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Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations

Author

Listed:
  • Zhanlin Chen

    (Yale University)

  • Jeremy Goldwasser

    (Yale University)

  • Philip Tuckman

    (Massachusetts Institute of Technology)

  • Jason Liu

    (Yale University)

  • Jing Zhang

    (University of California)

  • Mark Gerstein

    (Yale University
    Yale University
    Yale University)

Abstract

In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Forest Fire Clustering makes minimal prior assumptions and, different from current approaches, calculates a non-parametric posterior probability that each cell is assigned a cell-type label. These posterior distributions allow for the evaluation of a label confidence for each cell and enable the computation of “label entropies", highlighting transitions along developmental trajectories. Furthermore, we show that Forest Fire Clustering can make robust, inductive inferences in an online-learning context and can readily scale to millions of cells. Finally, we demonstrate that our method outperforms state-of-the-art clustering approaches on diverse benchmarks of simulated and experimental data. Overall, Forest Fire Clustering is a useful tool for rare cell type discovery in large-scale single-cell analysis.

Suggested Citation

  • Zhanlin Chen & Jeremy Goldwasser & Philip Tuckman & Jason Liu & Jing Zhang & Mark Gerstein, 2022. "Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31107-8
    DOI: 10.1038/s41467-022-31107-8
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    References listed on IDEAS

    as
    1. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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