Dimensionality reduction for visualizing single-cell data using UMAP

Nature Biotechnology

Volumn 37, Issue 1, 2019, Pages 38-47

  Becht, Etienne ^a   McInnes, Leland ^b   Healy, John ^b   Dutertre, Charles Antoine ^a   Kwok, Immanuel W H ^a   Ng, Lai Guan ^a   Ginhoux, Florent ^a   Newell, Evan W ^a,c  

^a AGENCY FOR SCIENCE   (Singapore)

^b The Tutte Institute for Mathematics and Computation   (Canada)

^c USA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLS; CLUSTERING ALGORITHMS; DATA VISUALIZATION;

BIOLOGICAL DATA; DIMENSIONALITY REDUCTION; HIGH DIMENSIONAL DATA; HIGH RESOLUTION; NONLINEAR DIMENSIONALITY REDUCTION; REPRODUCIBILITIES; SINGLE CELL STUDIES; TISSUE COMPOSITION;

CYTOLOGY;

ALGORITHM; ARTICLE; B LYMPHOCYTE; CORRELATION COEFFICIENT; DATA ANALYSIS; ERYTHROCYTE; HEMATOPOIESIS; HUMAN; INTERMETHOD COMPARISON; MACROPHAGE; MASS CYTOMETRY; MULTIFACTOR DIMENSIONALITY REDUCTION; MULTIPOTENT STEM CELL; NATURAL KILLER CELL; NEUTROPHIL; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; RNA SEQUENCE; SINGLE CELL ANALYSIS; T LYMPHOCYTE;

EID: 85059628259     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.4314     Document Type: Article

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