I often run into situations which fall outside the realm of most existing microarray meta-analysis solutions- where I have two sets of arrays to compare (say, RNA from a particular cell type vs whole tissue), but the two sets are from different datasets and sometimes different platforms. Most of the time, a direct comparison is not appropriate because the variability due to the batch effect is greater than that due to the biology. Batch effect compensation methods such as COMBAT aren't appropriate as the batch effect and the target variable of interest are confounding.
So far, I've been normalizing the datasets separately and then compare them using RankProd. I'd like to try a different method, because I've had some complaints about unexpected results in my output genelists and so I'd like to make sure that the output from multiple methods correlates reasonably well so I can have some confidence in presenting the results. After doing a decent search, I haven't come up with much aside from RankProd and METRADISC that's actually semi-advertised as being able to handle this sort of scenario, the latter of which is also rank-based.
I'm starting to get to the point of just wanting to try some thing that sound crazy, like normalizing separately, combining, median-centering/scaling (when there's more than one data set involved), and then transforming to POE (MetaArray package) and following with differential expression testing. Would that actually...work?
Is it appropriate to use an effect size-based method as implemented in GeneMeta for this sort of thing?
I've been holding off on experimenting with this until finding a more solid answer, but it seems like I really need to make some progress on this soon.