ArticleNature communications2020
Inferring spatial and signaling relationships between cells from single cell transcriptomic data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 269 papers.
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Who cites it
269 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- PlantCCC prioritizes context-specific candidate ligand-receptor communication patterns in plant spatial transcriptomics.Plant molecular biology · 2026Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.Nature genetics · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Plant Spatio-Temporal Integration Network (PSTN): a framework for dynamic single-cell and spatial transcriptomics in fungal-plant interactions.Bioinformatics (Oxford, England) · 2026Article
- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- Accurate prediction in reconstructed spatial transcriptomes does not ensure valid biological discovery.bioRxiv : the preprint server for biology · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Single-cell RNA sequencing profiles drug activity within spatially engineered 3D cultures.Nature communications · 2026Article
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- Zero-shot reconstruction of mutant spatial transcriptomes.Patterns (New York, N.Y.) · 2026Article
- ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
209 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell-cell communications are then obtained by "optimally transporting" signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene-gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell-cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues.
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