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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)
Verlagsversion Forschungsdaten 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Reconstruction; Allotypes; Shrinkage; Inference
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 11, Heft: 1, Seiten: , Artikelnummer: 5153 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) 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
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-554100-001
G-501901-024
G-503890-001
Förderungen 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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