Dear all I have a few questions on comparing vsn and quantile normalization. It would be really great to have some thoughts from the mailing list. To begin with, are there any special criteria on how to deal with expression data that is somewhat bimodally distributed ? We have single-channel codelink chips and nearly all show a bimodal distribution (logged histograms). There seems to be a peak in low intensity region and another in high intensity region with a furrow in between. How does the deviation from the normal distribution affect quantile and vsn normalization ? we have extracted the raw intensity expression values using codelink's own software. we want to use bioconductor for normalization. we have 10 arrays in one experiment and have repeated the experiment independently once again. problem in experiment 1: ->normalization using VSN did not yield satisfactory results. there are still marked differences between chips in experiment 1 even though all replicates show excellent correlation (>0.99). ->normalization using quantiles yielded near perfect boxplots. problem in comparing experiment 1 with experiment 2(repeat): ->expression values on the whole seem higher in experiment 2. VSN normalization does not really do anything drastic as before. so differences in intensities remain between experiment 1 and 2 and also within experiment 1. ->Quantiles again yield excellent boxplots. What can one conclude ? Does the problem really lie with the data or does quantile normalization overfit the data ? What can be then thrown away in this case? The data from experiment 1 or the vsn normalization? Are there other normalization methods that can be used with such data ? any comments would be helpful. regards Rohit