Thanks for the great tool! I have been using Resolvi to correct for batch effects in my single cell spatial transcriptomics data. Additionally, I m also using it to check for differences between the nuclei-expanded and cellpose-segmented sample datasets. The concatenated dataset is quite large ~ 1553536 cells. After training the model, I tried computing differential expression testing on leiden clusters but even after ~2 hours compute was not done. Additionally, the progress bar shows nothing. *In this screenshot I reran the cell after specifying group1.
Is this to be expected since the dataset is large or is there something abnormal happening? Just to mention, I don’t see a spike/change in my GPU/CPU usage after running differential test.
I also have the same problem. Then, I ran scVIVA on the same set of cells (using the resolVI embeddings as suggested in another post) and was able to run differential expression in a much shorter amount of time.
I have experienced the same, differential expression takes 9 hours, where as the step before, resolvi training finishes in 1.5 hours. About 300k cells x 5000 genes
What I did was run Resolvi in unsupervised mode and perform leiden clustering on the X_resolVI embeddings to get clusters.
Then I used the X_resolVI embeddings along with the cluster labels as input to scVIVA. From there I used the differential_expression module scVIVA uses.
