COMMUNAL (version 1.1.0)

COMMUNAL-package: COmbined Mapping of Multiple clUsteriNg ALgorithms

Description

This package allows for identification of optimal clustering for a data set. It provides a framework to run a wide range of clustering algorithms to determine the optimal number (k) of clusters in the data. It then provides a function to analyze the cluster assignments from each clustering algorithm to identify samples that repeatedly classify to the same group. We call these 'core clusters,' leading to optimal beds for later class discovery.

Details

Package:

COMMUNAL

Type:

Package

Version:

1.1

Date:

2015-08-12

License:

GPL-2

Imports:

clValid, fpc, methods

Depends:

R (>= 2.10), cluster

Suggests:

RUnit, NMF, ConsensusClusterPlus, rgl

Start with a matrix of data to cluster. Important functions are: COMMUNAL to run clustering algorithms once clusterRange to run COMMUNAL across increasing subsets of data getGoodAlgs to identify robust algorithms from clusterRange getNonCorrNonMonoMeasures to identify non-monotonic, non-correlated validity measures from clusterRange plotRange3D to pick k clusterKeys to identify core clusters returnCore to identify core clusters

Examples

## Not run: 
# ## create artificial data set with 3 distinct clusters
# set.seed(1)
# V1 = c(abs(rnorm(100, 2)), abs(rnorm(100, 50)), abs(rnorm(100, 140)))
# V2 = c(abs(rnorm(100, 2, 8)), abs(rnorm(100, 55, 4)), abs(rnorm(100, 105, 1)))
# data <- t(data.frame(V1, V2))
# colnames(data) <- paste("Sample", 1:ncol(data), sep="")
# rownames(data) <- paste("Gene", 1:nrow(data), sep="")
# 
# ## run COMMUNAL
# result <- COMMUNAL(data=data, ks=seq(2,5))  # result is a COMMUNAL object
# k <- 3                                # suppose optimal cluster number is 3
# clusters <- result$getClustering(k)   # method to extract clusters
# mat.key <- clusterKeys(clusters)      # get core clusters
# examineCounts(mat.key)                # help decide agreement.thresh
# core <- returnCore(mat.key, agreement.thresh=50) # find 'core' clusters (all algs agree)
# table(core) # the 'core' clusters
# 
# ## Additional arguments are passed down to clValid, NMF, ConsensusClusterPlus
# result <- COMMUNAL(data=data, ks=2:5,
#                       clus.methods=c("diana", "ccp-hc", "nmf"), reps=20, nruns=2)
# 
# ## To identify k, use clusterRange and plotRange3D to visualize validation measures
# data(BRCA.100) # 533 tissues to cluster, with measurements of 100 genes each
# varRange <- c(10,25,50,75,100)
# meas <- c("Connectivity", "average.between",
#           "ch", "sindex", "avg.silwidth", 
#           "average.within", "dunn", "widestgap",
#           "wb.ratio", "entropy", "dunn2", 
#           "pearsongamma", "g3", "within.cluster.ss", 
#           "min.separation", "max.diameter")
# BRCA.results <- clusterRange(BRCA.100, ks=2:6, varRange=varRange, validation=meas)
# 
# goodMeasures <- getNonCorrNonMonoMeasures(BRCA.results)
# goodAlgs <- getGoodAlgs(BRCA.results)
# 
# plot.data <- plotRange3D(BRCA.results, goodAlgs=goodAlgs, goodMeasures = goodMeasures)
# ## End(Not run)