Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

A strategy to incorporate prior knowledge into correlation network cutoff selection

MPG-Autoren
/persons/resource/persons262202

Gerstner,  Nathalie
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Benedetti, E., Pucic-Bakovic, M., Keser, T., Gerstner, N., Bueyuekoezkan, M., Stambuk, T., et al. (2020). A strategy to incorporate prior knowledge into correlation network cutoff selection. NATURE COMMUNICATIONS, 11(1): 5153. doi:10.1038/s41467-020-18675-3.


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-2729-F
Zusammenfassung
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.