Fig. 6

Each cell shows the silhouette coefficient (brighter colours indicate stronger detected clustering, with a threshold set at 0.5) as a function of within-feature effect size (Cohen’s d), and the proportion of features that differed between two simulated clusters with different covariance structures (3-factor and 4-factor). Each row presents a different dimensionality reduction approach: None, multi-dimensional scaling (MDS), or uniform manifold approximation and projection (UMAP). Each column presents a different type of clustering algorithm: k-means, agglomerative (hierarchical) clustering with Ward linkage and Euclidean distance, agglomerative clustering with average linkage and cosine distance, and HDBSCAN