Cluster sub-set analysis using Seurat

I am trying to dig deeper into my Seurat single-cell data analysis. If I want to further sub-cluster a big cluster then what would be the best way to do it:

1) Decreasing the resolution at FindClusters stage

or

2) extracting the individual cell index and re-clustering and then further analysis.

What would be the major difference in outcome between two approaches?
How to perfrom the second approach in Seurat? using Whichcells function to extract and then I am not sure about next step please

Thank you.

Subsetting and rerunning the umap coords and clustering will separate out different things better.

Personally, I cheat, and use Loupe. It’s easy to use the mouse to draw categories, then export your categories into the metadata of your seurat object. But you have to have your data go through the 10xGenomics pipeline for that.

Both approaches are valid. It really depends on your question. You can increase the resolution to get more clusters. You can just select the cluster of interest and analyze it more closely or in different ways. For example, if your population of interest is only 10% of the total cells, it would be difficult to analyze it in the full dataset. If you just keep those cells, that becomes easier.

If you want to extract the cells of a specific cluster, you can use subset(). The Interaction Tips vignette has a few examples such as:

# Can I create a Seurat object of just the NK cells and B cells?
subset(pbmc, idents = c("NK", "B"))

I have a follow-up question:

How can I define a new cluster based on a feature’s expression level, but maintain it in the same object, along side the other clusters that were already defined by clustering?

Thanks!