ArticleNature communications2023
NeST: nested hierarchical structure identification in spatial transcriptomic data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 14 citations in OpenAlex.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Pan-tumor spatial transcriptomics reveals conserved properties of tumor organization.Cell reports. Medicine · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Multiscale domain identification for spatial transcriptomics via persistent homology.Cell reports methods · 2026Article
- HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps.Nature communications · 2026Article
- SCALE: unsupervised multiscale domain identification in spatial omics data.Nucleic acids research · 2026Article
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- PHD-MS: Multiscale Domain Identification for Spatial Transcriptomics via Persistent Homology.ArXiv · 2025Article
- Epitope-imprinted biomaterials with tailor-made molecular targeting for biomedical applications.Bioactive materials · 2025Review
- Detecting global and local hierarchical structures in cell-cell communication using CrossChat.Nature communications · 2024Article
- SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer.Genome biology · 2024Article
- Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics.Communications biology · 2024Article
- SANTO: a coarse-to-fine alignment and stitching method for spatial omics.Nature communications · 2024Article
- MENDER: fast and scalable tissue structure identification in spatial omics data.Nature communications · 2024Article
- NeST: nested hierarchical structure identification in spatial transcriptomic data.Nature communications · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
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