Resolution parameter in Seurat's FindClusters function for larger cell numbers In Seurats' documentation for FindClusters() function it is written that for around 3000 cells the resolution parameter should be from 0.6 and up to 1.2. I am wondering then what should I use if I have 60 000 cells? How to determine that?
Assuming you have an informative selection of variable genes from which you have constructed a number of useful PCs, I'd run a number of iterations with FindClusters() as described in the other answer, then choose a level which overclusters the dataset (for example, clusters that are visibly separate on a t-SNE or other dimensionality reduction plot should definitely have their own number):

seuratobject &lt;- SetAllIdent(seuratobject, id='chosen.resolution')


Then run:

Seurat::BuildClusterTree()
Seurat::FindAllMarkersNode()


Assessing the cluster markers for each node will hopefully give you a good idea on which clusters should be combined. Then you can "combine" the clusters and re-label the cells using something like:

library(plyr)
cell.labels &lt;- seuratobject@ident
cell.labels &lt;- mapvalues(cell.labels,
  from=0:16, # cluster numbers
  to=c('A', 'B', 'C', 'C', 'D', 'E', 'E', ... )) # etc

seuratobject &lt;- AddMetaData(seuratobject, cell.labels, 'Combined.clusters')


The usefulness of the clustering will very much depend on the selection of variable genes, therefore, depending on the (diversity of the) dataset, you will want to experiment with selection parameters or subset the dataset and repeat the above procedure.