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, inspired by forest fire dynamics, the authors devise an algorithm that can cluster single-cell data with minimal prior assumptions and can compute a non-parametric posterior probability for each data point.