  Pathway enrichment analysis has become a widely used knowledge-based approach
for the interpretation of biomedical data. Its popularity has led to an
explosion of both enrichment methods and pathway databases. While the elegance
of pathway enrichment lies in its simplicity, multiple factors can impact the
results of such an analysis which may not be accounted for. Researchers may
fail to give influential aspects their due, resorting instead to popular
methods and gene set collections, or default settings. Despite ongoing efforts
to establish set guidelines, meaningful results are still hampered by a lack of
consensus or gold standards around how enrichment analysis should be conducted.
Nonetheless, such concerns have prompted a series of benchmark studies
specifically focused on evaluating the influence of various factors on pathway
enrichment results. In this review, we organize and summarize the findings of
these benchmarks to provide a comprehensive overview on the influence of these
factors. Our work covers a broad spectrum of factors, spanning from
methodological assumptions to those related to prior biological knowledge, such
as pathway definitions and database choice. In doing so, we aim to shed light
on how these aspects can lead to insignificant, uninteresting, or even
contradictory results. Finally, we conclude the review by proposing future
benchmarks as well as solutions to overcome some of the challenges which
originate from the outlined factors.
