I’m a huge fan of UMAP, but this [0] paper suggests that t-SNE can be tuned to produce UMAP-like results (the algorithms are extremely similar—you can recover t-SNE with certain UMAP parameter choices). One of the insights is to use PCA first to better preserve the global structure.

For example, see figure 9 in the paper: the plot on the left is the typical result of default t-SNE (distance between global structures not well-represented, since everything is jammed together), and the plot on the right is very UMAPish.

Basically, there are a lot of preprocessing and parameter choices involved in producing these embedding plots, so it’s advisable to try to understand the effects of these choices regardless of which algorithm you choose.

[0]: https://www.nature.com/articles/s41467-019-13056-x