run.consensus.cluster: Runs Consensus Clustering Algorithm
In blogsdon/metanetwork: Network meta analysis

View source: R/run.consensus.clustering.R

run.consensus.cluster

R Documentation

Runs Consensus Clustering Algorithm

Description

A modiefed parallel version of code imported from https://github.com/Bioconductor-mirror/ConsensusClusterPlus 1.11.1. Taken out of findModules.consensusCluster.R

Usage

run.consensus.cluster(
  d,
  kGrid = NULL,
  repCount = NULL,
  diss = inherits(d, "dist"),
  pItem = NULL,
  pFeature = NULL,
  innerLinkage = NULL,
  distance = NULL,
  clusterAlg = NULL,
  weightsItem = NULL,
  weightsFeature = NULL,
  verbose = NULL,
  corUse = NULL
)

Arguments

`d`	Required. A matrix where columns=items/samples and rows are features. For example, a gene expression matrix of genes in rows and microarrays in columns. OR ExpressionSet object. (Default = NULL)
`kGrid`	Optional. (Default = NULL)
`repCount`	Optional. Replicate count. (Default = NULL)
`diss`	Optional. A distance matrix of d. (Default = inherits( d, "dist" ))
`pItem`	Optional. A numerical value. proportion of items to sample. (Default = 0.8)
`pFeature`	Optional. A numerical value. proportion of features to sample. (Default = 1)
`innerLinkage`	Optional. A heirarchical linkage method for subsampling. (Default = "average")
`distance`	Optional. A character value. sample distance measures: "pearson","spearman", or "euclidean". (Default = "pearson")
`clusterAlg`	Optional. A character value. cluster algorithm. "hc" heirarchical (hclust) or "km" for kmeans. (Default = "kmeans")
`weightsItem`	Optional. A numerical vector. weights to be used for sampling items. (Default = NULL)
`weightsFeature`	Optional. AN umerical vector. weights to be used for sampling features. (Default = NULL)
`verbose`	Optional. A boolean when set to TRUE, prints messages to the screen to indicate progress. This is useful for large datasets.(Default = FALSE)
`corUse`	Optional. Use all cores avaiable. (Default = "Everything")