Just published: UMAPs have become a very popular tool for visualizing high-dimensional data in biology, but they have significant drawbacks. In https://doi.org/10.1186/s12859-024-05927-y Kitanovski et al describe scBubbletree (sc because our focus is on single cell data) as a better alternative: it is easier to interpret, quantitative, and allows for integration of different types of data. The method is implemented in a free, open source R package (https://bioconductor.org/packages/release/bioc/html/scBubbletree.html). I am sure that this type of quantitative visualization is beneficial beyond single cell gene expression data.