I have a great subtitle for this nice new paper from Anuli Uzozie et al.,currently in press at MCP.
At first glance, this is a boring paper. They've got a bunch of samples and they've got a bunch of discovery data so they design SRMs on a relatively small number of interesting targets (their heatmap shows less than 30 at the end of the paper) and run these samples on their TSQ Vantage. Sounds like they forgot to write the last chapter of their previous paper and here, somehow, they got it into MCP.
However, this might be the best validation exercises I've ever seen. It really makes me suspect one of those fancy biostatistics people was involved in this from the very beginning -- because they get so much data out of it! Start with the fancy randomization stuff (boring...but good science...) and the level of downstream statistics that makes REALLY impressive conclusions from relatively little data -- and I'm walking away from this paper a little bored (which might just be 'cause I'm typing rather than finishing my coffee) but seriously impressed....
...and wondering if I designed my experiments just a little better if I wouldn't have just a whole lot more data out of every run....?
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