MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer.
Xiaoying WangMaoteng DuanJingxian LiAnjun MaGang XinDong XuZihai LiBingqiang LiuQin MaPublished in: Nature communications (2024)
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.
Keyphrases
- single cell
- rna seq
- high throughput
- endothelial cells
- lymph node
- electronic health record
- induced apoptosis
- big data
- induced pluripotent stem cells
- cell therapy
- pluripotent stem cells
- stem cells
- immune response
- convolutional neural network
- oxidative stress
- mesenchymal stem cells
- bipolar disorder
- artificial intelligence
- climate change
- squamous cell carcinoma
- endoplasmic reticulum stress
- early stage
- neoadjuvant chemotherapy
- human health
- cell death
- deep learning
- optic nerve