Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations
Year: 2022
Type: article
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.
Source: Nature Communications
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,890
Awards: A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders, The Y-SCORCH Data Generation Center at Yale for Single-Cell Opioid Responses in the Context of HIV, M-SCORCH: Methamphetamine use disorder data generation center for Single Cell Opioid Responses in the Context of HIV
OpenAlex ID: W4283171432