Search and match across spatial omics samples at single-cell resolution.

ABSTRACT: Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and

SUBMITTER: Tang Z 

PROVIDER: S-EPMC11529703 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature