ArticleNature communications2024
MENDER: fast and scalable tissue structure identification in spatial omics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 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
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- Decoding Spatial Heterogeneity and Multi-Omics Regulation with Hierarchical 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
- SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data.Genome biology · 2026Article
- SCALE: unsupervised multiscale domain identification in spatial omics data.Nucleic acids research · 2026Article
- Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.PLoS computational biology · 2025Article
- Bridging single cells to organs: Mesoscale modules as fundamental units of tissue function.Cell · 2025Review
- SpaCross deciphers spatial structures and corrects batch effects in multi-slice spatially resolved transcriptomics.Communications biology · 2025Article
- Benchmarking multi-slice integration and downstream applications in spatial transcriptomics data analysis.Genome biology · 2025Article
- Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
- Spatial mutual nearest neighbors for spatial transcriptomics data.Bioinformatics (Oxford, England) · 2025Article
- TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.Briefings in bioinformatics · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- Quantitative characterization of cell niches in spatially resolved omics data.Nature genetics · 2025Article
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Abstract
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
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