ArticleGenome medicine2024
Unsupervised spatially embedded deep representation of spatial transcriptomics.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 257 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
257 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 epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks.Nature communications · 2026Article
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- SpaHDSRL: hierarchical dual-graph self-supervised representation learning for integrating spatially resolved multi-omics data.Briefings in bioinformatics · 2026Article
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- A multiperspective evaluation framework of spatial transcriptomics clustering methods.NAR genomics and bioinformatics · 2026Article
- Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.Nature methods · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- STWave: Fine-Scale Spatial Structure Discovery in Microscopic-Resolution Spatial Transcriptomics via Patchwise Wavelet Graphs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Toward A Pre-disease State-centered New Paradigm in Multi-omics Research.Genomics, proteomics & bioinformatics · 2026Article
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Deciphering spatial heterogeneity by multimodal spatial transcriptomics modelling with SpatialModal.Bioinformatics (Oxford, England) · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- SPIDER: spatially integrated denoising via embedding regularization with single cell supervision.Bioinformatics (Oxford, England) · 2026Article
- Tissue and cellular spatiotemporal dynamics in colon aging.Nature biotechnology · 2026Article
- Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics.Small methods · 2026Article
- Decoding Spatial Heterogeneity and Multi-Omics Regulation with Hierarchical Graph Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
197 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).
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Registered trials
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.