Construct a SNN neighbour list from assay.

Usage

findNbrsSNN(
  spe,
  assay = NULL,
  dimred = "PCA",
  n_dimred = NULL,
  k = 20,
  BNPARAM = BiocNeighbors::AnnoyParam(),
  type = c("jaccard", "rank", "number"),
  prune = NULL,
  nbrs_name = NULL,
  cpu_threads = 6
)

Arguments

spe: A SpatialExperiment object.
assay: Name of assay for clustering. Incompatible with dimred.
dimred: Name of the dimensionality reduction (e.g. PCA) for clustering. Incompatible with assay
n_dimred: Integer scalar or vector specifying the dimensions to use if dimred is specified. Defaults to NULL, which uses all available dimensions of the reduced dim (e.g. all PCs produced by runPCA).
k: Integer scalar for number of nearest neighbors to find. Default to 20.
BNPARAM: BiocNeighborParam object specifying the nearest neighbor algorithm. Default is Annoy.
type: Type of weighting scheme for shared neighbors. Options are jaccard (default), rank, and number. type="rank" is defined in Xu and Su (2015).
prune: Edges with SNN weight at or below this value are removed before clustering. Defaults to NULL, which applies a jaccard cutoff of 0.1 for "jaccard", converted to the equivalent shared-neighbour count (2(k+1)*0.1/1.1) for "number". type="rank" is not pruned by default. Set to 0 to disable.
nbrs_name: Name of the neighbour list to be stored in spe. Default to be assay/dimred + "_snn".
cpu_threads: Number of cpu threads for parallel computation.

Value

A spe with the clusters stored in reducedDims.

A SpatialExperiment object

Details

Construct a SNN neighbour list using either the spe's assay or reduced dimension and store it in spe@metadata$nbrs$cell

neighbour list contain

Examples


data("xenium_bc_spe")
spe <- runPCA(spe)
#> Default assay logcounts not found. Switching to counts assay instead.
#> 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"