ArticleNature methods2025
Mapping the topography of spatial gene expression with interpretable deep learning.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Accurate and well-powered case-control analysis of spatial molecular data.Nature methods · 2026Article
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.Briefings in bioinformatics · 2026Article
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- Investigating Spatial Dynamics in Spatial Omics Data with StarTrail.Journal of the American Statistical Association · 2026Article
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- SMURF: soft-segmentation for single-cell reconstruction and topological analysis of spatial transcriptomic data.Nature communications · 2026Article
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- Tissueformer: extending single-cell foundation models to predict population-level phenotypes.BMC bioinformatics · 2026Article
- Deep Learning and Microbiome Analysis Reveal the Preservation Mechanism ofFoods (Basel, Switzerland) · 2026Article
- Decoding cellular population dynamics through mechanistic modelling and statistical data analysis.NPJ systems biology and applications · 2026Review
- Logistic regression for estimating functional effects with spatial transcriptomics.Nucleic acids research · 2026Article
- Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro.bioRxiv : the preprint server for biology · 2026Article
- A transcriptomic axis aligns with in vivo functional dynamics in hippocampal inhibitory circuits.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- MINGL Quantifies Borders, Gradients, and Heterogeneity in Multicellular Tissue Organization.bioRxiv : the preprint server for biology · 2026Article
- SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- Human Neocortical Glutamatergic Neurons Revealed Through Multimodal Profiling.bioRxiv : the preprint server for biology · 2026Article
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Abstract
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.