GOSim (version 1.10.0)

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

Description

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

Usage

evaluateClustering(clust, Sim)

Arguments

clust

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

Sim

similarity matrix

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

Details

If necessary, more details than the description above

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

	
	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