Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source.
Keywords:Bayesian methods; machine learning; spatial gene expression; spatial modeling; spatial transcriptomics; tumor microenvironment.