ArticleNature communications2025
SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- KSTITCH links cellular morphology and gene expression in spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Transcriptomics Reframe the Immunosuppressive Microenvironment of Neuroendocrine Neoplasms.Cancers · 2026Review
- Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Graph convolutional networks for inferring cell-cell communication from spatial transcriptomics data.Bioinformatics advances · 2026Article
- Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.Iranian journal of basic medical sciences · 2026Review
- VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis.Research square · 2025Article
- Advancing biological understanding of cellular senescence with computational multiomics.Nature genetics · 2025Review
- Scaling deep identifiable models enables zero-shot characterization of single-cell biological states.bioRxiv : the preprint server for biology · 2024Article
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5 authors.
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Abstract
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.
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