Visualizing single-cell data with the neighbor embedding spectrum
Damrich, S.; Klockow, M. V.; Berens, P.; Hamprecht, F. A.; Kobak, D.
Show 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.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Sincast: a computational framework to predict cell identities in single cell transcriptomes using bulk atlases as references 95%
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference 94%
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.