Hello, We have been using voom and limma for some time now, and while we're fairly happy with it, it seems to produce significance levels that are on the conservative side. We also use edgeR to produce more optimistic results, but don't entirely trust the significance levels that it reports. I am looking for something in-between these extremes, and want to run an idea past this list as a sanity check. I would especially value Gordon and Charity's comments if they have time. The voom log transformation is essentially: log2( (count+0.5) / library.size ) It then does some clever things with weights. What I'm considering instead is log2( count / library.size + moderation.amount / mean.library.size ) where moderation.amount is much larger then 0.5, say 5. A couple of things here: - Instead of down-weighting low counts, I'm trying to get rid of the extra variation from low counts by artificially left censoring the data. - I'm using the mean of the libaray sizes because I want the left censor to be in the same place for each sample even if the library sizes are different, so that if a gene is entirely switched off in one condition it won't look variable just because there is a different left censor in each sample. I'm also using this transformation to create heatmaps. This seems to be working with the data set I am working with, I get more significant results and they seem reasonable by eye. It seems to me that even if this approach isn't ideal it should at least be safe, at worst it will cause limma to reduce the df.prior and produce less significant results. Anything I've missed? -- Paul Harrison Victorian Bioinformatics Consortium / Monash University