Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.
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We thank members of the Singapore Immunology Network and notably members of the E.W.N. laboratory. We thank S. Li, Y. Simoni, M. Chng, Y. Cheng, J.W. Lim and M. Fehlings for their insightful feedback. This study was funded by A-STAR/SIgN core funding and A-STAR/SIgN immunomonitoring platform funding.
E.W.N. is a board director and shareholder of immunoSCAPE Pte. Ltd., which is an immune profiling service provider.
Integrated supplementary information
a) Phenotypic characterization of the phenograph clusters. Each cluster medoid is represented after column-wise Z-score transformation. b) Identification of each phenograph cluster of both UMAP (left), t-SNE (middle) and 2D PCA (right). For clarity, only twelve clusters are shown per plot.
Scatterplot of embeddings of the Wong dataset using UMAP (top), t-SNE (middle) and 2D PCA (bottom) color-coded by tissues of origin.
Expression of Ter119 (a marker for mature erythrocytes) color-coded on the UMAP embedding of the Samusik_01 dataset.
Heatmap of the density of a 300x300 square grid of the UMAP or t-SNE projections for the Samusik_01 dataset. The number of events in each bin is color-coded.
Top: UMAP projection of the full Han dataset annotated by AUC scores for various cell lineages (red: high score, blue: low score). Bottom: full Han dataset colored by sample type, Sample ID and pre-filtering status.
Supplementary Figure 6 Side-by-side comparison of each dimensionality reduction method across all datasets annotated by cell types.
Scatterplots of six dimensionality-reduction methods and 6 datasets. Cell populations are annotated using manual gating (Samusik dataset), manually-labelled Phenograph clusters (Wong dataset) or sample of origin (Han_400k dataset).
Embeddings of full datasets as well as subsamples of varying sizes replicated thrice for five dimensionality reduction methods. The color-code is generated using the embedding of the full dataset and propagated to the subsamples. Datasets shown are the a) Samusik_all, b) Wong and c) Han_400k datasets.
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Becht, E., McInnes, L., Healy, J. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37, 38–44 (2019). https://doi.org/10.1038/nbt.4314
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