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 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,std.coef=NULL)
     eBayes(fit,proportion=0.01,std.coef=NULL)

_A_r_g_u_m_e_n_t_s:

     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.

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     '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

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Lnnstedt, 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

_S_e_e _A_l_s_o:

     'fitFDist', 'tmixture.matrix'.

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

_E_x_a_m_p_l_e_s:

     #  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

