KPC: implementation of K-Profiles Clustering

In nlnet: Nonlinear Network, Clustering, and Variable Selection Based on DCOL

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

Author(s)

Tianwei Yu <[e-mail]>

References

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

See Also

data.gen

Examples

 ## 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

nlnet documentation built on Jan. 13, 2021, 10:35 a.m.