A strategy to incorporate prior knowledge into correlation network cutoff selection

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...

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Published in:Nature communications 2020-10, Vol.11 (1), p.5153-5153, Article 5153
Main Authors: Benedetti, Elisa, Pučić-Baković, Maja, Keser, Toma, Gerstner, Nathalie, Büyüközkan, Mustafa, Štambuk, Tamara, Selman, Maurice H. J., Rudan, Igor, Polašek, Ozren, Hayward, Caroline, Al-Amin, Hassen, Suhre, Karsten, Kastenmüller, Gabi, Lauc, Gordan, Krumsiek, Jan
Format: Article
Language:English
Subjects:
631/114
631/114/2390
631/114/2397
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
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Summary: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. Correlation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap between inferred networks and prior knowledge.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-18675-3