Louvain clustering on weighted network?

I’m looking at network based clustering for single cell data, and was wondering about the louvain implementation used here. Is there a reason scanpy doesn’t currently allow clustering on a weighted network (like that generated by sc.pp.neighbors(adata, knn=False, method="gauss"))? It’s my impression that this would be more similar to the cited Phenograph method.

I’d be interested in adding that functionality (and have a proof of concept on my fork), but wanted to hear if there was a reason this wasn’t implemented currently first.

Thanks for any input!

[ivirshup]

I’d agree with your statement that engineering the representation is more important than the analysis. I view my goal here as allowing more representations as input. Would you mind saying more about why you thing using different metics is less clean (simple?)? I would think that would depend on what representation you’re calculating the distances on.

[fidelram]

@LuckyMD I changed the resolution of the method and I find that the weighted version produces results that are different. In other words, the difference in the clustering does not seem to come from differences in the resolution parameter.

Here is an example. In both cases, 13 clusters are found; however only the weighted version can identify the small cluster No. 13:

Increasing the resolution of the non-weighted method eventually discerns the small cluster 13

I would vote to add the modifications from @ivirshup