Cluster cells — clusterCells • SpatialMap

Clusters cells using graph clustering methods. A prerequisite is a nearest neighbor network (NN) object having been computed on the data, often by runUMAP.

Usage

clusterCells(
  object,
  method = "leiden",
  nn.name = "umap",
  col.name = ".clusters",
  cluster.resolution = NULL,
  ...,
  analyze = NULL,
  cores = NULL,
  parallel = NULL
)

Arguments

object: A SpatialMap object
method: What method should be used to cluster? (Right now, lovain and leiden clustering are supported. DBscan easy to add)
nn.name: The name of the NN object in nearestNeighborNetworks to use. Defaults to UMAP, since UMAP is a common workflow. Any graph can be used though.
col.name: The name of the column containing cluster assignments to add to Regions' cellMetadata
cluster.resolution: Enter a cluster resolution parameter. Defaults to 1.0 for leiden, 50 for louvain.
...: Additional arguments passed to the clustering functions
analyze: What to analyze (and how). The options are "regions", NULL (activeAnalysis), the name of a currently existing formal analysis, or the name of a column in object's projectMetadata (an "informal" analysis).
cores: Parallelization options
parallel: Parallelization options

Value

A SpatialMap object with new cell cluster memberships added to the cellMetadata slot as a factor. The new column in cellMetadata will be named according to the value passed to col.name.

Details

See vignette("AnalysisGuide3_Unsupervised_clustering") & vignette("AnalysisGuide4_Subsetting_and_subclustering") for a representative workflow that uses this function.