getConsensusClustering: Get subtypes from ConsensusClustering
In xlucpu/MOVICS: Multi-Omics integration and VIsualization in Cancer Subtyping

Description Usage Arguments Value References Examples

This function wraps the Consensus Clustering algorithm and provides standard output for 'getMoHeatmap()' and 'getConsensusMOIC()'.

getConsensusClustering(
  data = NULL,
  N.clust = NULL,
  type = rep("gaussian", length(data)),
  norMethod = "none",
  reps = 500,
  pItem = 0.8,
  pFeature = 0.8,
  clusterAlg = "hc",
  innerLinkage = "ward.D",
  finalLinkage = "ward.D",
  distance = "pearson",
  plot = NULL,
  writeTable = F,
  title = file.path(getwd(), "consensuscluster"),
  seed = 123456,
  verbose = F
)

`data`	List of matrices.
`N.clust`	Number of clusters.
`type`	Data type corresponding to the list of matrics, which can be gaussian, binomial or possion.
`norMethod`	A string vector indicate the normalization method for consensus clustering.
`reps`	An integer value to indicate the number of subsamples.
`pItem`	A numerical value to indicate the proportion of items to sample.
`pFeature`	A numerical value to indicate the proportion of features to sample.
`clusterAlg`	A string value to indicate the cluster algorithm.
`innerLinkage`	A string value to indicate the heirachical linakge method for subsampling.
`finalLinkage`	A string value to indicate the heirarchical method for consensus matrix.
`distance`	A string value to indicate the distance function.
`plot`	A string value to indicate the output format for heatmap.
`writeTable`	A logical value to indicate if writing output and log to csv.
`title`	A string value for output directory.
`seed`	A numerical value to set random seed for reproducible results.
`verbose`	A logical value to indicate if printing messages to the screen to indicate progress.

A list with the following components:

fit an object returned by ConsensusClusterPlus.

clust.res a data.frame storing sample ID and corresponding clusters.

clust.dend a dendrogram of sample clustering.

mo.method a string value indicating the method used for multi-omics integrative clustering.

Monti S, Tamayo P, Mesirov J, et al (2003). Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Mach Learn, 52:91-118.