discussion: mention methods to limit number of permutations

content/06.discussion.md

@@ -44,6 +44,7 @@ We worked on a sample with the top variable genes in a single tissue from GTEx t
 Although CCC is much faster than MIC, Pearson and Spearman are still the most computationally efficient since they only rely on simple data statistics.
 Our results, however, reveal the advantages of using more advanced coefficients like CCC for detecting and studying more intricate molecular mechanisms that replicated in independent datasets.
 The application of CCC on larger compendia, such as recount3 [@pmid:34844637] with thousands of heterogeneous samples across different conditions, can reveal other potentially meaningful gene interactions.
+We compute $P$-values using permutation tests, which are computationally intensive; in the future, we plan to explore approaches that limit the number of permutations such as those using extreme value theory [@doi:10.1093/bioinformatics/btp211].
 The single parameter of CCC, $k_{\mathrm{max}}$, controls the maximum complexity of patterns found and also impacts the compute time.
 Our analysis suggested that $k_{\mathrm{max}}=10$ was sufficient to identify both linear and more complex patterns in gene expression.
 A more comprehensive analysis of optimal values for this parameter could provide insights to adjust it for different applications or data types.