Clustering Spatial Transcriptomics Data with Dirichlet Process Mixture of Random Spanning Trees

Music City Center 

Spatial transcriptomics has gained tremendous popularity as it allows researchers to map gene expression directly onto tissue architecture, preserving spatial context and providing high-resolution insights into cellular interactions and biological processes within their native environments. In talk, we introduce a novel Bayesian nonparametric framework, Dirichlet process mixture of random spanning trees (DP-RST), designed to detect an unknown number of non-convex clusters in complex spatial domains. The model's two-layer partitioning effectively addresses challenges posed by the intricate spatial organization of tissue samples, such as non-convex clusters and irregular spatial boundaries of the samples itself. We apply DP-RST to a mouse colonic dataset during healing from inflammatory damage, revealing meaningful clusters associated with different stages of tissue repair. Differential gene expression analysis highlights key genes with spatially distinct patterns, revealing the compartmentalization of immune, metabolic, and regenerative processes during mucosal healing.

Bayesian nonparametrics

Genomics

Non-convex clusters

Non-convex spatial domain

Random partition

Swiss-roll