Extended Data Fig. 7: Temporal segmentation and changepoint prediction of simulated trajectories for DeepSPT and benchmark approaches.

a-e, Predicted changepoints (CP) versus true changepoints. Trajectories are constructed by combining two populations with overlapping diffusional properties into individual tracks with one changepoint (see Methods). Black line represents perfect classification. a, HMM-bayes¹⁷, 1556 data points compared to 5000 for other approaches due to computational time restraints as HMM-bayes requires several minutes per track. b, Rolling MSD. c, Original diffusional fingerprinting (Pinholt et al.¹³) d, Method E from the 2021 AnDi Challenge. e, DeepSPT. f, Table of classification metrics for the temporal segmentation of trajectory timepoints as post- and pre-uncoating and changepoint prediction. Median accuracy measures the median accuracy per trajectory (N=100). F1-score measures the F1-score of all individual timepoint predictions. Median frame error measures the median of absolute distances between predicted and true changepoints across all trajectories. Mean frame error measures the mean of absolute distances between predicted and true changepoints across all trajectories. Mean squared error measures the mean of the second norm distances between predicted and true changepoints across all trajectories. R2 measures the coefficient of determination.