Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

Zeya Wang, Veerabhadran Baladandayuthapani, Ahmed O. Kaseb, Hesham M. Amin, Manal M. Hassan, Wenyi Wang, Jeffrey S. Morris

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


It is well established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this article, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. We evaluate our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity. In application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), we ascertain how blood protein networks vary with disease severity, as measured by HepatoScore, a novel biomarker signature measuring disease severity. Our case study shows that the network connectivity increases with HepatoScore and a set of hub proteins as well as important protein connections are identified under different HepatoScore, which may provide important biological insights to the development of precision therapies for HCC.

Original languageEnglish
Pages (from-to)533-546
Number of pages14
JournalJournal of the American Statistical Association
Issue number538
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 American Statistical Association.


  • Bayesian adaptive shrinkage
  • Gene regulatory network
  • Nonstatic graph
  • Tumor heterogeneity
  • Undirected graphical models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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