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In the era of single-cell sequencing, there is a growing need to extractinsights from data with clustering methods. Here, we introduce Forest FireClustering, an efficient and interpretable method for cell-type discovery fromsingle-cell data. Forest Fire Clustering makes minimal prior assumptions and,different from current approaches, calculates a non-parametric posteriorprobability that each cell is assigned a cell-type label. These posteriordistributions allow for the evaluation of a label confidence for each cell andenable the computation of "label entropies," highlighting transitions alongdevelopmental trajectories. Furthermore, we show that Forest Fire Clusteringcan make robust, inductive inferences in an online-learning context and canreadily scale to millions of cells. Finally, we demonstrate that our methodoutperforms state-of-the-art clustering approaches on diverse benchmarks ofsimulated and experimental data. Overall, Forest Fire Clustering is a usefultool for rare cell type discovery in large-scale single-cell analysis.

Forest Fire Clustering for Single-cell Sequencing with Iterative Label Propagation and Parallelized Monte Carlo Simulation