I am developing an analysis of protein microarray data and have found that limma performs very well in terms of identifying positive controls in the assay. Unfortunately, there are a large number of known, non-specific interactors that previous people have been identified in earlier experiments. For the pipeline I would like to down-weight, but not exclude, these proteins from the analysis. I have an idea, but I'm not sure if it's a good one:
The thought is to add a value to the intensity of a probe that depends on its average across past experiments. This would essentially maintain the variance while reducing the fold changes between groups for that probe. It kind of places a higher burden of proof on the that interaction for it to be real.
The problem is that I won't be seeing true fold changes anymore when I look at my output and that seems pretty bad. Does anyone out there have any other solutions to this sort of problem or can think of a way to moderate the t values by incorporating old data? Or is there some feature of limma/eBayes that could handle probe weights when calculating p-values?
Any thoughts would be appreciated!