Dimensionality reduction for visualizing single-cell data using UMAP.
Nat Biotechnol
; 2018 Dec 03.
Article
en En
| MEDLINE
| ID: mdl-30531897
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Nat Biotechnol
Asunto de la revista:
BIOTECNOLOGIA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Singapur