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

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. Fina

SUBMITTER: Chen Z 

PROVIDER: S-EPMC9209427 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature