ebayes package:limma R Documentation _E_m_p_i_r_i_c_a_l _B_a_y_e_s _S_t_a_t_i_s_t_i_c_s _f_o_r _D_i_f_f_e_r_e_n_t_i_a_l _E_x_p_r_e_s_s_i_o_n _D_e_s_c_r_i_p_t_i_o_n: 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. _U_s_a_g_e: ebayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4)) eBayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4)) treat(fit, lfc=0) _A_r_g_u_m_e_n_t_s: 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' 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 lfc: the minimum log2-fold-change which is considered material _D_e_t_a_i_l_s: 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. 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. 'eBayes' is intended to have a more object-orientated flavor 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. _V_a_l_u_e: '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'. 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' 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 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' _A_u_t_h_o_r(_s): Gordon Smyth _R_e_f_e_r_e_n_c_e_s: McCarthy, D. J., and Smyth, G. K. (2009). Testing significance relative to a fold-change threshold is a TREAT. _Bioinformatics_. Loennstedt, 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_, Volume *3*, Article 3. _S_e_e _A_l_s_o: 'squeezeVar', 'fitFDist', 'tmixture.matrix'. An overview of linear model functions in limma is given by 06.LinearModels. _E_x_a_m_p_l_e_s: # 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) # 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))