I might be too old for using nonlinear dimensionality reduction in my daily life but these smart young people with all their neuroplasticity are getting the hang of it. I think I've posted this before, but here is a good video introduction to these concepts. (All of this is just an aside, but it is really cool seeing t-SNE's in proteomics that make sense :)
Check this out, though. You know all the data people have been acquiring using LOPIT (localization of proteins by their organelles, or something, I'm at least close)? These data aren't just a neat trick like I thought. This group reuses LOPIT data to better understand other data! Think about where most of our annotations come from that say "this protein is in the cytoplasm, this protein is in the nucleus, etc.," I'd bet you $4 that it didn't come from proteomics data. It is more likely that in 1954 someone tagged a yeast protein with uranium and before they died at their microscope, they were pretty sure that protein was at the yeast membrane. Then someone in the 90s when all crazy with BLAST on their NetScape browser and found a 10% sequence match and 1e-74 whatever crazy metric-less score on BLAST that makes everything sound more significant than any metric ever and now that human protein is annotated as membrane forever. We should probably think about updating some of that stuff with LOPIT data....
When you do? These authors demonstrate multiple examples where evaluating quantitative data in this subcellular context further strengthens the conclusions of earlier studies. Even more interesting? There is a strong example where the conclusions you can draw from the data seem very different when thinking of the protein in an organellar context rather than as a list of ups and downs.
The trick here is how to apply these maps without having to bother with updating my R packages which I'm sure are out of sync between PCs by now....
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