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GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles

Author

Listed:
  • Botao Fa

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Ting Wei

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yuan Zhou

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Luke Johnston

    (Shanghai Jiao Tong University)

  • Xin Yuan

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yanran Ma

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yue Zhang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Zhangsheng Yu

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University School of Medicine)

Abstract

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

Suggested Citation

  • Botao Fa & Ting Wei & Yuan Zhou & Luke Johnston & Xin Yuan & Yanran Ma & Yue Zhang & Zhangsheng Yu, 2021. "GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24489-8
    DOI: 10.1038/s41467-021-24489-8
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    Cited by:

    1. Yunpei Xu & Shaokai Wang & Qilong Feng & Jiazhi Xia & Yaohang Li & Hong-Dong Li & Jianxin Wang, 2024. "scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Xiaoying Wang & Maoteng Duan & Jingxian Li & Anjun Ma & Gang Xin & Dong Xu & Zihai Li & Bingqiang Liu & Qin Ma, 2024. "MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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