q-values are an estimate of the False Discovery Rate (FDR), given multiple hypothesis tests, assuming no correlation between tests. As an alternative to Bonferroni and other p-values adjustment methods, they are often reported in journals instead of p-values.
The typical interpretation of q-values/FDR is that if you were to cut off significance at a certain p-value, the q-value would tell you the likely proportion of false positives in the set of significant genes/gene sets.
Modern hypothesis testing is not built for multiple tests, but statisticians are bending over backwards to try to account for them. Using EGAN, or any software that produces p-values, it is important to remember that the p-value significance is only valid upon corroborating evidence.
Hope that helps,
Jesse
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Showing both in the table is a good idea. I suggest using p-value for coming up with new hypotheses that you might test.
Jesse
Hi Dirk,
PermuSEED values range between 0 and one, and are not log10.
Jesse