scverse
Possible enhancement: multithreaded (via numba) mann-whitney tests
- [x] Additional function parameters / changed functionality / changed defaults?
I recently wrote up a parallelized implementation of the Mann-Whitney U test, for my own use (gist is here). For the types of tests we tend to do in scRNAseq (lots of different features, 2d arrays) it basically scales with the number of cores you can throw at it. When you're doing a lot of tests this is very nice!
Given that scanpy already has a dependency on numba this would be a pretty simple thing to add, if you want to do so. Thought I would just point it out!
- James
We're always up for improved performance! Would love to see improvements here. (Btw, I think I've already got your gist bookmarked on twitter)
Do you have any benchmarks of performance here? Especially against our current implementation.
I haven't benchmarked against scanpy, only against scipy.stats.mannwhitneyu (which at this point can handle arrays, I know it couldn't before). On my laptop (an 8-core Intel MacBook Pro) it's about a 10x speedup. But with more cores it can be a lot more.
Even without parallelization, you can get some improvement by just using numba.njit on some of the internal bits (e.g. tiecorrect).
Of course, your code has a lot of options that I didn't bother with, because I didn't need them. Some of them might be harder to JIT than others.
Your changes made it into rank_genes_groups’ wilcoxon flavor via #3529.
Scanpy doesn’t currently have mannwhitneyu, but if you want to contribute it, feel free!
