genediff(LMGene)
genediff()所属R语言包：LMGene
Gene-by-gene and posterior p-value calculation function.
基因和基因后的P-值计算功能。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes two sets of p-values per gene or probe via gene-by-gene ANOVA, using both the gene-specific MSE and the posterior MSE for each term in the ANOVA. P-values are not adjusted for multiple testing. <br> Assumes a fixed effects model and that the correct denominator for all comparisons is the MSE.
通过基因通过基因变异数计算两套每个基因或探针P-值，同时使用特定基因的MSE和方差在每个任期后的MSE。 P值调整为多个测试。参考假定一个固定效应模型和所有比较正确的分母是微型和小型企业。
用法----------Usage----------
genediff(eS, model = NULL, method = c("MLE", "MOM", "MOMlog"),
verbose = TRUE)
参数----------Arguments----------
参数：eS
An ExpressionSet object. Any transformation and normalization of exprs(eS) should be conducted prior to use in genediff.
ExpressionSet对象。任何改造和标准化exprs(eS)使用前应进行genediff。
参数：model
Model used for comparison; see details and LMGene.
模型用于比较;看到的细节和LMGene。
参数：method
Method by which posterior p-values are calculated. Default "MLE".
方法后的P-值计算。默认"MLE"。
参数：verbose
If TRUE, the prior degrees of freedom and mean reciprocal precision are printed. See details.
如果TRUE，自由，意味着相互精密的前度印。查看详情。
Details
详情----------Details----------
The argument eS must be an ExpressionSet object from the Biobase package. If you have data in a matrix and information about experimental design factors, then you can use neweS to convert the data into an ExpressionSet object. Please see neweS for more detail.
参数eS必须从BIOBASE包ExpressionSet对象。如果您拥有数据matrix和有关实验设计因素的信息，那么你可以使用neweSExpressionSet对象转换成的数据。请参阅neweS更多细节。
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 method argument specifies how the adjusted MSE and degrees of freedom should be calculated for use in computation of the posterior p-values:
method参数指定如何调整MSE和自由度，应在使用后的P-值计算计算：
"MLE" Default. Calculate adjusted MSE and degrees of freedom by maximum likelihood estimation, as described in Wright and Simon (2003).
“MLE的”默认。计算调整后的MSE和自由度最大似然估计，赖特和西蒙（2003）所述。
"MOM" Calculate adjusted MSE and degrees of freedom by method of moments, as described in Rocke (2003).
“妈妈”矩量法计算调整后的MSE和自由度，在Rocke（2003）所述。
"MOMlog" Calculate adjusted MSE and degrees of freedom by method of moments on log scale, as described in Smyth (2004). Uses functions fitFdist and trigammainverse from the package limma. Note that the method of Smyth (2004) is
“MOMlog”调整后的MSE和自由度计算方法对数刻度的时刻，史密斯（2004年）。使用功能fitFdist和trigammainverse包limma。需要注意的是史密斯的方法（2004）
All three methods assume that the gene-specific MSE's follow a gamma distribution with mean tau. (NB: Notation and parameterization vary somewhat between each of the source papers.) The mean of the gamma distribution, tau, is modeled with an inverse gamma prior with hyperparameters alpha and beta. Empirical Bayes methods are used to estimate the prior hyperparameters, either by maximum likelihood, method of moments, or method of moments on the log scale. The "posterior MSE" is the posterior mean of the variances given the observed gene-specific MSE's.
所有三种方法的假设，特定基因的MSE的伽玛分布与平均头。 （注：符号和参数。每个源文件之间有所不同）伽玛分布，头，平均为蓝本前具有逆伽玛hyperparametersα和β。经验Bayes方法用于估计前hyperparameters，通过最大似然法，矩量法，或对数刻度的时刻方法。 “后的MSE”是观测到的特定基因的MSE的差异后的平均值。
If verbose = TRUE, the function prints the estimated prior degrees of freedom, which equals twice the prior shape parameter alpha, and the estimated prior mean reciprocal precision, or 1/(alpha*beta).
如果verbose = TRUE，功能打印估计前度的自由，这等于两次事先形状参数α，估计事先意味着相互精度，或1 /（αβ）。
All p-values are calculated from fixed-effects ANOVA F statistics, using either the gene-specific MSE or the posterior MSE as the denominator.
所有的p值计算从固定效应的方差分析的F统计量，使用特定基因的MSE或作为分母后的MSE。
值----------Value----------
A list with components:
与组件列表：
参数：Gene.Specific
A matrix of p-values calculated using the gene-specific MSE, with one row for each gene/probe and one column for each factor
一个P-值计算使用的MSE特定基因，每个基因/探针一行和一列的矩阵各因素
参数：Posterior
A matrix of p-values calculated using the posterior MSE, with one row for each gene/probe and one column for each factor
一个使用后的MSE，每个基因/探针一行和一列计算p值各因素的矩阵
作者（S）----------Author(s)----------
David Rocke, Geun-Cheol Lee, and Blythe Durbin-Johnson
参考文献----------References----------
Statistical Applications in Genetics and Molecular Biology 3, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3/
Bioinformatics 19, 2448–2455.
参见----------See Also----------
LMGene, rowaov, neweS
LMGene，rowaov，neweS
举例----------Examples----------
library(Biobase)
library(LMGene)
#data[数据]
data(sample.mat)
data(vlist)
raw.eS <- neweS(sample.mat, vlist)
# glog transform data[glog变换数据]
trans.eS <- transeS(raw.eS, lambda = 727, alpha = 56)
# calculate p-values[计算p-值]
pvlist <- genediff(trans.eS)
pvlist$Posterior[1:5,]
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。
|
发表于 2012-2-25 23:30:54
