SharedComps: Intersection of clusters across multiple methods

In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description

The SharedComps function is an easy way to select the objects that are shared over clusters of different methods.

Usage

SharedComps(List, nrclusters = NULL, fusionsLog = FALSE,
  weightclust = FALSE, names = NULL)

Arguments

List	A list of clustering outputs or the output of the DiffGenes function. The first element of the list will be used as a reference in ReorderToReference.
nrclusters	If List is the output several clustering methods, it has to be provided in how many clusters to cut the dendrograms in. Default is NULL.
fusionsLog	Logical. To be handed to ReorderToReference: indicator for the fusion of clusters. Default is FALSE
weightclust	Logical. To be handed to ReorderToReference: to be used for the outputs of CEC, WeightedClust or WeightedSimClust. If TRUE, only the result of the Clust element is considered. Default is FALSE.
names	Names of the methods or clusters. Default is NULL.

Value

A vector containing the shared objects of all listed elements.

Examples

data(fingerprintMat)
data(targetMat)
data(geneMat)
data(GeneInfo)

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_F,MCF7_T)
names=c('FP','TP')

Comps=SharedComps(List=L,nrclusters=7,fusionsLog=FALSE,weightclust=FALSE,names=names)

IntClust documentation built on May 2, 2019, 5:51 a.m.