Large analysis of tSNE and UMAP parameters
James Melville https://github.com/jlmelville made a great work by porting UMAP to a R package and removing the need of Python pre-installation. His package is uwot https://cran.r-project.org/package=uwot and is available on CRAN. The umap funtion offers many parameters that could be tuned. There is also a t-umap function and a lvish method inspired from the largeVis package https://github.com/elbamos/largeVis that was previously on CRAN.
James also made an even more in-depth analysis of the influence of the parameters of tSNE and UMAP. He has written the smallvis package https://github.com/jlmelville/smallvis and he tried many variations of those two main algorithms. To be noticed, "this package is therefore not suitable for large scale visualization. Hence the name smallvis."
I just came up to this huge resource https://jlmelville.github.io/smallvis/ (look at section Explorations of smallvis). I started by looking at results from the MNIST dataset, as it usually seems to me to be close to the cytometry data although all groups are balanced (same number of events in each population).
I hope some of you will find answers,
Samuel