DMT-Dens: Density-preserving manifold visualization for biological data
Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biological data. Although many methods preserve local neighborhoods, they may distort the apparent sampling density of processed observations, altering the visual contrast between dense and sparse regions and complicating the interpretation of rare, transitional, or continuous cell-state populations. Results: We present DMT-Dens, a parametric manifold-visualization method built on a latent-token Transformer encoder. The model integrates rank-based manifold alignment with hard-pair aggregation. To preserve density, it optimizes a loss based on the Pearson cor...
| Authors | Ruizhe Wang, Yixuan Dong, Bolin Yang et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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