Path-level interpretation of Gaussian graphical models using the pair-path subscore.
BMC Bioinformatics
; 23(1): 12, 2022 Jan 05.
Article
en En
| MEDLINE
| ID: mdl-34986802
ABSTRACT
BACKGROUND Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS:
We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https//github.com/nathan-gill/pps .CONCLUSIONS:
The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Glucemia
/
Hiperglucemia
Tipo de estudio:
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos