This is just a heads-up that, for the first time in several years, I've made a user visible change to the empirical Bayes procedure used in the limma package. I have changed the hyperparameter estimation to make it less sensitive to sample standard deviations which are zero to machine precision, as can arise from the RMA algorithm with small data sets. In particular, very small standard deviations and zero standard deviations are now treated in a unified way. This means that eBayes() and topTable() will produce slightly changed signficance results for all data sets. For most data sets the changes will be minor, affecting only the 3rd significant figure of t-statistics. For small data sets (few arrays), eBayes() does slighty more smoothing than before, which I think is a positive change. The only exception is for data sets which have many exactly zero standard deviations, which may experience somewhat less smoothing than at present. The data sets for which the change will be substantial are small data sets containing very small (1e-15) but non-zero standard deviations. For these data sets, the eBayes() algorithm will do much more smoothing than at present, producing far more stable results. I've committed the new version to the developmental version of Bioconductor as limma 2.4.0. I'd be pleased to receive feedback if there are problems. Further changes are also possible over the next few months. Cheers Gordon