ebayes {limma} | R Documentation |
Given a series of related parameter estimates and standard errors, compute moderated t-statistics and log-odds of differential expression by empirical Bayes shrinkage of the standard errors towards a common value.
ebayes(fit,proportion=0.01,stdev.coef.lim=c(0.1,4)) eBayes(fit,proportion=0.01,stdev.coef.lim=c(0.1,4))
fit |
a list object produced by lm.series , gls.series , mrlm or lmFit containing components coefficients , stdev.unscaled , sigma and df.residual |
proportion |
numeric value between 0 and 1, assumed proportion of genes which are differentially expressed |
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 |
These functions is used to rank genes in order of evidence for differential expression.
It uses an empirical Bayes method to shrink the gene-wise sample variances towards a common values and, in so doing, augmenting the degrees of freedom for the individual variances.
The function accepts as input output from the functions lmFit
, lm.series
, mrlm
or gls.series
.
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.
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
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
produces an ordinary list with the following components.
eBayes
adds the following components to fit
to produce an augmented object, usually of class MArrayLM
.
t |
numeric vector or matrix of penalized t-statistics |
p.value |
numeric vector of p-values corresponding to the t-statistics |
s2.prior |
estimated prior value for sigma^2 |
df.prior |
degrees of freedom associated with s2.prior |
s2.post |
vector giving the posterior values for sigma^2 |
lods |
numeric vector or matrix giving the log-odds of differential expression |
var.prior |
estimated prior value for the variance of the log2-fold-change for differentially expressed gene |
F |
numeric vector of F-statistics for testing all contrasts simultaneously equal to zero |
F.p.value |
numeric vector giving p-values corresponding to F |
Gordon Smyth
Lönnstedt, I. and Speed, T. P. (2002). Replicated microarray data. Statistica Sinica 12, 31-46.
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3, No. 1, Article 3. http://www.bepress.com/sagmb/vol3/iss1/art3
squeezeVar
, fitFDist
, tmixture.matrix
.
An overview of linear model functions in limma is given by 06.LinearModels.
# Simulate gene expression data, # 6 microarrays and 100 genes with one gene differentially expressed 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 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 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 par(mfrow=c(1,1))