STForte: tissue context-specific encoding and consistency-aware spatial imputation for spatially resolved transcriptomics.

ABSTRACT: Encoding spatially resolved transcriptomics (SRT) data serves to identify the biological semantics of RNA expression within the tissue while preserving spatial characteristics. Depending on the analytical scenario, one may focus on different contextual structures of tissues. For instance, anatomical regions reveal consistent patterns by focusing on spatial homogeneity, while elucidating complex tumor micro-environments requires more expression heterogeneity. However, current spatial encoding methods lack consideration of the tissue context. Meanwhile, most developed SRT technologies are still limited in providing exact patterns of intact tissues due to limitations such as low resolution or missed measurements. Here, we propose STForte, a novel pairwise graph autoencoder-based approach with

SUBMITTER: Pang Y 

PROVIDER: S-EPMC12009714 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature