Hi Brigitte, I am not a Seurat developer, but your question has been discussed previously #96 In essence, there is no correct clustering parameter, either you will over or under cluster your data. To compensate for what makes biological sense in the context of your experiment, you can merge certain clusters together. I usually don't do this and just tweak the resolution till each cluster has at least 20 unique differentially expressed genes. I think as long as you define a cluster like a differentially expressed gene, it should be fine. Those are my thoughts though.

I have attached here some old R code from what I believe were Seurat v1.4 tutorials, it has a few lines and some description on how to perform cluster re-assignment......something to look at while you wait for a Seurat dev's answer.

🐉

pbmc33k.zip

Hi Birgitte,

While Seurat doesn't have tools for comparing cluster resolutions, there is a tool called clustree designed for this task and works on Seurat v3 objects natively. It's available on CRAN and can be installed with a simple install.packages('clustree')

You can read their vignette (Seurat object section) for more details.

Hi Birgitte, there is another new tool called IKAP that helps to identify meaningful cutoffs for clustering resolutions:
IKAP github and IKAP preprint

Hi Birgitte,

While Seurat doesn't have tools for comparing cluster resolutions, there is a tool called clustree designed for this task and works on Seurat v3 objects natively. It's available on CRAN and can be installed with a simple install.packages('clustree')

You can read their vignette (Seurat object section) for more details.

Thank you very much for this!!!