Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.

ABSTRACT: Spatial gene expression patterns underlie tissue organization, development, and disease, yet current methods for detecting spatially variable genes (SVGs) lack the flexibility to capture multi-scale structure, ensure robustness across platforms, and integrate with genetic data to assess disease relevance. We present Spacelink, a unified framework that models spatial variability of a gene at both whole-tissue and cell-type resolution using an adaptive mixture of data-driven spatial kernels and summarizes it using an Effective Spatial Variability (ESV) metric. Spacelink achieved up to 3.2x higher detection power over eight existing global SVG and cell-type SVG methods while showing consistently superior FDR control across 34 different simulation settings and also showed superior cross-platfo

SUBMITTER: Lee H 

PROVIDER: S-EPMC12485882 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature