SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data
Yuelei Zhang, Bianjiong Yu, Wenxuan Ming, Xiaolong Zhou, Jin Wang, Dijun Chen
Abstract
Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.