Dimensionality reduction for visualizing single-cell data using UMAP

被引：3192

作者：

Becht, Etienne ^{[1
]}

McInnes, Leland ^{[2
]}

Healy, John ^{[2
]}

Dutertre, Charles-Antoine ^{[1
]}

Kwok, Immanuel W. H. ^{[1
]}

Ng, Lai Guan ^{[1
]}

Ginhoux, Florent ^{[1
]}

Newell, Evan W. ^{[1
,3
]}

机构：

[1] ASTAR, Singapore Immunol Network SIgN, Singapore, Singapore

[2] Tutte Inst Math & Comp, Ottawa, ON, Canada

[3] Fred Hutchinson Canc Res Ctr, Vaccine & Infect Dis Div, 1124 Columbia St, Seattle, WA 98104 USA

来源：

NATURE BIOTECHNOLOGY | 2019年 / 37卷 / 01期

关键词：

ATLAS; CYTOMETRY;

D O I：

10.1038/nbt.4314

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

引用

页码：38 / +

页数：8

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