Hi, I've been trying to run the Xenium tutorial but ran into some issues. Most notably, the step running the downstream_analysis() function seems to be taking a very long time. The provided dataset is very big so that partly explains why, but I'd be curious to know the resources available on your end (and average runtime).
Also, I was confused by the following statement in the docstring:
(Note: This function executes default DE analysis as is mentioned in scanpy docs. For more flexibility, it is recommended for users to perform analysis over generated neighborhood scores manually with the help of scanpy")
Can I simply swap with a standard pipeline? If so, it's not clear to me why there would be operations on the pathways/gene sets and/or on the ligand-target pairs? It seems that the function combines multiple functional modules (pre-processing using scanpy, DE analysis, enrichment), which limits flexibility. For example, right now if one wants to specify igraph as flavor for the clustering, one needs to essentially modify the source code. Also, just looking at the code, it seems that there are several areas that would be computationally greedy (conversion to dataframes instead of working on the AnnData object, no vectorized operations,...)
Thanks for your help on this!
Hi, I've been trying to run the Xenium tutorial but ran into some issues. Most notably, the step running the
downstream_analysis()function seems to be taking a very long time. The provided dataset is very big so that partly explains why, but I'd be curious to know the resources available on your end (and average runtime).Also, I was confused by the following statement in the docstring:
Can I simply swap with a standard pipeline? If so, it's not clear to me why there would be operations on the pathways/gene sets and/or on the ligand-target pairs? It seems that the function combines multiple functional modules (pre-processing using
scanpy, DE analysis, enrichment), which limits flexibility. For example, right now if one wants to specifyigraphas flavor for the clustering, one needs to essentially modify the source code. Also, just looking at the code, it seems that there are several areas that would be computationally greedy (conversion to dataframes instead of working on the AnnData object, no vectorized operations,...)Thanks for your help on this!