Dimensionality reduction for visualizing single-cell data using UMAP

E Becht, L McInnes, J Healy, CA Dutertre… - Nature …, 2019 - nature.com
Nature biotechnology, 2019nature.com
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 …
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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