StClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs.

ABSTRACT: Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time

SUBMITTER: Zuo C 

PROVIDER: S-EPMC12170857 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature