consensus.COCA: Build a consensus classification using the COCA method

In cit-bioinfo/BuildConsensus: Build a consensus classification from multiple classification methods

Description

Compare and cluster various classification systems using a "Cluster of Cluster" approach

Usage

consensus.COCA(annot.class, outdir, maxK = 8, reps = 100, seed = 12,
  innerLinkage = "ward.D2", finalLinkage = "ward.D2",
  distance = "euclidean", ...)

Arguments

annot.class	a dataframe of samples annotated according to the several classification systems to compare.
outdir	the path to the directory where to store plots and results. A new directory will be created if the supplied path does not exist.
maxK	an integer value that aximum cluster number to be evaluated.
reps	an integer value that represents the number of repetitions to be performed.
seed	an integer. Used for result reproducibility. See set.seed
innerLinkage	a linkage method for constructing the hierarchical clustering. See hclust for linkage methods.
finalLinkage	a linkage method for constructing the final clustering. See hclust for linkage methods.
distance	a distance metric to evaluate the samples. See dist for methods.
...	other parameters passed to ConsensusClusterPlus

Author(s)

Aurelie Kamoun

Examples

## Not run: 
 #-- Using bladder cancer classes as example
 library(BuildConsensus)
 data(blca_class)
 
 coca_res <- consensus.coca(blca_class[sample(nrow(blca_class), 500),], outdir = "coca_res", maxK = 5)

## End(Not run)

cit-bioinfo/BuildConsensus documentation built on Nov. 27, 2019, 11:29 a.m.