Before revision:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
After revision:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or UMAP ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and cosine distance, HDBSCAN).