nlnet (version 1.4)

KPC: implementation of K-Profiles Clustering

Description

implementation of K-Profiles Clustering

Usage

KPC(dataset, nCluster, maxIter = 100, p.max = 0.2, p.min = 0.05)

Arguments

dataset

the data matrix with genes in the row and samples in the column

nCluster

the number of clusters K

maxIter

the maximum number of iterations

p.max

the starting p-value cutoff to exclude noise genes

p.min

the final p-value cutoff to exclude noise genes

Value

Return a list about gene cluster and the list of value p

cluster

gene cluster

p.list

a list of value p

References

http://www.hindawi.com/journals/bmri/aa/918954/

See Also

data.gen

Examples

# NOT RUN {
 ## generating the data matrix & hiden clusters as a sample
 input<-data.gen(n.genes=40, n.grps=4)
 ## now input includes data matrix and hiden clusters, so get the matrix as input.
 input<-input$data
 
 ## set nCluster value to 4
 kpc<-KPC(input,nCluster=4)
  
 ##get the hiden cluster result from "KPC"
 cluster<-kpc$cluster
 ##get the list of p
 p<-kpc$p.list
# }