ccml {ccml}

A two-step consensus clustering inputing multiple predictive labels with different sample coverages (missing labels)

Description

A two-step consensus clustering inputing multiple predictive labels with different sample coverages (missing labels)

Usage

ccml(
  title,
  label,
  output = "rdata",
  nperm = 10,
  ncore = 1,
  seedn = 100,
  stability = TRUE,
  maxK = 15,
  reps = 1000,
  pItem = 0.9,
  plot = NULL,
  clusterAlg = "spectralClusteringAffinity",
  innerLinkage = "complete",
  ...
)

Arguments

title

A character value for output directory. Directory is created only if not existed. This title can be an abosulte or relative path. Input for callNCW, plotCompareCW, ConsensusClusterPlus::ConsensusClusterPlus, ConsensusClusterPlus::calcICL

label

A matrix or data frame of input labels or a character value of input file name, columns=different clustering results and rows are samples. label could be import as '.rdata', '.rda', or '.csv'. Input for callNCW, plotCompareCW

output

A character value for output format, or "rdata"(default) as save to .rdata when both output and plot are not NULL, others will return to workspace.

nperm

A integer value of the permutation numbers, or nperm=10(default), which means nperm*1000 times of permutation. Input for callNCW

ncore

A integer value of cores to use, or ncore=1 (default). It's the input core numbers for the parallel computation in this package parallel. Input for callNCW

seedn

A numerical value to set the start random seed for reproducible results, or seedn=100 (default). For every 1000 iteration, the seed will +1 to get repeat results. Input for callNCW, ConsensusClusterPlus::ConsensusClusterPlus

stability

A logical value. Should estimate the stability of normalized consensus weight based on permutation numbers (default stability=TRUE), or not? Input for callNCW

maxK

integer value. maximum cluster number to evaluate. Input for ConsensusClusterPlus::ConsensusClusterPlus for the consensus clustering based on normalized consensus weights.

reps

integer value. number of subsamples. Input for ConsensusClusterPlus::ConsensusClusterPlus

pItem

numerical value. proportion of items to sample. Input for ConsensusClusterPlus::ConsensusClusterPlus

plot

character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets. Input for ConsensusClusterPlus::ConsensusClusterPlus, ConsensusClusterPlus::calcICL,callNCW,plotCompareCW

clusterAlg

character value. cluster algorithm. 'spectralClusteringAffinity' for spectral clustering of similarity/affinity matrix(default), other methods for clustering of distance matrix, '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. Input for ConsensusClusterPlus::ConsensusClusterPlus.

innerLinkage

heirarchical linkage method for subsampling, or "complete"(default). Input for ConsensusClusterPlus::ConsensusClusterPlus

...

Other input arguments for ConsensusClusterPlus::ConsensusClusterPlus

Value

A list of three items

• ncw - A matrix of normalized consensus weights. Output from callNCW.

• fcluster - A list of length maxK. Each element is a list containing consensusMatrix (numerical matrix), consensusTree (hclust), consensusClass (consensus class asssignments). ConsensusClusterPlus also produces images. Output from ConsensusClusterPlus::ConsensusClusterPlus

• icl a list of two elements clusterConsensus and itemConsensus corresponding to cluster-consensus and item-consensus. Output from ConsensusClusterPlus::ConsensusClusterPlus

Examples

# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


# not estimate stability of permutation numbers.
res_1=ccml(title=title,label=label,nperm = 3,ncore=1,stability=FALSE,maxK=5,pItem=0.8)

# other methods for clustering of distance matrix
res_2<-ccml(title=title,label=label,nperm = 10,ncore=1,stability=TRUE,maxK=3,
            pItem=0.9,clusterAlg = "hc")

# set the start random seed
res_3<-ccml(title=title,label=label,output=FALSE,nperm = 5,ncore=1,seedn=150,stability=TRUE,maxK=3,
           pItem=0.9)