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.

Keywords

Dimensionality reductionComputer scienceNonlinear dimensionality reductionMass cytometryVisualizationReduction (mathematics)BenchmarkingData miningPrincipal component analysisCurse of dimensionalityPattern recognition (psychology)Artificial intelligenceMathematicsChemistry

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Publication Info

Year
2018
Type
article
Volume
37
Issue
1
Pages
38-44
Citations
5349
Access
Closed

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Cite This

Étienne Becht, Leland McInnes, John Healy et al. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology , 37 (1) , 38-44. https://doi.org/10.1038/nbt.4314

Identifiers

DOI
10.1038/nbt.4314
PMID
30531897

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Data completeness: 81%