Quick concept question here...
If I have two groups ("Functional" with 100 genes, "Control" with 50 genes) and I ran a bunch of models on each gene individually and found that "Functional" out of 100 genes, 50 were significant for a test, and "Control" (although I expected none) I got 10 showing a significant result.
Now, I still want to know if the proportion of significant results is different between these two unequal groups as that can still be meaningful to me.
Do I compare every single actual p-value (100 p-values in Functional, 50 in Control) using a one-tailed t-test (because I originally only expected significant results in the Functional category, so now I am expecting it to at least have a greater number of significant tests than in Control). OR...should I convert the p-values to remove any variation so any p-value <= 0.05, I change to 0.05 and anything above this I change to 1. I am thinking to do this to avoid really small/large p-values from driving variation in this test as I essentially want to know if the proportion of significant results is sig diff between the two groups. Does this make sense?