ArticleNature communications2024
Graph Fourier transform for spatial omics representation and analyses of complex organs.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.bioRxiv : the preprint server for biology · 2026Article
- Multi-scale spatial testing recovers gene programs missed by existing detection methods.bioRxiv : the preprint server for biology · 2026Article
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- A spectral dimension reduction technique that improves pattern detection in multivariate spatial data.Bioinformatics (Oxford, England) · 2026Article
- Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with SPLISOSM.Nature biotechnology · 2026Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression.Scientific reports · 2025Article
- Antibody-oligonucleotide conjugates for spatial proteomics: principles, applications, and challenges.Acta biochimica et biophysica Sinica · 2025Article
- Dynamic Synthesis of Multi-Modal Representations for CITE-seq Data Integration and Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.Genome biology · 2025Article
- Sex differences in bladder cancer: understanding biological and clinical implications.Biology of sex differences · 2025Review
- InSituCor: exploring spatially correlated genes conditional on the cell type landscape.Genome biology · 2025Article
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues.Cell genomics · 2025Article
- Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data.Nature communications · 2025Review
- Single-cell RNA-seq data augmentation using generative Fourier transformer.Communications biology · 2025Article
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16 authors.
Funding
Abstract
Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.
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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.