MetaClustering: MetaClustering

In saeyslab/FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

MetaClustering

Description

Cluster data with automatic number of cluster determination for several algorithms

Usage

MetaClustering(data, method, max = 20, seed = NULL, ...)

Arguments

data	Matrix containing the data to cluster
method	Clustering method to use
max	Maximum number of clusters to try out
seed	Seed to pass on to given clustering method
...	Extra parameters to pass along

Value

Numeric array indicating cluster for each datapoint

See Also

metaClustering_consensus

Examples

   # Read from file, build self-organizing map and minimal spanning tree
   fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM")
   flowSOM.res <- ReadInput(fileName, compensate = TRUE,transform = TRUE,
                            scale = TRUE)
   flowSOM.res <- BuildSOM(flowSOM.res,colsToUse = c(9, 12, 14:18))
   flowSOM.res <- BuildMST(flowSOM.res)
   
   # Apply metaclustering
   metacl <- MetaClustering(flowSOM.res$map$codes,
                            "metaClustering_consensus",
                            max = 10)
   
   # Get metaclustering per cell
   flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]    

saeyslab/FlowSOM documentation built on April 20, 2024, 8:30 p.m.