evaluateClustering: Evaluate a given grouping of genes or GO terms.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/clusterEvaluation.R

Description

Evaluate a given grouping of genes or terms with respect to their GO similarity.

Usage

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evaluateClustering(clust, Sim)

Arguments

clust

vector of cluster labels (integer or character) for each gene

Sim

similarity matrix

Details

If necessary, more details than the description above

Value

evaluateClustering returns a list with two items:

clusterstats

matrix (ncluster x 2) of median within cluster similarities and median absolute deviations

clustersil

cluster silhouette values

Author(s)

Holger Froehlich

References

Rousseeuw, P., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comp. and Applied Mathematics, 1987, 20, 53-6

See Also

getGeneSimPrototypes, getGeneSim, getTermSim, GOenrichment

Examples

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	setOntology("BP")
	gomap <- get("gomap",env=GOSimEnv)
	allgenes = sample(names(gomap), 1000) # suppose these are all genes
	genesOfInterest = sample(allgenes, 20) # suppose these are all genes of interest
	
	sim = getGeneSim(genesOfInterest,verbose=FALSE) # and these are their similarities
	hc = hclust(as.dist(1-sim), method="ward") # use them to perform a clustering
	plot(hc) # plot the cluster tree
	cl = cutree(hc, k=3) # take 3 clusters	

	if(require(cluster)){
		ev = evaluateClustering(cl, sim) # evaluate the clustering
		print(ev$clusterstats) # print out some statistics
		plot(ev$clustersil,main="") # plot the cluster silhouettes
	}
	
	# investigate cluster 1 further	
	if(require(topGO))
		GOenrichment(genesOfInterest[cl == 1], allgenes, cutoff=0.05) # print out what cluster 1 is about

GOSim documentation built on Nov. 1, 2018, 2:26 a.m.