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 , rlm.series 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 |
This function is used to rank genes in order of evidence for differential expression.
The function accepts as input output from the functions lm.series
, rlm.series
or gls.series
.
The estimates s2.prior
and df.prior
are computed by fdist.fit
.
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
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 |
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 5.LinearModels.
# Simulate gene expression data, # 6 microarrays and 100 genes with one gene differentially expressed M <- matrix(rnorm(100*6,sd=0.3),100,6) M[1,] <- M[1,] + 1.6 fit <- lm.series(M) eb <- ebayes(fit) qqt(eb$t,df=eb$df+fit$df) abline(0,1) # Points off the line may be differentially expressed