ArticleNature methods2024
Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
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- scENature communications · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Cancer-associated fibroblasts and tertiary lymphoid structure orchestrate the spatial architecture of tumor immunity.Journal of hematology & oncology · 2026Review
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Deciphering microenvironmental heterogeneity by scalable Niche Guided Module Discovery.Communications biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- LDHA-driven lactate metabolism promotes MDSC activation and immunosuppressive microenvironment in prostate cancer.Oncogene · 2026Article
- Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.Interdisciplinary sciences, computational life sciences · 2026Article
- Identification of a new population of TnnBone research · 2026Article
- Smoothie: efficient inference and integration of spatial co-expression networks from denoised spatial transcriptomics data.Communications 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
- The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.Nature cell biology · 2026Article
- Article
- Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.Bioinformatics advances · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
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3 authors.
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Abstract
Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge's exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells.
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