Cluster cells in spe using graph methods.

Usage

getClusters(
  spe,
  nbrs_name = NULL,
  method = c("leiden", "louvain"),
  resolution = 1,
  cluster_name = "cluster",
  unassigned = c("merge", "label", "discard"),
  min_size = NULL,
  start_from = 1,
  seed = 1,
  ...
)

Arguments

spe: A SpatialExperiment object.
nbrs_name: Name of neighbour list for clustering. If NULL, uses the newest one in spe@metadata$nbrs$cell. findNbrsSNN must have been run first; otherwise getClusters errors.
method: Clustering methods. Options are leiden and louvain.
resolution: Higher resolution for more clusters and lower for fewer clusters. See cluster_leiden and cluster_louvain
cluster_name: Name to store the clusters in spe's colData
unassigned: How to handle cells in clusters with at most min_size cells (singletons and tiny fragments, often left isolated by SNN pruning). One of: "merge" (default) graph-merges each cell into its most-connected retained cluster; "label" leaves all such cells "unassigned"; "discard" removes them from the spe (changing ncol(spe)).
min_size: Maximum size for a cluster to be treated as too small (see unassigned). Defaults to NULL, which uses either 5 or 0.01\ whichever is smaller.
start_from: Integer at which cluster numbering starts. 1 (default) numbers clusters 1..K.
seed: seed for clustering
...: Other clustering arguments for cluster_leiden or cluster_louvain

Value

A spe with the clusters stored in reducedDims.

A SpatialExperiment object

Details

Cluster cells with igraph using an SNN neighbour list built by findNbrsSNN, which must be run before getClusters. Any neighbour list in spe@metadata$nbrs$cell can be selected via nbrs_name.

Examples


data("xenium_bc_spe")
spe <- normalizeAssay(spe)
spe <- runPCA(spe)
#> Genes with 0 variance are excluded: ENSG00000135218 NegControlProbe_00002 NegControlCodeword_0504 NegControlCodeword_0509 NegControlCodeword_0510 NegControlCodeword_0511 NegControlCodeword_0512 NegControlCodeword_0516 NegControlCodeword_0517 NegControlCodeword_0518 NegControlCodeword_0519 NegControlCodeword_0520 NegControlCodeword_0522 NegControlCodeword_0526 NegControlCodeword_0527 NegControlCodeword_0530 NegControlCodeword_0536 NegControlCodeword_0537 BLANK_0030 BLANK_0163 BLANK_0165 BLANK_0212 BLANK_0221 BLANK_0230 BLANK_0237 BLANK_0311 BLANK_0361 BLANK_0365 BLANK_0382 BLANK_0384 BLANK_0387 BLANK_0388 BLANK_0391 BLANK_0393 BLANK_0397 BLANK_0399 BLANK_0404 BLANK_0406 BLANK_0410 BLANK_0411 BLANK_0418 BLANK_0425 BLANK_0432 BLANK_0447
spe <- findNbrsSNN(spe,dimred="PCA")
#> [1] "Getting K-nearest neighbour"
#> [1] "Getting shared nearest neighbour"
spe <- getClusters(spe, resolution=0.5)
#> Reassigned all 1 cells from clusters with <= 1 cells (0 by graph, 1 by distance).