metaClustering_consensus: MetaClustering

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

MetaClustering

Description

Cluster data using hierarchical consensus clustering with k clusters

Usage

metaClustering_consensus(data, k = 7, seed = NULL)

Arguments

data	Matrix containing the data to cluster
k	Number of clusters
seed	Seed to pass to consensusClusterPlus

Value

Numeric array indicating cluster for each datapoint

See Also

MetaClustering

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 consensus metaclustering
   metacl <- metaClustering_consensus(flowSOM.res$map$codes, k = 10)    

saeyslab/FlowSOM documentation built on July 6, 2024, 10:59 a.m.