ebayes(limma)
ebayes()所属R语言包：limma
Empirical Bayes Statistics for Differential Expression
差异表达的经验Bayes统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given a series of related parameter estimates and standard errors, compute moderated t-statistics, moderated F-statistic, and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value.
鉴于一系列相关参数的估计和标准错误，计算放缓t-统计量，F统计量放缓，并记录赔率朝着一个共同的价值标准错误的经验Bayes收缩差异表达。
用法----------Usage----------
ebayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4), trend=FALSE)
eBayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4), trend=FALSE)
treat(fit, lfc=0, trend=FALSE)
参数----------Arguments----------
参数：fit
an MArrayLM fitted model object produced by lmFit or contrasts.fit, or an unclassed list produced by lm.series, gls.series or mrlm containing components coefficients, stdev.unscaled, sigma and df.residual
MArrayLM模型拟合对象lmFit或contrasts.fit，或lm.series，gls.series或mrlm包含组件<unclassed名单产生X>，coefficients，stdev.unscaled和sigma
参数：proportion
numeric value between 0 and 1, assumed proportion of genes which are differentially expressed
0和1之间的数值，假定这些差异表达基因的比例
参数：stdev.coef.lim
numeric vector of length 2, assumed lower and upper limits for the standard deviation of log2-fold-changes for differentially expressed genes
向量长度为2的数字，假设的差异表达基因的“log2倍变化的标准差的上限和下限
参数：trend
logical, should an intensity-trend be allowed for the prior variance. Default is that the prior variance is constant.
逻辑，应事先方差强度趋势被允许。默认是前方差是不变的。
参数：lfc
the minimum log2-fold-change which is considered material
最低的log2倍 - 这被认为是物质的变化
Details
详情----------Details----------
These functions is used to rank genes in order of evidence for differential expression. They use an empirical Bayes method to shrink the probe-wise sample variances towards a common value and to augmenting the degrees of freedom for the individual variances (Smyth, 2004). The functions accept as input argument fit a fitted model object from the functions lmFit, lm.series, mrlm or gls.series. The fitted model object may have been processed by contrasts.fit before being passed to eBayes to convert the coefficients of the design matrix into an arbitrary number of contrasts which are to be tested equal to zero. The columns of fit define a set of contrasts which are to be tested equal to zero.
这些功能是用于在基因差异表达的证据顺序排名。他们使用的经验Bayes方法，朝着一个共同的价值缩水探针明智的样本方差和个体差异（史密斯，2004），以充实的自由度。函数作为输入参数接受fit从函数拟合模型对象lmFit，lm.series，mrlm或gls.series。拟合模型对象可能已被处理contrasts.fit之前传递eBayes转换成任意数量的对比测试等于零的设计矩阵的系数。 fit列定义一组对比测试等于零。
The empirical Bayes moderated t-statistics test each individual contrast equal to zero. For each probe (row), the moderated F-statistic tests whether all the contrasts are zero. The F-statistic is an overall test computed from the set of t-statistics for that probe. This is exactly analogous the relationship between t-tests and F-statistics in conventional anova, except that the residual mean squares and residual degrees of freedom have been moderated between probes.
经验Bayes主持t-统计量检验每一个人的对比等于零。每个探针（行），F-统计的放缓测试是否所有的反差是零。 F统计是一套，探针从t-统计量计算的整体测试。这正是类似的t-检验和常规ANOVA F统计量之间的关系，除剩余平均平方和残差自由度已探针之间的缓和。
The estimates s2.prior and df.prior are computed by fitFDist. s2.post is the weighted average of s2.prior and sigma^2 with weights proportional to df.prior and df.residual respectively. The lods is sometimes known as the B-statistic. The F-statistics F are computed by classifyTestsF with fstat.only=TRUE.
估计s2.prior和df.prior是fitFDist计算。 s2.post是s2.prior和sigma^2与重量成正比df.prior和df.residual分别加权平均。 lods有时也被称为B - 统计。 F-统计F计算classifyTestsFfstat.only=TRUE。
eBayes doesn't compute ordinary (unmoderated) t-statistics by default, but these can be easily extracted from the linear model output, see the example below.
eBayes不计算默认情况下，普通（未管制）t-统计量，但这些可以很容易地从线性模型输出中提取，见下面的例子。
ebayes is the earlier and leaner function. eBayes is intended to have a more object-orientated flavor as it produces objects containing all the necessary components for downstream analysis.
ebayes是早期和精简的功能。 eBayes是为了有一个更面向对象的味道，因为它产生了下游分析包含所有必要的组件对象。
treat computes empirical Bayes moderated-t p-values relative to a minimum required fold-change threshold. Use topTreat to summarize output from treat. Instead of testing for genes which have log-fold-changes different from zero, it tests whether the log2-fold-change is greater than lfc in absolute value (McCarthy and Smyth, 2009). treat is concerned with p-values rather than posterior odds, so it does not compute the B-statistic lods. The idea of thresholding doesn't apply to F-statistics in a straightforward way, so moderated F-statistics are also not computed.
treat计算经验Bayes主持-T p值相对所需的最低折变阈值。使用topTreat总结从treat输出。相反的log倍，从零变化不同的基因测试，测试log2倍变化是否是比lfc绝对值（麦卡锡和史密斯，2009）。 treat是有关p值，而不是后赔率，因此它不会计算的B-统计lods。阈值的想法并不适用于在一个简单的方法，所以主持的F-统计还没有计算F-统计。
值----------Value----------
eBayes produces an object of class MArrayLM with the following components, see MArrayLM-class. ebayes produces an ordinary list without F or F.p.value. treat produces an MArrayLM object, but without lods, var.prior, F or F.p.value.
eBayes生产类MArrayLM以下组件，请参阅MArrayLM-class的对象。 ebayes不F或F.p.value产生普通列表。 treat生产MArrayLM对象，但没有lods，var.prior，F或F.p.value。
参数：t
numeric vector or matrix of moderated t-statistics
放缓的t-统计的数字向量或矩阵
参数：p.value
numeric vector of p-values corresponding to the t-statistics
p值对应的t-统计的数字向量
参数：s2.prior
estimated prior value for sigma^2. A vector if covariate is non-NULL, otherwise a scalar.
事先估计值sigma^2。 covariate如果是一个向量非NULL，另有一个标量。
参数：df.prior
degrees of freedom associated with s2.prior
以s2.prior自由度
参数：df.total
numeric vector of total degrees of freedom associated with t-statistics and p-values. Equal to df.prior+df.residual or sum(df.residual), whichever is smaller.
数字向量的总额度与t-统计量和p值的自由。等于df.prior+df.residual或sum(df.residual)，以较小者为准。
参数：s2.post
numeric vector giving the posterior values for sigma^2
数字向量sigma^2后值
参数：lods
numeric vector or matrix giving the log-odds of differential expression
数值向量或矩阵的差异表达的log赔率
参数：var.prior
estimated prior value for the variance of the log2-fold-change for differentially expressed gene
事先估计值的差异表达基因的log2倍变化的方差
参数：F
numeric vector of moderated F-statistics for testing all contrasts defined by the columns of fit simultaneously equal to zero
放缓的F-统计的数字向量测试由fit同时等于零列定义的所有对比
参数：F.p.value
numeric vector giving p-values corresponding to F
数值向量，给予相应的Fp值
作者（S）----------Author(s)----------
Gordon Smyth and Davis McCarthy
参考文献----------References----------
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btp053
Statistical Applications in Genetics and Molecular Biology, Volume 3, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3
参见----------See Also----------
squeezeVar, fitFDist, tmixture.matrix.
squeezeVar，fitFDist，tmixture.matrix。
An overview of linear model functions in limma is given by 06.LinearModels.
线性模型功能概述limma由06.LinearModels给出。
举例----------Examples----------
# See also lmFit examples[也看到lmFit例子]
# Simulate gene expression data,[模拟基因表达数据，]
# 6 microarrays and 100 genes with one gene differentially expressed[6芯片和100个基因，一个基因的差异表达]
set.seed(2004); invisible(runif(100))
M <- matrix(rnorm(100*6,sd=0.3),100,6)
M[1,] <- M[1,] + 1
fit <- lmFit(M)
# Ordinary t-statistic[普通的T-统计]
par(mfrow=c(1,2))
ordinary.t <- fit$coef / fit$stdev.unscaled / fit$sigma
qqt(ordinary.t,df=fit$df.residual,main="Ordinary t")
abline(0,1)
# Moderated t-statistic[主持ţ统计]
eb <- eBayes(fit)
qqt(eb$t,df=eb$df.prior+eb$df.residual,main="Moderated t")
abline(0,1)
# Points off the line may be differentially expressed[断了线的点可能会差异表达]
par(mfrow=c(1,1))
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 2012-2-25 23:14:21
