Signaling pathways coupling phenomena

Two radically different approaches are currently available to identify the signaling pathways that are significantly impacted in a given condition: enrichment analysis and impact analysis. These approaches calculate a p-value that aims to quantify the significance of the involvement of the given pathway in the condition under study. These p-values were thought to be inversely proportional to the likelihood of their respective pathways being involved in the given condition, and hence be independent. Here we show that various pathways can affect each other´s p-values in significant ways. Thus, the significance of a given pathway in a given experiment has to be interpreted in the context of the other pathways that appear to be significant. In certain circumstances, pathways previously found to be significant with some of the existing methods may not be so. We hypothesize that the phenomenon is related to the amount of common genes between different pathways. Here we present results obtained by analyzing pathways obtained from the KEGG signaling pathways database. However, the same phenomenon is expected to influence the analysis of any pathways from any pathway repository.