ArticleNature communications2024
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.
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 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Specificity-driven graph learning for the identification of rare cell states.Advanced biotechnology · 2026Article
- Geneformer-guided multiomics integration identifies Pbx1 as a network hub of hematopoietic stem cell aging.Science advances · 2026Article
- Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer.Nature communications · 2026Article
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- Multimodal foundation transformer models for multiscale genomics.Nature methods · 2026Review
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- SLECA: A Single-Cell Atlas of Systemic Lupus Erythematosus Enabling Rare-Cell Discovery Using Graph Transformer.Computational and structural biotechnology journal · 2026Article
- The applications of single-cell multiomics in drug screening.Pharmaceutical science advances · 2025Review
- Perspectives on integrating artificial intelligence and single-cell omics for cellular plasticity research.Quantitative biology (Beijing, China) · 2025Article
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Dynamic Synthesis of Multi-Modal Representations for CITE-seq Data Integration and Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation.bioRxiv : the preprint server for biology · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
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9 authors.
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Abstract
Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.
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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.