How to use FindSubCluster?

[Pancreas-Pratik]

I just found the FindSubCluster tool within Seurat, and am super excited to use it. Just not sure exactly how!

The usage is here:

FindSubCluster(
  object,
  cluster,
  graph.name,
  subcluster.name = "sub.cluster",
  resolution = 0.5,
  algorithm = 1
)

I receive an error when using it.

> test <- FindSubCluster(scfp, 7, scfp, subcluster.name = "test",  resolution = 3, algorithm = 1)
Error in match(x, table, nomatch = 0L) : 
  'match' requires vector arguments
> test <- FindSubCluster(scfp, 7, scfjjd, subcluster.name = "test",  resolution = 3, algorithm = 1)
Error in `[[.Seurat`(object, graph.name) : object 'scfjjd' not found
> test <- FindSubCluster(scfp, 7, RNA, subcluster.name = "test",  resolution = 3, algorithm = 1)
Error in `[[.Seurat`(object, graph.name) : object 'RNA' not found

My object name is scfp. I have done FindClusters already. The cluster I wish to subcluster is 7.

I think my trouble is with the graph.name argument.

EDIT: I tried this:

> test <- FindSubCluster(scfp, "7", "scfp", subcluster.name = "test",  resolution = 3, algorithm = 1)
Error: Cannot find 'scfp' in this Seurat object

Still no success yet...

Any help would really be appreciated!

[Pancreas-Pratik]

Figured it out partially. Thanks to help from @fracarb8 at BioStars.

I went to: https://satijalab.org/seurat/articles/multimodal_reference_mapping.html

and Ctrl+F: graph.name

Turns out graph.name is an argument in FindNeighbors. So I just the FindNeighbors command choosing graph.name as some arbitrary name (test). and then reran FindSubCluster and it worked! Now I'm going to play around with it to optimize the subclustering.

So it looks something like this:

scfp <- FindNeighbors(scfp, graph.name = "test", dims = 1:100)
scfp <- FindClusters(scfp, graph.name = "test", resolution = 4, algorithm = 1, verbose = TRUE)
subclusteredscfp <- FindSubCluster(scfp, "7", "test", subcluster.name = "test",  resolution = 3, algorithm = 1)

Now I have to see what actually is happening, and what the tSNE and uMAPs look like now!

Thank you Thank you!

[Pancreas-Pratik]

Still struggling to figure out how to visualize the subclustering.

[Pancreas-Pratik]

Okay think I got it...

I explored the Seurat object a litle bit more and found that the cluster assignments were saved.
I was able to visualize using the group.by argument so here is the process:

scfp <- FindNeighbors(scfp, graph.name = "test", dims = 1:100)
scfp <- FindClusters(scfp, graph.name = "test", resolution = 2, algorithm = 1, verbose = TRUE)
scfp <- RunTSNE(scfp, dims = 1:100)
DimPlot(scfp, reduction = "tsne", label = TRUE, label.size = 6 )

And then after doing this:

scfp <- FindSubCluster(scfp, "6", "test", subcluster.name = "blood",  resolution = 2, algorithm = 1)
DimPlot(scfp, reduction = "tsne", group.by = "blood", label = TRUE, label.size = 6)

This is the resulting tSNE:

Of course going to play around to optimize cluster numbers...