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Mike Miller wrote:
In both gene chip and microarray studies, many SNPs are analyzed, so the
dimensionality is very high. Imagine that you have 100,000 observations
on all of 100 cases (with some disease) and 100 controls (without the
disease and matched with cases on ethnicity, etc.). If you use a
p-value cutoff of .05, and there are no real effects in the data, you
will have 5,000 false positives. So if you have 6,000 positives, you
have about 17% true and 83% false. And so it goes...
But surely they have some analysis that tells you that 5000 positives is
not enough, and they can tell you just how many positives they do need
before it is significant?
Actually, I have done a lot of research in the pure math side of this
kind of high dimensional probability, and indeed some of the other
faculty at MU are renouned world experts in this area. If you have any
problems in this area, I would certainly enjoy trying to tackle them.
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