Supervised Factorization to Associate Spatial Transcriptomics with Complementary Molecular Readouts

Abstract

Spatial Transcriptomics enables studying gene expression data within spatial context of tissues. Yet understanding how spatial molecular phenomena influence transcriptional patterns remains a key challenge. We propose a novel supervised Non-negative Matrix Factorization (NMF) framework, where supervision is selectively and explicitly applied to guide the learning of a supervised spatial factor. This distinguishes our method from prior approaches by enforcing spatial alignment only on a targeted component of the factorization, enabling biologically interpretable associations between gene expression and spatial molecular events. This approach also enables the identification of genes whose expression patterns are spatially correlated with molecular events of interest. Applied to datasets involving Alzheimers Disease (AD) and Myocardial Infarction (MI), our method successfully discovered supervised spatial factor associated with disease related signal. In the case of Alzheimers Disease (AD), we have presented a spatial decay model to represent how the influence of amyloid-beta plaque signals diminishes with distance, and used this as a supervision signal during matrix factorization. Applied across both disease contexts, our method successfully identified biologically meaningful gene sets associated with disease progression. By ranking genes based on their contribution to the supervised spatial factor, the framework highlights candidate genes potentially involved in disease-related processes.