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Benedetti, E. ; Pučić-Baković, M.* ; Keser, T.* ; Gerstner, N. ; Büyüközkan, M. ; Štambuk, T.* ; Selman, M.H.J.* ; Rudan, I.* ; Polašek, O.* ; Hayward, C.* ; Al-Amin, H.* ; Suhre, K.* ; Kastenmüller, G. ; Lauc, G.* ; Krumsiek, J.

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

Nat. Commun. 11:5153 (2020)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Reconstruction; Allotypes; Shrinkage; Inference
Language english
Publication Year 2020
HGF-reported in Year 2020
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 11, Issue: 1, Pages: , Article Number: 5153 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
30505 - New Technologies for Biomedical Discoveries
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-554100-001
G-501901-024
G-503890-001
Grants MRC
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)
DOI 10.1038/s41467-020-18675-3
WOS ID WOS:000582680100002
Scopus ID 85092552390
PubMed ID 33056991
Erfassungsdatum 2020-11-24
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