evaluateClustering: Evaluate a given grouping of genes or GO terms.
In GOSim: Computation of functional similarities between GO terms and gene products; GO enrichment analysis

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

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

1	evaluateClustering(clust, Sim)

`clust`	vector of cluster labels (integer or character) for each gene
`Sim`	similarity matrix

If necessary, more details than the description above

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

Holger Froehlich

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

getGeneSimPrototypes, getGeneSim, getTermSim, GOenrichment

	
	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