Final non-spatial clustering

A function to perform clustering on adaptively smoothed gene expression data to generate ClustSIGNAL clusters.

Usage

p2_clustering(
  spe,
  dimRed_f = c("None", "embed.smooth"),
  batch = FALSE,
  batch_by = "None",
  threads = 1,
  clustParams = list(clust_c = 0, subclust_c = 0, iter.max = 30, k = 10, cluster.fun =
    "louvain")
)

Arguments

spe

SpatialExperiment object containing the adaptively smoothed gene expression.

dimRed_f

a character indicating the name of the reduced dimensions in the SpatialExperiment object (i.e., from reducedDimNames(spe)) to use for final clustering step. Two valid options are "None" (default), which triggers a PCA run on smoothed expression, and "embed.smooth", which triggers a search for "embed.smooth" low embedding in reducedDimNames(spe).

batch

a logical parameter for whether to perform batch correction. Default value is FALSE.

batch_by

a character indicating name of colData(spe) column containing the batch names. Default value is 'None'.

threads

a numeric value for the number of CPU cores to be used for the analysis. Default value set to 1.

clustParams

a list of parameters for TwoStepParam clustering methods: clust_c is the number of centers to use for clustering with KmeansParam. By default set to 0, in which case the method uses either 3000 centers or 1/5th of the total cells in the data as the number of centers, whichever is lower. subclust_c is the number of centers to use for sub-clustering the initial clusters with KmeansParam. This parameter is not used in the final clustering step. iter.max is the maximum number of iterations to perform during clustering and sub-clustering with KmeansParam. Default value is 30. k is a numeric value indicating the k-value used for clustering with NNGraphParam. Default value is 10. cluster.fun is a character indicating the graph clustering method used with NNGraphParam. By default, the Louvain method is used.

Value

SpatialExperiment object containing clusters generated from smoothed data.

Examples

data(ClustSignal_example)

# For non-spatial clustering of normalised counts
spe <- clustSIGNAL::p2_clustering(spe)
#> 06:35:25 Calculating PCA using smoothed data.
#> 06:35:25 Final clustering performed on smoothed data. Clusters = 6 
spe$ClustSIGNAL |> head()
#> [1] 3 3 3 3 3 3
#> Levels: 1 2 3 4 5 6