metaClustering_consensus: MetaClustering

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

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)    

FlowSOM documentation built on Nov. 8, 2020, 6:40 p.m.