Spatial transcriptomics have enabled gene expression profiling on tissues with spatial localization information, characterizing the transcriptomic landscape of many tissues. Here, we present a statistical method, BASS, for effective spatial transcriptomic analysis that examines the hierarchical organization of tissues at two distinct scales. Specifically, at the single-cell scale, our method performs cell type clustering and clusters cells into cell types. At the tissue regional scale, our method segments the tissue section into distinct spatial domains in a de novo fashion. Importantly, our method performs both analyses in a coherent fashion through a Bayesian hierarchical modeling framework, allowing for seamless integration of gene expression information with spatial information to improve the analyses at both scales. Moreover, our method allows for integrative analysis of spatial transcriptomic data measured on multiple tissue sections in the same anatomic region, allowing us to borrow critical biological information across tissue sections to further enhance analytic performance.

Tool Link: https://zhengli09.github.io/BASS-Analysis/

This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.