clusterEvaluation {GOSim} | R Documentation |
Evaluate a given grouping of genes or terms with respect to their GO similarity.
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