ArticleGenome biology2022
De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Empowering multifaceted analysis of spatial transcriptomics data with RGAST.Briefings in bioinformatics · 2026Article
- Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.Bioinformatics (Oxford, England) · 2026Article
- Sparse dimensionality reduction for analyzing single-cell-resolved interactions.Bioinformatics advances · 2026Article
- Spatial omics of neuroinflammation: insights across brain diseases.Frontiers in immunology · 2026Review
- CauSTI: causal spatial transcriptomic interaction inference for cell-cell communication.Frontiers in cell and developmental biology · 2026Article
- DeepPNCC: reconstructing pseudo-spatial cell-cell interaction landscapes from single-cell data to decipher breast cancer pathogenesis.Journal of translational medicine · 2025Article
- MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics.PLoS computational biology · 2025Article
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- OrgaCCC: Orthogonal graph autoencoders for constructing cell-cell communication networks on spatial transcriptomics data.PLoS computational biology · 2025Article
- Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data.Clinical and translational medicine · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Quantitative characterization of cell niches in spatially resolved omics data.Nature genetics · 2025Article
- Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments.NPJ precision oncology · 2025Review
- Model Architecture Analysis and Implementation of TENET for Cell-Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data.Bio-protocol · 2025Article
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2 authors.
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Abstract
Based on a deep generative model of variational graph autoencoder (VGAE), we develop a new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for the de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data. DeepLinc demonstrates high efficiency in learning from imperfect and incomplete spatial transcriptome data, filtering false interactions, and imputing missing distal and proximal interactions. The latent representations learned by DeepLinc are also used for inferring the signature genes contributing to the cell interaction landscapes, and for reclustering the cells based on the spatially coded cell heterogeneity in complex tissues at single-cell resolution.
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