After subclustering using FindSubCluster, how do I FindAllMarkers using the additonal cluster assignments on the whole Seurat Object?

[Pancreas-Pratik]

After subclustering using FindSubCluster, how do I FindAllMarkers using the additional cluster assignments on the whole Seurat Object? The cluster I subcluster is skipped over during FindClusters for some reason?

> scfp <- FindNeighbors(scfp, graph.name = "test", dims = 1:100)
Computing nearest neighbor graph
Computing SNN
> scfp <- FindClusters(scfp, graph.name = "test", resolution = 2, algorithm = 1, verbose = TRUE)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1836
Number of edges: 16978

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7368
Number of communities: 24
Elapsed time: 0 seconds
4 singletons identified. 20 final clusters.
> #scfp <- RunUMAP(scfp, dims = 1:100)
> scfp <- RunTSNE(scfp, dims = 1:100)
> #DimPlot(scfp, reduction = "umap", label = TRUE)
> DimPlot(scfp, reduction = "tsne", label = TRUE, label.size = 6 )
> scfp <- FindSubCluster(scfp, "6", "test", subcluster.name = "blood",  resolution = .3, algorithm = 1)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 104
Number of edges: 819

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7301
Number of communities: 3
Elapsed time: 0 seconds
> DimPlot(scfp, reduction = "tsne", group.by = "blood", label = TRUE, label.size = 6)
> scfp.markers <- FindAllMarkers(scfp, graph.name = "test", group.by = "blood", only.pos = TRUE, min.pct = 0.1, logfc.threshold = 0.25)
Calculating cluster 0
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  
Calculating cluster 1
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  
Calculating cluster 2
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s  
Calculating cluster 3
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s  
Calculating cluster 4
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s  
Calculating cluster 5
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s  
Calculating cluster 6
Calculating cluster 7
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  
Calculating cluster 8
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  
Calculating cluster 9
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  
Calculating cluster 10
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s  
Calculating cluster 11
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  
Calculating cluster 12
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 13
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s  
Calculating cluster 14
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s  
Calculating cluster 15
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  
Calculating cluster 16
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s  
Calculating cluster 17
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s  
Calculating cluster 18
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  
Calculating cluster 19
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s  
> View(scfp.markers)

[nbpeterson3]

The meta data produced by FindSubCluster ([email protected]$blood) has the cluster assignments for each observation. Is it possible to take this and assign it to @active.ident?

[Pancreas-Pratik]

Thank you @nbpeterson3! This is a good suggestion. I will try this, and post my outcome!

[Pancreas-Pratik]

Thank you @nbpeterson3! This was the solution. I played around with SetIdent. Looks good so far.

[nbpeterson3]

Glad it worked! Would you mind sharing your solution?

[Pancreas-Pratik]

I would not mind!
So here's what I did:

I'll just post the commands with some pointers.

@timoast explained the graph.name argument for me here: #4155

And also included it in the documentation: https://www.rdocumentation.org/packages/Seurat/versions/4.0.0/topics/FindNeighbors

Here's a tutorial and my best description using my question example above:

##So in the FindNeighbors you should set the graph.name argument to a vector containing "NN" and "SNN" for easy identification of which is which
##NN refers nearest-neighbor while SNN refers to shared-nearest-neighbor
##Seurat by default does FindNeighbors using SNN, this will do both NN and SNN
##So like this:
> scfp <- FindNeighbors(scfp, graph.name = c("NN", "SNN"), dims = 1:100)


##You have to now choose which graph you will choose NN or SNN, I'll choose NN here
> scfp <- FindClusters(scfp, graph.name = "NN", resolution = 2, algorithm = 1, verbose = TRUE)
> scfp <- RunTSNE(scfp, dims = 1:100)


##Visualize your plot to see which cluster you want to subcluster
> DimPlot(scfp, reduction = "tsne", label = TRUE, label.size = 3)


##Now use that cluster number as part of the syntax in FindSubCluster. I chose cluster 6. And then after put which graph.name you chose for FindClusters (Remember, I chose NN). and then choose something for subcluster.name.
> scfp <- FindSubCluster(scfp, "6", "NN", subcluster.name = "blood",  resolution = .3, algorithm = 1)


##Your subcluster is now saved in metadata. In this example it's location is "[email protected]$blood". Now use SetIdent to save your new cluster assignment to the main Ident in your object.
> scfp <- SetIdent(scfp, value = [email protected]$blood)


##Visualize the main object without choosing meta data to see if it worked.
> DimPlot(scfp, reduction = "tsne", label = TRUE, label.size = 6)


##Use FindAllMarkers on your main Seurat Object
> scfp.markers <- FindAllMarkers(scfp, only.pos = TRUE, min.pct = 0.1, logfc.threshold = 0.25)


##Continue on with analysis.

[Pancreas-Pratik]

@nbpeterson3 I updated how I did it, based on a better understanding of graph.name!