A0533
Title: Network models for spatial transcriptomics data
Authors: Satwik Acharyya - University of Alabama at Birmingham (United States) [presenting]
Abstract: Network models are powerful tools to investigate complex dependence structures in high-throughput genomic datasets. They allow for a holistic, systems-level view of the various biological processes, for intuitive understanding and coherent interpretations. However, most existing network or graphical models are developed under assumptions of homogeneity of samples and are not readily amenable to modeling spatial heterogeneity, which often manifests in spatial genomics data. Two spatial network models are discussed, focusing on spatially varying covariance and precision matrices. (I) SpaceX (spatially dependent gene co-expression network) is a Bayesian methodology to identify both shared and cluster-specific co-expression networks across genes. (II) Spatial Graphical Regression (SGR) is a flexible approach based on graphical regression that enables spatially varying graphs over the spatial domain of the tissue. The framework incorporates multiple spatial covariates and provides a linear and non-linear functional mapping between the spatial domain and the precision matrices. All the approaches are illustrated by using case studies from cancer genomics.
