To assess the robustness of the clusters, we trained a random forest classifier to recognize cluster labels and then assessed its performance on held-out data (80% training set, 20% test set). The average precision and recall were both 82%, indicating a high level of robustness, particularly considering the large total number of clusters (Figures S1C–S1E).
