Hi BioC, I've tried to glean what I can from the limma usersguide but would like confirmation that my design and contrasts matrices for lmfit make sense. I have been given 16 2-channel plates, 8 testing gene over expression and 8 testing a gene knock out. Each group of 8 has 2 biological replicates each of which has 2 technical replicates each of which is dye-swapped. That is to say, I have plates: 1 OE_ctrl OE1 2 OE_ctrl OE1 3 OE1 OE_ctrl 4 OE1 OE_ctrl 5 OE_ctrl OE2 6 OE_ctrl OE2 7 OE2 OE_ctrl 8 OE2 OE_ctrl 9 KD_ctrl KD1 10 KD_ctrl KD1 11 KD1 KD_ctrl 12 KD1 KD_ctrl 13 KD_ctrl KD2 14 KD_ctrl KD2 15 KD2 KD_ctrl 16 KD2 KD_ctrl Note that the controls from each set of 8 are technical replicates. Plates 4 and 9 have 0-weights due to low quality in 60% and 91% of their spots, so I removed them from. My design matrix is then: d=cbind( DyeEffect=1, OE1vOECTL=c(1,1,-1,0,0,0,0,0,0,0,0,0,0,0), OE2vOECTL=c(0,0,0,1,1,-1,-1,0,0,0,0,0,0,0), KD1vKDCTL=c(0,0,0,0,0,0,0,1,-1,-1,0,0,0,0), KD2vKDCTL=c(0,0,0,0,0,0,0,0,0,0,1,1,-1,-1) ) By explicitly modeling each block with it's own coefficient, I don't need to give a block argument to lmFit. The linear model for each probe would then be: Y_i = DyeEffect + OE1vOECTL + OE2vOECLT + KD1vKDCTL + KD2vKDCTL + e_i for i=1..14 Finally, to get the estimated fold due to OE and KD, the contrasts would be: cont.matrix=makeContrasts( OE=(OE1vOECTL+OE2vOECTL)/2, KD=(KD1vKDCTL+KD2vKDCTL)/2, DIFF=(OE1vOECTL+OE2vOECTL-KD1vKDCTL-KD2vKDCTL)/2 levels=d ) I'm wondering if there's another design that would also incorporate the fact that OE1 and OE2 use the same controls. Also, is there any point in trying to salvage at least some information from the low quality plates? Any comments are appreciated. thanks, michael s