| 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,std.coef=NULL) eBayes(fit,proportion=0.01,std.coef=NULL)
fit |
a list object produced by lm.series, gls.series, rlm.series or lmFit containing components coefficients, stdev.unscaled, sigma and df.residual |
proportion |
assumed proportion of genes which are differentially expressed |
std.coef |
assumed standard deviation of log2 fold changes for differentially expressed genes. Normally this parameter is estimated from the data. |
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. (2003). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. http://www.statsci.org/smyth/pubs/ebayes.pdf
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