Generate elbow plots — optiPlot • FuseSOM

A function generating the elbow plot for the optimal number of clusters returned by the estimateNumcluster() function Methods available are: Gap, Silhouette, Slope, Jump, and Within Cluster Distance(WCD)

Usage

optiPlot(data, method = "jump")

Arguments

data: a Self Organizing Map object generated by generatePrototypes(), or an object of class SingleCellExperiment or SpatialExperiment
method: one of 'jump', 'slope', 'wcd', 'gap', or 'silhouette'

Value

an elbow plot object where the optimal number of clusters is marked

Author

Elijah WIllie ewil3501@uni.sydney.edu.au

Examples

data("risom_dat")
risomMarkers <- c(
  "CD45", "SMA", "CK7", "CK5", "VIM", "CD31", "PanKRT", "ECAD"
)
res <- runFuseSOM(risom_dat, markers = risomMarkers, numClusters = 23)
#> You have provided a dataset of class data.frame
#> Everything looks good. Now running the FuseSOM algorithm
#> Now Generating the Self Organizing Map Grid
#> Optimal Grid Size is: 5
#> Now Running the Self Organizing Map Model
#> Now Clustering the Prototypes
#> Now Mapping Clusters to the Original Data
#> The Prototypes have been Clustered and Mapped Successfully
#> The FuseSOM algorithm has completed successfully
resEstK <- estimateNumCluster(res$model, kSeq = 2:25)
#> Now Computing the Number of Clusters using Discriminant Analysis
#> Now Computing The Number Of Clusters Using Distance Analysis
p <- optiPlot(resEstK, method = "jump")