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

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 introduced MarsGT: Multi-omics Analysis for Rare population infe...

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Detalles Bibliográficos
Autores principales: Wang, Xiaoying, Duan, Maoteng, Li, Jingxian, Ma, Anjun, Xu, Dong, Li, Zihai, Liu, Bingqiang, Ma, Qin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462017/
https://www.ncbi.nlm.nih.gov/pubmed/37645917
http://dx.doi.org/10.1101/2023.08.15.553454
Descripción
Sumario: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 introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.