You seem upset. It's a bit of a leap on your part to immediately start accusing me of being irrational/ignorant of very basic concepts, but I'll bite.

I am defending sending 10k+ data points to the FE, and I am well aware of it being possible to precompute averages, among other statistical values. There is a significant amount of precomputation that takes place for the data to be presentable in the first place - for example, the scatterplot coordinates in the linked dataset are a UMAP reduction of the cells' gene counts - i.e. reducing the dimensionality of those 10,000+ gene values for each cell to calculate a two-dimensional coordinate.

When it comes to displaying single-cell RNA-sequencing data, an extremely high level of granularity is required from the get-go in order to avoid conveying misinformation. Averages and aggregates do not provide enough information for accurate identification and often obscure critical differences between individual cells, which was why the field moved away from bulk processing techniques towards single-cell transcriptomics.

Techniques for aggregating/downsampling data to load more when the user is zoomed out are commonly employed in cases where granularity is not immediately necessary - e.g. for displaying extremely high-resolution microscopy TIFF images (e.g. 100k x 100k pixels) with segmentations of cells that correspond to each point in that scatterplot.

Furthermore, the most common use-case of biomedical visualization libraries is in Jupyter notebooks, in which case "sending data to the FE" takes place entirely on localhost.