56 GraphR: a probabilistic modeling framework for genomic networks incorporating sample heterogeneity.

Oregon Convention Center 

Probabilistic graphical models are powerful tools to infer, interpret, and visualize complex biological systems. However, most existing graphical models assume homogeneity across samples, limiting their application in heterogeneous contexts e.g. tumor and spatial heterogeneity. We propose a general and flexible Bayesian approach called Graphical Regression (GraphR) which incorporates intrinsic heterogeneity at different scales such as discrete, continuous and spatial, enables sparse network estimation at sample-specific level, has higher precision compared to existing approaches and is computationally efficient for analyses of large genomic datasets. We employ GraphR to analyze four diverse multiomic and spatial transcriptomics datasets to infer inter- and intra-sample genomic networks and delineate several novel biological discoveries. We have developed the GraphR R-package and a user-friendly Shiny App for analysis and dynamic network visualization.

Heterogeneous graphical models

Genomics

Spatial transcriptomics

Variable selection

Variational Bayes 

Section on Statistics in Genomics and Genetics