ExecuteSNF.CC: Execute the combined SNF (Similarity Network Fusion) and...

Description Usage Arguments Value See Also Examples

View source: R/ClusteringMethod.R

Description

This function is a combined process of SNF and Consensus Clustering for cancer subtypes identification. First it applied SNF to get the fusion patients similarity matrix. Then use this fusion patients similarity matrix as the sample distance for Consensus Clustering.

Usage

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ExecuteSNF.CC(datasets, clusterNum, K = 20, alpha = 0.5, t = 20,
  maxK = 10, pItem = 0.8, reps = 500, title = "ConsensusClusterResult",
  plot = "png", finalLinkage = "average")

Arguments

datasets

A list containing data matrices. For each data matrix, the rows represent genomic features, and the columns represent samples. Same as ExecuteSNF

clusterNum

A integer representing the return cluster number. Same as ExecuteSNF

K

Number of nearest neighbors. Same as ExecuteSNF

alpha

Variance for local model. Same as ExecuteSNF

t

Number of iterations for the diffusion process. Same as ExecuteSNF

maxK

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

pItem

Same as ExecuteCC

reps

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

title

character value for output directory. This title can be an absolute or relative path. Same as ExecuteCC

plot

Same as ExecuteCC

finalLinkage

Same as ExecuteCC

Value

Same as the ExecuteCC(). A list with the following elements.

See Also

ExecuteSNF ExecuteCC

Examples

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data(GeneExp)
data(miRNAExp)
GBM=list(GeneExp,miRNAExp)
result=ExecuteSNF.CC(GBM, clusterNum=3, K=20, alpha=0.5, t=20,
                    maxK = 5, pItem = 0.8,reps=500, 
                    title = "GBM", plot = "png", 
                    finalLinkage ="average")
result$group

CancerSubtypes documentation built on Nov. 8, 2020, 8:24 p.m.