ebayes {limma}R Documentation

Empirical Bayes Statistics for Differential Expression

Description

Given a microarray linear model fit, compute moderated t-statistics, moderated F-statistic, and log-odds of differential expression by empirical Bayes moderation of the standard errors towards a common value.

Usage

ebayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4),
       trend=FALSE, robust=FALSE, winsor.tail.p=c(0.05,0.1))
eBayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4),
       trend=FALSE, robust=FALSE, winsor.tail.p=c(0.05,0.1))
treat(fit, lfc=0, trend=FALSE, robust=FALSE, winsor.tail.p=c(0.05,0.1))

Arguments

fit

an MArrayLM fitted model object produced by lmFit or contrasts.fit. For ebayes only, fit can alternatively be an unclassed list produced by lm.series, gls.series or mrlm 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

trend

logical, should an intensity-trend be allowed for the prior variance? Default is that the prior variance is constant.

robust

logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances?

winsor.tail.p

numeric vector of length 1 or 2, giving left and right tail proportions of x to Winsorize. Used only when robust=TRUE.

lfc

the minimum log2-fold-change that is considered scientifically meaningful

Details

These functions are 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.

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.

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.

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, kept for backwards compatibility, while eBayes is the later more object-orientated version. The difference is that ebayes outputs only the empirical Bayes statistics whereas eBayes adds them to the fitted model object fit. eBayes is recommended for routine use as it produces objects containing all the necessary components for downstream analysis

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.

If trend=TRUE then an intensity-dependent trend is fitted to the prior variances s2.prior. Specifically, squeezeVar is called with the covariate equal to Amean, the average log2-intensity for each gene. See squeezeVar for more details.

If robust=TRUE then the robust empirical Bayes procedure of Phipson et al (2016) is used. See squeezeVar for more details.

Value

eBayes produces an object of class MArrayLM (see MArrayLM-class) containing everything found in fit plus the following added components:

t

numeric vector or matrix of moderated t-statistics

p.value

numeric vector of p-values corresponding to the t-statistics

s2.prior

estimated prior value for sigma^2. A vector if covariate is non-NULL, otherwise a scalar.

df.prior

degrees of freedom associated with s2.prior

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.

s2.post

numeric vector giving the posterior values for sigma^2

lods

numeric vector or matrix giving the log-odds of differential expression (natural log scale).

var.prior

estimated prior value for the variance of the log2-fold-change for differentially expressed gene

F

numeric vector of moderated F-statistics for testing all contrasts defined by the columns of fit simultaneously equal to zero

F.p.value

numeric vector giving p-values corresponding to F

treat a produces an MArrayLM object similar to eBayes but without lods, var.prior, F or F.p.value. ebayes produces an ordinary list containing the above components except for F and F.p.value.

Note

The algorithm used by eBayes and treat with robust=TRUE was revised slightly in limma 3.27.6. The minimum df.prior returned may be slightly smaller than previously.

Author(s)

Gordon Smyth and Davis McCarthy

References

McCarthy, D. J., and Smyth, G. K. (2009). Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25, 765-771. http://bioinformatics.oxfordjournals.org/content/25/6/765

Loennstedt, I., and Speed, T. P. (2002). Replicated microarray data. Statistica Sinica 12, 31-46.

Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10, 946-963. http://projecteuclid.org/euclid.aoas/1469199900

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, Article 3. http://www.statsci.org/smyth/pubs/ebayes.pdf

See Also

squeezeVar, fitFDist, tmixture.matrix.

An overview of linear model functions in limma is given by 06.LinearModels.

Examples

#  See also lmFit examples

#  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)

#  Moderated t-statistic
fit <- eBayes(fit)
topTable(fit)

#  Ordinary t-statistic
ordinary.t <- fit$coef / fit$stdev.unscaled / fit$sigma

#  Q-Q plots of t statistics
#  Points off the line may be differentially expressed
par(mfrow=c(1,2))
qqt(ordinary.t, df=fit$df.residual, main="Ordinary t")
abline(0,1)
qqt(fit$t, df=fit$df.total,main="Moderated t")
abline(0,1)
par(mfrow=c(1,1))

[Package limma version 3.32.5 Index]