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Cluster of cluster

Cluster_of_cluster.Rd

ClusterofCluster is a multi-method based on the R package "ConsensusClusterPlus".

Usage

Cluster_of_cluster(
  mRNAexp,
  miRNAexp,
  lncRNAexp,
  methylation,
  maxK = 6,
  reps = 50,
  pItem = 0.8,
  pFeature = 1,
  clusterAlg = "hc",
  distance = "pearson",
  innerLinkage = "complete",
  seed = 1262118388.71279,
  plot = "pdf"
)

Arguments

mRNAexp

A matrix of mRNA expression

miRNAexp

A matrix of miRNA expression

lncRNAexp

A matrix of lncRNA expression

maxK

integer value. maximum cluster number for Consensus Clustering Algorithm to evaluate.

reps

integer value. number of subsamples(in other words, The iteration number of each cluster number)

pItem

Please refer to the "ConsensusClusterPlus" package for detailed information.

pFeature

Please refer to the "ConsensusClusterPlus" package for detailed information.

clusterAlg

character value. cluster algorithm. 'hc' heirarchical (hclust), 'pam' for paritioning around medoids, 'km' for k-means upon data matrix, 'kmdist' for k-means upon distance matrices (former km option), or a function that returns a clustering.

distance

character value. 'pearson': (1 - Pearson correlation), 'spearman' (1 - Spearman correlation), 'euclidean', 'binary', 'maximum', 'canberra', 'minkowski" or custom distance function.

innerLinkage

Please refer to the "ConsensusClusterPlus" package for detailed information.

seed

optional numerical value. sets random seed for reproducible results.

plot

Please refer to the "ConsensusClusterPlus" package for detailed information.

Value

A list of length maxK. Each element is a list containing consensusMatrix (numerical matrix), consensusTree (hclust), consensusClass (consensus class asssignments).

See also

ConsensusClusterPlus

Examples

data(mRNAexp)
data(miRNAexp)
data(lncRNAexp)
data(methylation)
mRNAexp=data.filter(mRNAexp,percentage=0.6)
miRNAexp=data.filter(miRNAexp,percentage=0.6)
lncRNAexp=data.filter(lncRNAexp,percentage=0.6)
mRNAexp[mRNAexp==0]<-NA
miRNAexp[miRNAexp==0]<-NA
lncRNAexp[lncRNAexp==0]<-NA
mRNAexp<-as.matrix(mRNAexp)
miRNAexp<-as.matrix(miRNAexp)
lncRNAexp<-as.matrix(lncRNAexp)
mRNAexp=data.imputation(mRNAexp,fun="knn")
miRNAexp=data.imputation(miRNAexp,fun="knn")
lncRNAexp=data.imputation(lncRNAexp,fun="knn")
results = Cluster_of_cluster(mRNAexp,miRNAexp,lncRNAexp,methylation,maxK=6,reps=50,pItem=0.8,pFeature=1,clusterAlg="hc",distance="pearson",innerLinkage="complete",seed=1262118388.71279,plot="pdf")
#> end fraction
#> clustered
#> clustered
#> clustered
#> clustered
#> clustered
#> end fraction
#> clustered
#> clustered
#> clustered
#> clustered
#> clustered
#> end fraction
#> clustered
#> clustered
#> clustered
#> clustered
#> clustered
#> end fraction
#> clustered
#> clustered
#> clustered
#> clustered
#> clustered
#> end fraction
#> clustered
#> clustered
#> clustered
#> clustered
#> clustered