Extended Data Fig. 5: Comparison of Multiscale PHATE with other clustering techniques on hierarchical stochastic block model.

a, Computed Adjusted Rand Index (ARI) between each algorithm’s predicted clusters and the known clusters on synthetic single-cell data generated by splatter (Zappia et al.) across a range of noise types, dropout and biological variation, and noise levels. Shading represents one standard deviation around mean ARI score for each comparison. b, Schematic of the hierarchical stochastic block model we generated for multigranular cluster comparisons. For each method, increasing amounts of random Gaussian noise were added to the adjacency matrix of stochastic block model to simulate increasing amounts of noise. While adding noise directly to data introduces simple linear noise, adding Gaussian noise to the edge weights of an adjacency matrix simulates more complex non-linear type of noise which is often present in high-dimensional biological data. c, Computed Adjusted Rand Index (ARI) between each algorithm’s predicted clusters and the known clusters across coarse and fine granularities of 2 layer stochastic block model perturbed with increasing amounts of noise. Shading represents one standard deviation around mean ARI score for each comparison. d, Computed Adjusted Rand Index (ARI) between each algorithm’s predicted clusters and the known clusters across coarse, intermediate and fine granularities of 3 layer stochastic block model perturbed with increasing amounts of noise. Shading represents one standard deviation around mean ARI score for each comparison.