Log In Sign Up

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

by   Zeya Wang, et al.

Graphical models are commonly used to discover associations within gene or protein networks for complex diseases such as cancer. Most existing methods estimate a single graph for a population, while in many cases, researchers are interested in characterizing 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 paper, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. Bayesian shrinkage algorithms are used to induce sparsity in the underlying graphical models. We assess our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity and a case study assessing how blood protein networks in hepatocellular carcinoma patients vary with severity of disease, measured by HepatoScore, a novel biomarker signature measuring disease severity.


Gaussian Graphical Regression Models with High Dimensional Responses and Covariates

Though Gaussian graphical models have been widely used in many scientifi...

Neural Graphical Models

Graphs are ubiquitous and are often used to understand the dynamics of a...

Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics

Gaussian graphical models are widely used to represent conditional depen...

Bayesian functional graphical models

We develop a Bayesian graphical modeling framework for functional data f...

The Minimax Learning Rate of Normal and Ising Undirected Graphical Models

Let G be an undirected graph with m edges and d vertices. We show that d...

A Bayesian Nonparametric model for textural pattern heterogeneity

Cancer radiomics is an emerging discipline promising to elucidate lesion...

Individualized Inference in Bayesian Quantile Directed Acyclic Graphical Models

We propose an approach termed "qDAGx" for Bayesian covariate-dependent q...