Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks
Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks
ABSTRACT Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. …