ArticleBriefings in bioinformatics2023
A universal framework for single-cell multi-omics data integration with graph convolutional networks.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 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
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- Benchmarking single-cell multi-modal data integrations.Nature methods · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- sCIN: a contrastive learning framework for single-cell multi-omics data integration.Briefings in bioinformatics · 2025Article
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis.iScience · 2025Article
- Applications and advances of multi-omics technologies in gastrointestinal tumors.Frontiers in medicine · 2025Review
- Decoding sex differences in human immunity through systems immunology.Oxford open immunology · 2025Review
- MosGraphFlow: a novel integrative graph AI model mining signaling targets from multi-omic data.BMC methods · 2025Article
- M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.bioRxiv : the preprint server for biology · 2024Article
- mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.bioRxiv : the preprint server for biology · 2024Article
- Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.NPJ systems biology and applications · 2024Article
- scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data.Genome biology · 2024Article
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Mapping Cell Atlases at the Single-Cell Level.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single-cell omics data are growing at an unprecedented rate, whereas effective integration of them remains challenging due to different sequencing methods, quality, and expression pattern of each omics data. In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.
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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.