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Path-level interpretation of Gaussian graphical models using the pair-path subscore.

Publication ,  Journal Article
Gill, NP; Balasubramanian, R; Bain, JR; Muehlbauer, MJ; Lowe, WL; Scholtens, DM
Published in: BMC Bioinformatics
January 5, 2022

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.

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Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

January 5, 2022

Volume

23

Issue

1

Start / End Page

12

Location

England

Related Subject Headings

  • Pregnancy Outcome
  • Pregnancy
  • Normal Distribution
  • Hyperglycemia
  • Humans
  • Female
  • Cross-Sectional Studies
  • Blood Glucose
  • Bioinformatics
  • 49 Mathematical sciences
 

Citation

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Gill, N. P., Balasubramanian, R., Bain, J. R., Muehlbauer, M. J., Lowe, W. L., & Scholtens, D. M. (2022). Path-level interpretation of Gaussian graphical models using the pair-path subscore. BMC Bioinformatics, 23(1), 12. https://doi.org/10.1186/s12859-021-04542-5
Gill, Nathan P., Raji Balasubramanian, James R. Bain, Michael J. Muehlbauer, William L. Lowe, and Denise M. Scholtens. “Path-level interpretation of Gaussian graphical models using the pair-path subscore.BMC Bioinformatics 23, no. 1 (January 5, 2022): 12. https://doi.org/10.1186/s12859-021-04542-5.
Gill NP, Balasubramanian R, Bain JR, Muehlbauer MJ, Lowe WL, Scholtens DM. Path-level interpretation of Gaussian graphical models using the pair-path subscore. BMC Bioinformatics. 2022 Jan 5;23(1):12.
Gill, Nathan P., et al. “Path-level interpretation of Gaussian graphical models using the pair-path subscore.BMC Bioinformatics, vol. 23, no. 1, Jan. 2022, p. 12. Pubmed, doi:10.1186/s12859-021-04542-5.
Gill NP, Balasubramanian R, Bain JR, Muehlbauer MJ, Lowe WL, Scholtens DM. Path-level interpretation of Gaussian graphical models using the pair-path subscore. BMC Bioinformatics. 2022 Jan 5;23(1):12.
Journal cover image

Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

January 5, 2022

Volume

23

Issue

1

Start / End Page

12

Location

England

Related Subject Headings

  • Pregnancy Outcome
  • Pregnancy
  • Normal Distribution
  • Hyperglycemia
  • Humans
  • Female
  • Cross-Sectional Studies
  • Blood Glucose
  • Bioinformatics
  • 49 Mathematical sciences