I am currently interpreting some GO terms I’ve clustered using binary clustering. The problem is that in every cluster, I get tens of terms having specifiers as “positive” and “negative regulation of …”. So I’m really confused and can’t decide how to interpret the results. What I did is the following: I ordered the terms according to their BH adjusted p values, and then decided to consider the specifier having the most significant p-value as ultimately describing the regulation of the cluster’s function… Is that the right way to do it? And what about the other specifier, should I just ignore its presence in favor of the more significant (e.g if the term “negative regulation of …” has the lowest pvalue, regardless if there is other “positive regulation of …” terms with higher and less significant pvalues, should I just assume the regulation is negative and ignore the positive terms?)? I also thought about counting the positive and negative terms and then take the specifier having the largest count as describing the cluster. Am i thinking right?