Dimensionality reduction for visualizing single-cell data using UMAP

doi:10.1038/nbt.4314

. 2018 Dec 3.

doi: 10.1038/nbt.4314. Online ahead of print.

Dimensionality reduction for visualizing single-cell data using UMAP

Etienne Becht¹, Leland McInnes², John Healy², Charles-Antoine Dutertre¹, Immanuel W H Kwok¹, Lai Guan Ng¹, Florent Ginhoux¹, Evan W Newell^{1

3}

Affiliations

¹ Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
² Tutte Institute for Mathematics and Computing, Ottawa, Ontario, Canada.
³ Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, Washington, USA.

PMID: 30531897
DOI: 10.1038/nbt.4314

Dimensionality reduction for visualizing single-cell data using UMAP

Etienne Becht et al. Nat Biotechnol. 2018.

. 2018 Dec 3.

doi: 10.1038/nbt.4314. Online ahead of print.

Authors

Etienne Becht¹, Leland McInnes², John Healy², Charles-Antoine Dutertre¹, Immanuel W H Kwok¹, Lai Guan Ng¹, Florent Ginhoux¹, Evan W Newell^{1

3}

Affiliations

¹ Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
² Tutte Institute for Mathematics and Computing, Ottawa, Ontario, Canada.
³ Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, Washington, USA.

PMID: 30531897
DOI: 10.1038/nbt.4314

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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Comment in

Initialization is critical for preserving global data structure in both t-SNE and UMAP.
Kobak D, Linderman GC. Kobak D, et al. Nat Biotechnol. 2021 Feb;39(2):156-157. doi: 10.1038/s41587-020-00809-z. Epub 2021 Feb 1. Nat Biotechnol. 2021. PMID: 33526945 No abstract available.

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Dimensionality reduction for visualizing single-cell data using UMAP

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