ArticleNature communications2022
SOTIP is a versatile method for microenvironment modeling with spatial omics data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- Combining Spatial Multi-Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self-Supervised Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- The automated computational workflow QUICHE reveals structural definitions of antitumor responses in triple-negative breast cancer.Nature cancer · 2026Article
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data.Genome biology · 2026Article
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
- MMSpa is a deep learning-based tool that enhances the identification of spatial domains in spatial transcriptomics studies.PLoS biology · 2026Article
- Spatial mutual nearest neighbors for spatial transcriptomics data.Bioinformatics (Oxford, England) · 2025Article
- SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data.Cell genomics · 2025Article
- MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility data.Genome biology · 2025Article
- SOAPy: a Python package to dissect spatial architecture, dynamics, and communication.Genome biology · 2025Article
- From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling.iScience · 2024Review
- Current opinions on large cellular models.Quantitative biology (Beijing, China) · 2024Article
- Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.Genome biology · 2024Article
- GraphCompass: spatial metrics for differential analyses of cell organization across conditions.Bioinformatics (Oxford, England) · 2024Article
- Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning.Nature communications · 2024Article
- Complete spatially resolved gene expression is not necessary for identifying spatial domains.Cell genomics · 2024Article
- HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.BMC genomics · 2024Article
- A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.Briefings in bioinformatics · 2024Article
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7 authors.
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Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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