Initialization is critical for preserving global data structure in both t-SNE and UMAP
- PMID: 33526945
- DOI: 10.1038/s41587-020-00809-z
Initialization is critical for preserving global data structure in both t-SNE and UMAP
Comment on
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Dimensionality reduction for visualizing single-cell data using UMAP.Nat Biotechnol. 2018 Dec 3. doi: 10.1038/nbt.4314. Online ahead of print. Nat Biotechnol. 2018. PMID: 30531897
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- Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019). - DOI
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- McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
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- Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–40 (2019). - DOI
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- Belkin, M. & Niyogi, P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 585–591 (2002).
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