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

Zhanlin Chen, Jeremy Goldwasser, Philip Tuckman, Jason Liu, Jing Zhang () and Mark Gerstein ()
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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

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31107-8

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