Perform K-means clustering on a specified reduction

This function runs K-means clustering on a specified reduction in a SpaMTP Seurat object and adds the cluster assignments to the object metadata.

Usage

GetKmeanClusters(
  data,
  reduction = "SpatialPCA",
  cluster.name = "spatial_clusters",
  clusters = 8,
  iter.max = 10,
  nstart = 1,
  algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  trace = FALSE,
  seed = 888
)

Arguments

data

A SpaMTP Seurat object containing the results from RunSpatialGraphPCA().

reduction

Character string stating the name of the reduction slot to use (default = "SpatialPCA").

cluster.name

Character string of the name of the metadata column to store the cluster labels (default = "spatial_clusters").

iter.max

Integer defining the maximum number of iterations allowed (default = 10).

nstart

Integer stating the number of random sets to choose (default = 1).

algorithm

Character string defining the K-means algorithm to use. One of "Hartigan-Wong", "Lloyd", "Forgy", or "MacQueen" (default = "Hartigan-Wong").

trace

Logical boolean indicating whether to produce tracing information on the progress of the algorithm (default = FALSE).

seed

Integer of the random seed to use for reproducibility (default = 888).

centers

Integer defining the number of clusters to form (default = 8).

Value

A SpaMTP Seurat object with a new metadata column containing the K-means cluster assignments.

Examples

# seurat_object <- GetKmeanClusters(spamtp_obj, reduction = "SpatialPCA", centers = 8, cluster.name = "test_clusters")
# SpatialDimPlot(spamtp_obj, group.by = "test_clusters")