Year: 2026
Type: preprint
Abstract: Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.
Authors: Ihsan E. Buker, Yang Ni, Stephanie Hicks, Jian Kang, Satwik Acharyya
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Scalable Bayesian methods for big imaging data analysis, Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data, Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data (R01GM148974), DMS/NIGMS 2: Bayesian modeling of single cell spatial transcriptomics data to identify significant spatial, UAB Cardiovascular Disease Predoctoral Training Program in Biostatistics and Epidemiology, 3/3 Morehouse School of Medicine/Tuskegee University/University of Alabama at Birmingham O'Neal Comprehensive Cancer Center Partnership, Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples
OpenAlex ID: W7210275772