Genetic, metabolic and biochemical pathway analysis system and methods

Abstract

Identifying pathways that are significantly impacted in a given condition is a crucial step in the understanding of the underlying biological phenomena. All approaches currently available for this purpose calculate a p-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These p-values were previously thought to be independent. Here, we show that this is not the case, and that pathways can affect each other's p-values through a 'crosstalk' phenomenon that affects all major categories of existing methods. We describe a novel technique able to detect, quantify, and correct crosstalk effects, as well as identify novel independent functional modules. We assessed this technique on data from four real experiments coming from three phenotypes involving two species.