Fig. 5

Robustness of Slingshot pseudotimes to clustering method: Simulated two-lineage datasets. Gaussian kernel density plots of accuracy scores for different clustering methods (columns) and numbers of clusters (rows) based on simulated data with two lineages. Clustering was performed using hierarchical clustering, k-means, and Gaussian mixture modeling, with a range of values for the number of clusters, K. Principal component analysis was used for dimensionality reduction with two values for the number of components J^′: in Panel (a), three-dimensional PCA produced highly variable scores, while in Panel (b), four-dimensional PCA produced consistently high scores. Both panels show that Slingshot produces similar distributions of accuracy scores over a range of values for K. However, when K=3, Slingshot is often unable to detect the branching event and the resulting pseudotimes imperfectly match either true lineage. With more clusters, we see consistently accurate results. At higher values of K (not shown), accuracy scores begin to degrade slowly, as Slingshot begins to overfit and identify more spurious branching events. See “Simulation study” section for the definition of accuracy scores based on Kendall’s rank correlation coefficient and Additional file 1 for details on simulation scenarios