Hi Bioconductors I followed the example 10.2 in Limma's user guide which includes a coefficient for each mouse in the linear model to account for technical replication. I am somewhat puzzled because the example in the user's guide returns non-estimable coefficients and hence the contrast (mu1+mu2-wt2)/2 as proposed in the guide cannot be computed... (I assume the function "designMatrix()" can be replaced by "modelMatrix()".) Since the given example returns an error, and I am not a statistician, I am struggling in finding the correct solution. As we have four replicate spots on our arrays I cannot use the "duplicateCorrelation()" function here. How does the correct design matrix look like and which contrasts need to be computed? Has anyone a solution either for the example given in the user guide or my own experiment listed below? MANY THANKS! Pie I have 3 biological replicates and two technical replicates for each biological replicate, i.e., FileName Cy3 Cy5 File1 A1 B1 File2 B1 A1 File3 A2 B2 File4 B2 A2 File5 A3 B3 File6 B3 A3 and would like to estimate the effect between A and B including a coefficient to fit a separate effect for each individual.