I work with t-SNE on a daily basis, and also with UMAP but less frequently. My interest is not scRNAseq analysis but rather metagenomic binning, but dimensionality reduction methods don't care about the source of data. After having done this hundreds of times, I can state with certainty that clusters of dots correspond to real biological entities, and I am sure the same is true for sc-type analyses. There are numerous examples of experimentally confirmed cells or organisms that were first postulated in t-SNE/UMAP 2D plots. Non-linear dimensionality methods embed the points in ways that give intuitive visualizations and preserve local distances, but there is no guarantee that any two random points will have their distance faithfully represented in a t-SNE/UMAP 2D plot. In practice this means that cluster distances in the embedded space will most likely be preserved between groups that are relatively close to each other, and that is typically good enough for most applications.

The problem arises when one tries to understand the dynamics in sc-type datasets. As far as I know, there is no formal proof that the way t-SNE/UMAP spread the data points around has anything to do with biology. I think people believe those trajectories because they often make sense, and in many instances they might be correct. However, it is very likely that in some instances the order and connectivity of clusters have noting to do with biology, so concerns raised by Pachter and others are legitimate. Generally speaking, too many people use methods for which they do not understand pros and cons. The lure of dimensionality methods is especially dangerous because they produce such visually appealing representations that some people automatically assume they must be correct.

I generally avoid these Twitter-borne discussions since often enough people stirr a lot of noise for the sake of being heard, and then others jump in to fight about it -- all of which does not really help the scientific interaction. My personal take, like with any method, is to simply give it a try and see if it suggests potentially interesting results. Most of the time these dimensionality reductions are mere visualizations. For me personally it helps a lot to simply "look" at a 2D representation of my data when checking markers etc. The overall shape does not really matter, nor does the distance of points. Still, we have one project in which the spatial aspects of the UMAP and the cell density in certain areas suggested some interesting phenomenon that we followed up and validated in the lab, so here it gave valuable insights. Just try and see, like with any method.

For what it’s worth, Pachter is not the only person discouraging UMAPs, other computational biologists are discouraging UMAPs and have critiques that might be worth reading (e.g. Rafael Irizarry https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/ ).

My take: The single cell field has lots of problems / shortcomings (especially for methods that go beyond simple exploratory work), but not a lot of easy solutions. A simple “click of a button” method to do a “best practices” analysis doesn’t really exist.

For trajectory, in case you’re interested in the latest work from the Pachter group on this, feel free to check out: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012752

(FYI, I work with the group although I am not really involved in the subgroup that works on modeling.)