Taken together, our results demonstrate the superior performance of our approach compared to standard binary enrichment and rank-sum tests. Although methods with tuneable parameters might yield improved results in a particular setting, it is difficult to predict the optimal parameter choice. Indeed, the optimal parameter depends on sample size, as well as complexity and heritability of the phenotype. Another issue with binary enrichment tests is that the hypergeometric distribution is discrete, which leads to conservative p-values, especially if the expected number of successful draws is low. Our pathway scoring approach avoids this problem. Also, our approach lends itself to naturally extending pathway scoring in case genes have probabilistic membership in predefined pathways.
