A strategy to incorporate prior knowledge into correlation network cutoff selection

Elisa Benedetti, Maja Pučić-Baković, Toma Keser, Nathalie Gerstner, Mustafa Büyüközkan, Tamara Štambuk, Maurice H. J. Selman, Igor Rudan, Ozren Polašek, Caroline Hayward, Hassen Al-Amin, Karsten Suhre, Gabi Kastenmüller, Gordan Lauc and Jan Krumsiek ()
Additional contact information
Elisa Benedetti: Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health
Maja Pučić-Baković: Genos Glycoscience Research Laboratory
Toma Keser: University of Zagreb
Nathalie Gerstner: Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health
Mustafa Büyüközkan: Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health
Tamara Štambuk: Genos Glycoscience Research Laboratory
Maurice H. J. Selman: Leiden University Medical Center
Igor Rudan: University of Edinburgh
Ozren Polašek: University of Split School of Medicine
Caroline Hayward: University of Edinburgh
Hassen Al-Amin: Weill Cornell Medicine in Qatar
Karsten Suhre: Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City
Gabi Kastenmüller: Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health
Gordan Lauc: Genos Glycoscience Research Laboratory
Jan Krumsiek: Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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 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.

Date: 2020
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DOI: 10.1038/s41467-020-18675-3

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