A strategy to incorporate prior knowledge into correlation network cutoff selection.

ABSTRACT: Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to

SUBMITTER: Benedetti E 

PROVIDER: S-EPMC7560866 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature