Visualizing single-cell data with the neighbor embedding spectrum

Damrich, S.; Klockow, M. V.; Berens, P.; Hamprecht, F. A.; Kobak, D.

doi:10.1101/2024.04.26.590867

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bioRxiv · 10.1101/2024.04.26.590867

Visualizing single-cell data with the neighbor embedding spectrum

Damrich, S.·Klockow, M. V.·Berens, P.·Hamprecht, F. A.·Kobak, D.

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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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Damrich, S., Klockow, M. V., Berens, P., Hamprecht, F. A., Kobak, D.. 2024-04-29. Visualizing single-cell data with the neighbor embedding spectrum. https://doi.org/10.1101/2024.04.26.590867

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