Consider the simple one-way design, with biological and technical reps. Generally we would consider the biological reps to be blocks. (For simplicity, we can think of this as a one-channel analysis). The usual ANOVA seems to be at odds with what we get from limma (ignoring the eBayes step, which clearly cannot be recovered from the classical treatment). The usual ANOVA (t treatments, b biological reps, n technical reps within biological reps, giving ntb arrays) source df MS F treatment t-1 MS(T) MS(T)/MS(B) bio rep b-1 MS(B) MS(B)/MSE (although we don't really care about this) error=T*B ntb-t-b+1 MSE However, the Limma manual suggests using duplicateCorrelation and block to handle block designs, and this gives a different ANOVA. In particular, the error d.f. for this ANOVA is ntb-t. Using this method, the within block correlation is used in computing the t-statistics for the treatment, so you do not get the simple 1-way ANOVA that would come from ignoring block, but you cannot recover the p-value from the usual ANOVA, either. If you put in bio rep as a fixed factor, then Limma will use the MSE as the denominator for the contrast tests, so this also does not recover the ANOVA. I have not tried this computation with replicate spots (only replicate arrays) but either: 1) the usual ANOVA is right and duplicateCorrelation is doing something odd 2) duplicate Correlation is right and I don't understand the usual ANOVA 3) both methods are correct for somewhat different models, and I don't understand the statistical implications of this I am not discounting 3 - as the statisticians in the crowd know, there are 2 versions, constrained and unconstrained, for the simplest case of balanced ANOVA with fixed and random effects which lead to different p-values for some effects and both are defensible for almost any data set. Anyways, I would like to understand this better. So, I would welcome comments. Naomi S. Altman 814-865-3791 (voice) Associate Professor Bioinformatics Consulting Center Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111