15: SCG: Spatially Co-expressed Gene identification through spatially varying networks

Thomas M. Menino Convention & Exhibition Center 

Spatial transcriptomics provides high-resolution gene expression maps across tissues. These biological advances necessitate statistical frameworks for characterizing spatially heterogeneous gene co-expression patterns. We introduce the novel concept of Spatially Co-expressed Genes (SCGs), which provide a spatial map of gene co-expression and enable the study of spot-specific gene networks, offering insights into disease etiology. To identify SCGs, we propose a spatial covariance regression (SCR) model that learns spatially varying marginal dependence while exploiting low-rank structure. SCR models location-specific correlation matrices using a spatially varying factor model with loadings decomposed into global loadings and spatial basis functions. We impose a multiplicative gamma shrinkage prior on the global loadings and Gaussian process on the spatial bases. SCGs are identified via Otsu's thresholding applied to spatial variation of estimated edges. We develop a GPU-accelerated variational Bayes algorithm to ensure scalability and assess the performance through comprehensive simulation study. We apply SCR on Xenium Alzheimer's mouse brain data to discover spatial biomarkers.

Spatial transcriptomics

Spatial covariance modeling

Gene co-expression networks

Factor models

Gaussian processes

Variational Bayes inference 

Section on Statistics in Genomics and Genetics