SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data.

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

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11430467 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature