Dear List,
   
  I have the following situation when using LIMMA and would be extremely grateful for any suggestions.
  I have two treatments T1 and T2 compared versus Control, 5 replicates each, using AB1700 system. Before fitting linear models with lmFit and estimating moderated t-statistics, I am filtering on signal-to-noise S/N ratio (as recommended by ABI). The main purpose of this filtering is to avoid comparing noise to noise. So, for each pair of compared treatments (say T1vs.Control or T2vs.Control), only probes that have >50% of high S/N ratios in at least one of the compared treatments, are considered as “good”.
  For example, a probe Y which is “good” in T1 but is “bad” in both T2 and C will be kept for T1vs.Control but left out for T2vs.Control.
  One of the advantages of LIMMA, as far as I understand it, is that one can include ALL treatments T1, T2 and C in lmFit to get more statistical power. This makes the above filtering trickier. Let’s suppose we do our analysis, using the following code:
   
  temp<-normalizeBetweenArrays(log2(raw_signals), method=”quantile”)
design <- model.matrix(~0 +factor(c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)))
colnames(design) <- c("Control","T1","T2")
contrast.matrix <- makeContrasts(T1-Control,T2-Control,levels=design)
fit <- lmFit(temp, design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
topTable(fit2, coef=1, adjust=”BH”)
 
  Ultimately, we are interested in similarities/differences between the lists of genes regulated in T1vs.Control and T2vs.Control. Therefore, to make contrasts (lists of differentially expressed genes) T1vs.Control and T2vs.Control comparable, the gene list we feed into lmFit should be relevant for both comparisons, which means the above probe Y will be filtered out (it has low S/N ratios for both T2 and Control and thus, will be filtered out to avoid comparing noise versus noise in T2vs.Control). On the other hand, Y is still an interesting probe in the T1vs.Control comparison and we are just losing it. The way out of this seems to be in making separate pair-wise comparisons using two bits of code:
   
  temp<-normalizeBetweenArrays(log2(raw_signals1), method=”quantile”)
design <- model.matrix(~0 +factor(c(1,1,1,1,1,2,2,2,2,2)))
colnames(design) <- c("Control","T1")
contrast.matrix <- makeContrasts(T1-Control,levels=design)
fit <- lmFit(temp, design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2_1 <- eBayes(fit2)
                     
  temp<-normalizeBetweenArrays(log2(raw_signals2), method=”quantile”)
design <- model.matrix(~0 +factor(c(1,1,1,1,1,3,3,3,3,3)))
colnames(design) <- c("Control","T2")
contrast.matrix <- makeContrasts(T2-Control,levels=design)
fit <- lmFit(temp, design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2_2 <- eBayes(fit2)
 
  and comparing results of topTable for fit2_1 and fit2_2. However, here one would lose advantages of LIMMA analysis where all treatments are analysed together to increase the power.
  Could you please comment on this? Is there any way to keep such probes as Y and still analyse ALL treatments together?
   
  Thank you for your help,
  Lev.
   

       
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