ArticleBriefings in bioinformatics2024
Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- scCCVGBen for benchmarking of single-cell representation learning anchored on a centroid-coupled variational graph attention autoencoder across scRNA-seq and scATAC-seq.Frontiers in genetics · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- MOMHCA-SG: a multi-head cross-attention and similar graph convolutional network framework for Alzheimer's disease cell type classification.Frontiers in neuroscience · 2026Article
- The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment.Research (Washington, D.C.) · 2026Review
- Molecular mechanisms underlying T cell subset imbalance and precision therapeutic targets in primary biliary cholangitis.Frontiers in pharmacology · 2026Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.Briefings in bioinformatics · 2025Article
- spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation.Genome biology · 2025Article
- Spatial Transcriptomics in Lung Cancer and Pulmonary Diseases: A Comprehensive Review.Cancers · 2025Review
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Article
- Natural products targeting the immune-metabolic regulatory network in pulmonary hypertension: mechanisms, classification, and therapeutic prospects.Frontiers in pharmacology · 2025Review
- Applications and advances of multi-omics technologies in gastrointestinal tumors.Frontiers in medicine · 2025Review
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.
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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.