9 months ago by
Since a GO enrichment analysis runs many, many, many tests (either one test for each gene or one test for each GO-term, can't remember) just by chance alone you would expect to see terms that seemingly have a significant p-value
Let's say you set your p-value cutoff of 0.05. You run one test and your p-value is below 0.05, nothing of significance. You keep on repeating tests, by test 20 you have a significant p-value, hooray! However, with a p-value cutoff of 0.05, you expect to see one significant test purely by chance after 20 tests (20*0.05 = 1). See slide 4 here for another example.
That's why software that runs many many tests runs multiple test correction to get around this problem, it adjusts the p-values based on the number of tests you ran (~the size of your input dataset).
In your case I would ignore the column of the unadjusted p-value and just look at the adjusted p-values, if they're not <0.05 there then they are not significant.