I have a problem with the fundamentals of hypergeometric distribution for gene enrichment analysis. Here is the problem: I have a set of genes (population size 1500), a mixture of various genes including tumor suppressors (TS), oncogenes (OG), etc. Let's say the number of TS in the population is 700 (number of success in the population). Now, I have a so-called algorithm claiming that has the ability to select a subset of genes showing a cancerous behavior (meaning if the algorithm select the highest number of OG and the fewest number of TS, it has high predictive power). So, if the presumed algorithm can select a subset of 1080 genes (sample size) which 20 of them are TS (number of success in the sample size), the algorithm seems (qualitatively) to acts highly selective.
Nevertheless, when using hypergeometric test, the resulting p-value is insignificant, simply because the number of TS is considerably low and the test wrongly shows the algorithm behaved by chance. But, the point is that as the number of successes in the sample size decrease it represents the predictive power of algorithm. Therefore, how can I manage this situation and modify my problem? I thing I cannot simply use 1-calculated_p_value or use Fisher's test here, because the population size comprises of various genes and not TS and non-TS (maybe I am not correct).
I appreciate any help in advance.