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Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Author

Listed:
  • Zhuohan Yu

    (Jilin University)

  • Yanchi Su

    (Jilin University)

  • Yifu Lu

    (Jilin University)

  • Yuning Yang

    (University of Toronto)

  • Fuzhou Wang

    (City University of Hong Kong)

  • Shixiong Zhang

    (City University of Hong Kong)

  • Yi Chang

    (Jilin University)

  • Ka-Chun Wong

    (City University of Hong Kong)

  • Xiangtao Li

    (Jilin University)

Abstract

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Suggested Citation

  • Zhuohan Yu & Yanchi Su & Yifu Lu & Yuning Yang & Fuzhou Wang & Shixiong Zhang & Yi Chang & Ka-Chun Wong & Xiangtao Li, 2023. "Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36134-7
    DOI: 10.1038/s41467-023-36134-7
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    References listed on IDEAS

    as
    1. Juexin Wang & Anjun Ma & Yuzhou Chang & Jianting Gong & Yuexu Jiang & Ren Qi & Cankun Wang & Hongjun Fu & Qin Ma & Dong Xu, 2021. "scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Xiangjie Li & Kui Wang & Yafei Lyu & Huize Pan & Jingxiao Zhang & Dwight Stambolian & Katalin Susztak & Muredach P. Reilly & Gang Hu & Mingyao Li, 2020. "Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Sara Mostafavi & Anna Goldenberg & Quaid Morris, 2012. "Labeling Nodes Using Three Degrees of Propagation," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-10, December.
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    5. Duc Tran & Hung Nguyen & Bang Tran & Carlo La Vecchia & Hung N. Luu & Tin Nguyen, 2021. "Fast and precise single-cell data analysis using a hierarchical autoencoder," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    6. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    7. Suoqin Jin & Christian F. Guerrero-Juarez & Lihua Zhang & Ivan Chang & Raul Ramos & Chen-Hsiang Kuan & Peggy Myung & Maksim V. Plikus & Qing Nie, 2021. "Inference and analysis of cell-cell communication using CellChat," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
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