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 untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.
GrantsMRC European Commission Framework 6 project EUROSPAN FP7 contract BBMRI-LPC Croatian Science Foundation Republic of Croatia Ministry of Science, Education and Sports German Federal Ministry of Education and Research (BMBF) BMBF European Commission European Structural and Investment Funds grant Qatar National Research Fund (QNRF) Biomedical Research Program at Weill Cornell Medicine in Qatar - Qatar Foundation Medical Research Council (UK)