Dear Gordon,
   
  As I understand the normalizeBetweenArrays function is designed to work with log2 transformed expression values, except for the case when VSN is applied. My question is about quantile normalization. Some people do it on raw values, while you recommend to use
  normalizeBetweenArrays(log2(expressionmatrix), method="quantile") as a correct call. I have played a little with some AB1700 data comparing results of these two options, below.
  1. signals<- normalizeBetweenArrays(signals_raw, method="quantile")
  design <- model.matrix(~0 +factor(c(1,2,1,2,2,1)))
  colnames(design) <- c("group1", "group2")
  contrast.matrix <- makeContrasts(group2-group1, levels=design)
  fit <- lmFit(log2(signals), design)
  fit2 <- contrasts.fit(fit, contrast.matrix)
  fit2 <- eBayes(fit2)
  topTable(fit2, adjust="BH")
  2. signals<- normalizeBetweenArrays(log2(signals_raw), method="quantile")
  design <- model.matrix(~0 +factor(c(1,2,1,2,2,1)))
  colnames(design) <- c("group1", "group2")
  contrast.matrix <- makeContrasts(group2-group1, levels=design)
  fit <- lmFit(signals, design)
  fit2 <- contrasts.fit(fit, contrast.matrix)
  fit2 <- eBayes(fit2)
  topTable(fit2, adjust="BH")
   
  They produce nearly identical results. So, is it just a feature of this particular dataset or these two scripts should give similar results in general?
  Thak you very much for help.
   
  With kind regards,
  Lev.

 		
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