The study evaluates the Uniform Manifold Approximation and Projection (UMAP) algorithm as an alternative to t-SNE for single-cell data analysis. UMAP is a recently published non-linear dimensionality reduction technique that aims to preserve both local and global data structures more effectively than t-SNE. The authors compare UMAP and t-SNE on well-characterized single-cell datasets, highlighting several advantages of UMAP: 1. **Faster Runtime**: UMAP is significantly faster, completing the analysis in 5 minutes on average for 200,000 cells compared to 2 hours and 22 minutes for t-SNE. 2. **Consistency and Stability**: UMAP is more consistent across different replicates and subsamples, making it easier to interpret results. 3. **Meaningful Clustering**: UMAP creates informative clusters and organizes them in a meaningful way, preserving the continuity of cell subsets. 4. **Continuity in Cellular Trajectories**: UMAP better represents the multi-branched trajectories of cellular development, such as hematopoiesis, by maintaining a clear structure along a common axis. The study uses datasets from human tissues enriched for T and NK cells, as well as datasets from bone marrow hematopoiesis using mass-cytometry and single-cell RNA sequencing. UMAP is shown to consistently identify major cell lineages and differentiate stages, while t-SNE often separates cell populations into distinct clusters that may not accurately reflect their relationships. Overall, the authors conclude that UMAP is a valuable tool for single-cell analysis, offering improved performance and interpretability compared to t-SNE.The study evaluates the Uniform Manifold Approximation and Projection (UMAP) algorithm as an alternative to t-SNE for single-cell data analysis. UMAP is a recently published non-linear dimensionality reduction technique that aims to preserve both local and global data structures more effectively than t-SNE. The authors compare UMAP and t-SNE on well-characterized single-cell datasets, highlighting several advantages of UMAP: 1. **Faster Runtime**: UMAP is significantly faster, completing the analysis in 5 minutes on average for 200,000 cells compared to 2 hours and 22 minutes for t-SNE. 2. **Consistency and Stability**: UMAP is more consistent across different replicates and subsamples, making it easier to interpret results. 3. **Meaningful Clustering**: UMAP creates informative clusters and organizes them in a meaningful way, preserving the continuity of cell subsets. 4. **Continuity in Cellular Trajectories**: UMAP better represents the multi-branched trajectories of cellular development, such as hematopoiesis, by maintaining a clear structure along a common axis. The study uses datasets from human tissues enriched for T and NK cells, as well as datasets from bone marrow hematopoiesis using mass-cytometry and single-cell RNA sequencing. UMAP is shown to consistently identify major cell lineages and differentiate stages, while t-SNE often separates cell populations into distinct clusters that may not accurately reflect their relationships. Overall, the authors conclude that UMAP is a valuable tool for single-cell analysis, offering improved performance and interpretability compared to t-SNE.