This question is because I am misunderstanding how certain things fit together in Limma. There is no example like this in the documentation, and I am trying to figure out how to do this based on examples section 10.5 and 14.1. sorry for the lengthy post, this is a complicated one, but it might be an interesting case example for some of you. A simplified version of my experiment follows Background: Blood from 8 separate donors have been collected and undergone a cell sort. The sorted cells that we are interested in were divided and infected with HIV according to the following table (the letters do not mean the literal HIV subtype in this case, I have just simplified it to A,B,C and N=non-infected.). Filename Cy3 Cy5 Donor 1 Ref N_0 1 2 Ref N_6 1 3 Ref N_24 1 4 Ref N_74 1 5 Ref A_0 1 6 Ref A_6 1 7 Ref A_24 1 8 Ref A_74 1 9 Ref B_0 1 10 Ref B_6 1 11 Ref B_24 1 12 Ref B_74 1 13 Ref C_0 1 14 Ref C_6 1 15 Ref C_24 1 16 Ref C_72 1 ...for 7 more donors - I have a series of 2 channel array hybridizations against a common reference - the array used uses DUPLICATE spots (spacially spotted in pairs). - N is non-infected(this exp its HIV), - A,B,C are three different infection types - 0,6,24 are the times that the cells were harvested and RNA isolated. - A_0 is infected at time 0 which is different from non-infected 0 (N_0) in that A_0 is after 2 hours of incubation with the virus. - Total of 8 donors The question I have is how to deal with the ' donor effect' using Limma. First case (1): I could assume that my donor variability is much less than the variability in the treatments and just plow ahead(probably worth trying). In the second case (2), the problem being that there can be quite the donor variability so I am thinking that what might be better is if I subtract the 0 time point for each infection type WITHIN each donor from all the others so that all expression values are relative to 0: For example Donor1 N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, A_24-A_0, A_6-A_0, etc Donor1 N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, A_24-A_0, A_6-A_0, etc I would like to compare the difference between each donor for the non-infected N to characterize the donor variability so that I understand it and I would like to compare the infection types for each time point in the 2 different ways (cases). My ultimate goal it to compare the infection types at each time point against each other while reducing the noise due to donor variability. There are 2 things i need to know how to do How do I combine creating the contrast matrix and use it with calculating duplicate spot correlation in 14.1, for case 1? How do I create a contrast matrix to account for normalising against time 0 as in case (2) and then combine that with the duplicate spot correlation? lastly, are there in fact other proven methods for dealing with donor variability ? Thanks for any insight. Peter W.