MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Xiaoying Wang, Maoteng Duan, Jingxian Li, Anjun Ma, Gang Xin, Dong Xu, Zihai Li, Bingqiang Liu () and Qin Ma ()
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Xiaoying Wang: Shandong University
Maoteng Duan: Shandong University
Jingxian Li: Shandong University
Anjun Ma: College of Medicine, The Ohio State University
Gang Xin: The James Comprehensive Cancer Center, The Ohio State University
Dong Xu: University of Missouri
Zihai Li: The James Comprehensive Cancer Center, The Ohio State University
Bingqiang Liu: Shandong University
Qin Ma: College of Medicine, The Ohio State University

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-023-44570-8

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