While intuitively fuzzy enrichment analysis should be more accurate than ‘crisp’ enrichment analysis, because ‘fuzzy’ enrichment considers the ranks and magnitude of genes in both the input set and the library sets, our initial results so far only show a marginal enhancement, utilizing the same TF-centered benchmark presented above (Figure 1B). In the future, we plan to further explore ways to improve the performance of the fuzzy set enrichment analysis idea. It is also important to note that with the fuzzy set enrichment analysis, the scaling method used to convert typical values that represent, for example, level of differential expression, into membership values between 0 and 1 is important. Overall effective use of fuzzy enrichment analysis requires advanced computational expertise. However, in the near future, we plan to make such transformations easier and more transparent.
