UMAP is a non-linear dimensionality reduction technique that has been compared to t-SNE for single-cell data analysis. This study evaluates UMAP's performance in high-dimensional cytometry and single-cell RNA sequencing (scRNAseq), highlighting its faster runtime, consistency, meaningful organization of cell clusters, and preservation of continuity compared to t-SNE. The study shows that UMAP is significantly faster than t-SNE, with UMAP taking about 5 minutes for 200,000 cells versus 2 hours and 22 minutes for t-SNE. UMAP also organizes clusters in a meaningful way, allowing for better visualization of cellular trajectories. For example, UMAP ordered T and NK cells from 8 human organs in a way that identified major cell lineages and a common axis of differentiation stages. Additionally, UMAP provided a more stable and consistent representation of data across different replicates and subsamples. In the analysis of hematopoietic development, UMAP revealed a five-leaf branched structure consistent with hematopoietic differentiation, including hematopoietic stem cells (HSC), multipotent progenitors (MPP), common lymphoid progenitors (CLP), common myeloid progenitors (CMP), and other cell populations. UMAP also identified pre-B cell markers and hypothesized new gene markers for pre-B cells in mouse bone marrow, which would have been more difficult to detect using t-SNE. The study concludes that UMAP provides a more meaningful representation of data, preserves global structure and continuity, and is more computationally efficient and reproducible than t-SNE. These advantages make UMAP a valuable tool for single-cell analysis.UMAP is a non-linear dimensionality reduction technique that has been compared to t-SNE for single-cell data analysis. This study evaluates UMAP's performance in high-dimensional cytometry and single-cell RNA sequencing (scRNAseq), highlighting its faster runtime, consistency, meaningful organization of cell clusters, and preservation of continuity compared to t-SNE. The study shows that UMAP is significantly faster than t-SNE, with UMAP taking about 5 minutes for 200,000 cells versus 2 hours and 22 minutes for t-SNE. UMAP also organizes clusters in a meaningful way, allowing for better visualization of cellular trajectories. For example, UMAP ordered T and NK cells from 8 human organs in a way that identified major cell lineages and a common axis of differentiation stages. Additionally, UMAP provided a more stable and consistent representation of data across different replicates and subsamples. In the analysis of hematopoietic development, UMAP revealed a five-leaf branched structure consistent with hematopoietic differentiation, including hematopoietic stem cells (HSC), multipotent progenitors (MPP), common lymphoid progenitors (CLP), common myeloid progenitors (CMP), and other cell populations. UMAP also identified pre-B cell markers and hypothesized new gene markers for pre-B cells in mouse bone marrow, which would have been more difficult to detect using t-SNE. The study concludes that UMAP provides a more meaningful representation of data, preserves global structure and continuity, and is more computationally efficient and reproducible than t-SNE. These advantages make UMAP a valuable tool for single-cell analysis.