Excess singletons identified from 2.4M cells

[Enterprise-D]

Hi!

I am currently running FindClusters() on a Nanostring 2.4M dataset. Ideally there should be ~10 major clusters but over 120K communities was firstly identified, with most of the are singletons (I tried algorithm=c(1, 2, 3), and Leiden never finishes). I think this much more than expected as singletons account for ~5% of total cells.

Additionally, the merge of singletons seems to be very slow and after days of computation, it still hasn't stopped yet.

Any alternative solution to work around assuming that we have enough RAM? I tried sketch-based workflow but it will result in disagreement between UMAP from the "projected full PCA" and projected cell types.

Many thanks,