A0278
Title: Decoding spatial tissue architecture: A scalable Bayesian topic model for multiplexed imaging analysis
Authors: Xiyu Peng - Texas A&M University (United States) [presenting]
Abstract: Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance the understanding of treatment response and disease progression. Despite its popularity, there are significant challenges in data analysis, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. To overcome these challenges, a spatial topic model is introduced, designed to decode high-level spatial architecture across multiplexed tissue images. The method integrates both cell type and spatial information within a topic modeling framework, originally developed for natural language processing and adapted for computer vision. Its performance is benchmarked through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. It is shown that the method runs significantly faster on large-scale image datasets, along with high precision and interpretability. It consistently identifies biologically and clinically significant spatial topics, such as tertiary lymphoid structures.
