Explainable Spatial Representation Learning for Spatial Transcriptomics Data.

Thomas M. Menino Convention & Exhibition Center 

Spatial transcriptomics generates high-dimensional, spatially indexed gene expression data with complex dependence structures and measurement noise, making low-dimensional spatial representation learning a critical step for characterizing tissue organization and spatial heterogeneity. While recent advances have largely relied on auto-encoder-based 'black-box' approaches to learn spatial embeddings, these methods often lack interpretability, biological traceability, and principled uncertainty quantification. From a statistical perspective, Bayesian spatial factor models provide a transparent and generative alternative, but their practical use has been limited by identifiability issues, unstable posterior inference, and scalability challenges. This talk presents an explainable spatial representation learning framework based on Bayesian spatial factor modeling that addresses these limitations. A projected MCMC sampling strategy is introduced, leveraging conditional conjugacy and projection to constrain latent factor sampling to a scaled Stiefel manifold, substantially improving posterior stability, mixing efficiency, and robustness to initialization. Applications to large-scale human kidney spatial transcriptomics data demonstrate that the proposed approach yields spatially smooth, interpretable embeddings, biologically meaningful gene-level spatial effects, and uncertainty-aware inference for spatial molecular analysis.