Spatial integration of multi-omics single-cell data with SIMO.

ABSTRACT: Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates spatial transcriptomics with single-cell RNA-seq but expands beyond, enabling integration across multiple single-cell modalities, such as chromatin accessibility and DNA methylation, which have not been co-profiled spatially before. We benchmark SIMO on simulated datasets, demonstrating its high accuracy and robustness. Further application on biological datasets reveals SIMO's ability to detect topological patterns of cells and their regulat

SUBMITTER: Yang P 

PROVIDER: S-EPMC11787318 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature