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Dimensionality reduction for visualizing single-cell data using UMAP

Nature Biotechnology volume 37, pages 38–44 (2019)Cite this article

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Matters Arising to this article was published on 01 February 2021

Abstract

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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Figure 1: UMAP embeds local and large-scale structure of the data.
Figure 2: UMAP embeddings of bone marrow and blood samples recapitulate hematopoiesis.
Figure 3: Run times of five dimensionality reduction methods for inputs of varying sizes.
Figure 4: Analysis of local data structure in embeddings produced by each algorithm.
Figure 5: Preservation of pairwise distances in embeddings.
Figure 6: Reproducibility of large-scale structures in embeddings.

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Acknowledgements

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.

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Authors and Affiliations

  1. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Etienne Becht, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux & Evan W Newell

  2. Tutte Institute for Mathematics and Computing, Ottawa, Ontario, Canada

    Leland McInnes & John Healy

  3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

    Evan W Newell

Authors
  1. Etienne Becht
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  2. Leland McInnes
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  4. Charles-Antoine Dutertre
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Contributions

E.B., L.M., J.H., C.-A.D., I.W.H.K. and E.W.N. analyzed data. L.G.N., F.G. and E.W.N. helped supervise the project. L.M. and J.H. developed UMAP. All authors participated in writing and revising the manuscript.

Corresponding author

Correspondence to Evan W Newell.

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Competing interests

E.W.N. is a board director and shareholder of immunoSCAPE Pte. Ltd., which is an immune profiling service provider.

Integrated supplementary information

Supplementary Figure 1 Phenograph clustering identifies cell clusters in the Wong dataset

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.

Supplementary Figure 2 Annotation of the tissue of origins on UMAP, t-SNE and PCA plots

Scatterplot of embeddings of the Wong dataset using UMAP (top), t-SNE (middle) and 2D PCA (bottom) color-coded by tissues of origin.

Supplementary Figure 3 Identification of unlabeled erythrocytes in the Samusik_01 dataset

Expression of Ter119 (a marker for mature erythrocytes) color-coded on the UMAP embedding of the Samusik_01 dataset.

Supplementary Figure 4 Surface densities of events in UMAP and t-SNE embeddings

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.

Supplementary Figure 5 Pre-filtering of the Han dataset

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).

Supplementary Figure 7 Qualitative assessment of the reproducibility of embeddings

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.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1451 kb)

Life Sciences Reporting Summary (PDF 130 kb)

Supplementary Table 1

Description of the datasets (XLSX 5 kb)

Supplementary Table 2

Algorithms benchmarked (XLSX 5 kb)

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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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