Visualizing single-cell data with the neighbor embedding spectrum.
bioRxiv
; 2024 Apr 29.
Article
em En
| MEDLINE
| ID: mdl-38746298
ABSTRACT
The two-dimensional embedding methods t-SNE and UMAP are ubiquitously used for visualizing single-cell data. Recent theoretical research in machine learning has shown that, despite their very different formulation and implementation, t-SNE and UMAP are closely connected, and a single parameter suffices to interpolate between them. This leads to a whole spectrum of visualization methods that focus on different aspects of the data. Along the spectrum, this focus changes from representing local structures to representing continuous ones. In single-cell context, this leads to a trade-off between highlighting rare cell types or continuous variation, such as developmental trajectories. Visualizing the entire spectrum as an animation can provide a more nuanced understanding of the high-dimensional dataset than individual visualizations with either t-SNE or UMAP.
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MEDLINE
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En
Ano de publicação:
2024
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Article