UMAP vs “rigorous” t-SNE

UMAP vs “rigorous” t-SNE


I’ve heard a lot of people discussing UMAP recently as though it has essentially superseded t-SNE for visualizing scRNA-seq data. UMAP is certainly impressive, but it seems to me that there are a lot of things one can do to pretty dramatically improve the output of t-SNE – for example, perplexity annealing, or PCA initialization followed by merging two perplexities (all of which are described here, for example). All of the comparisons that I have seen between UMAP and t-SNE compare UMAP to t-SNE alone (e.g., without these “tricks” that can improve the t-SNE plots. This feels a little like a strawman to me; has anyone done any work or seen any studies comparing UMAP to t-SNE for scRNA-seq data visualization with these improvements?



dimensionality reduction



I think this “arising from” article is very relevant and provides a thorough accounting of what you discuss above. Essentially, it argues that many of the benefits of UMAP arise from its initialization procedure, and that t-SNE with the appropriate initialization procedure sees many of the same benefits. However, the ecosystems around these different methods and the different tools and implementations of them have diverged and expanded enough that it seems very likely there are many other distinct benefits of each approach depending on the particular implementation you choose.

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