I have a data set which pretty much duplicates the experimental design of one of the limma examples, so I am using that with great results :-D There is one difference though - the limma example has no duplicate spots, whereas my design does. So I did two runs of limma, one to find differentially expressed spots, the other to find differentially expressed genes (I assume limma would take the duplicates into question and do some averaging). The code I used was almost identical. For the spots: > design <- c(1,-1,1,-1) > fit <- lmFit(MAptnorm, design) > fit <- eBayes(fit) > topTable(fit,number=10,adjust="fdr") And for the genes: > design <- c(1,-1,1,-1) > fit <- lmFit(MAptnorm, design, ndups=2) > fit <- eBayes(fit) > topTable(fit,number=10,adjust="fdr") Note the only difference is the "ndups=2" bit. Now, for differentially regulated spots, I was lucky, lots of spots came up and mostly the two duplicates were side by side. One gene's duplicate spots were first and third in the top ten with M values of -5.4 - result! Now, when I did the second run, with "ndups=2", the results changed completely. Something must have gone wrong, my top gene has an M value of -0.8, it's not the same gene as before, and more's the point, topTable still lists BOTH duplicate spots. My only conclusion from this is that I am using limma incorrectly. But... How? Mick

> I have a data set which pretty much duplicates the experimental design > of one of the limma examples, so I am using that with great results :-D > > There is one difference though - the limma example has no duplicate > spots, whereas my design does. So I did two runs of limma, one to find > differentially expressed spots, the other to find differentially > expressed genes (I assume limma would take the duplicates into question > and do some averaging). > > The code I used was almost identical. For the spots: > >> design <- c(1,-1,1,-1) >> fit <- lmFit(MAptnorm, design) >> fit <- eBayes(fit) >> topTable(fit,number=10,adjust="fdr") > > And for the genes: > >> design <- c(1,-1,1,-1) >> fit <- lmFit(MAptnorm, design, ndups=2) >> fit <- eBayes(fit) >> topTable(fit,number=10,adjust="fdr") > > Note the only difference is the "ndups=2" bit. Now, for differentially > regulated spots, I was lucky, lots of spots came up and mostly the two > duplicates were side by side. One gene's duplicate spots were first and > third in the top ten with M values of -5.4 - result! > > Now, when I did the second run, with "ndups=2", the results changed > completely. Something must have gone wrong, my top gene has an M value > of -0.8, it's not the same gene as before, and more's the point, > topTable still lists BOTH duplicate spots. > > My only conclusion from this is that I am using limma incorrectly. > But... How? Your duplicate spots are apparently not side-by-side by rows, i.e., the spacing is not =1. So you need to set the correct spacing. If you really do have duplicate spots with a regular spacing, getLayout() with guess=TRUE might figure out the spacing for you. If your duplicate spots are not regularly spaces, then you have to stick with your first analysis, i.e., don't use ndups. Gordon > Mick