of the group of genes that are not differentially expressed.
估计是没有差异表达的基因组变异。
参数：mixprob
The prior/mixing probability of a gene being in the group of genes that are not differentially expressed.
一个没有差异表达的基因组中基因的前/混合概率。
参数：a
The minimum value of the normalized data.
规范化数据的最低值。
参数：b
The maximum value of the normalized data.
规范化数据的最大价值。
参数：loglike
The log likelihood for the fitted mixture model.
拟合混合模型的log的可能性。
参数：iter
The number of iterations run by the EM algorithm until either convergence or iteration limit was reached.
EM算法的运行，直到达到收敛或迭代限制的迭代的数量。
作者（S）----------Author(s)----------
N. Dean and A. E. Raftery
参考文献----------References----------
参见----------See Also----------
nudge1,norm2c,norm2d,norm1a,norm1b,norm1c,norm1d
nudge1，norm2c，norm2d，norm1a，norm1b，norm1c，norm1d
举例----------Examples----------
apo<-read.csv("http://www.stat.berkeley.edu/users/terry/zarray/Data/ApoA1/rg_a1ko_morph.txt",
header=TRUE)
rownames(apo)<-apo[,1]
apo<-apo[,-1]
apo<-apo+1
lRctl<-log(apo[,c(seq(2,16,2))],2)-log(apo[,c(seq(1,15,2))],2)
lRtxt<-log(apo[,c(seq(18,32,2))],2)-log(apo[,c(seq(17,31,2))],2)
lIctl<-log(apo[,c(seq(2,16,2))],2)+log(apo[,c(seq(1,15,2))],2)
lItxt<-log(apo[,c(seq(18,32,2))],2)+log(apo[,c(seq(17,31,2))],2)
result<-nudge2(lRctl,lRtxt,lIctl,lItxt)
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。
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发表于 2012-2-26 07:57:40
