clusters_cor {ViSEAGO} | R Documentation |
Build a distance or correlation matrix between partitions from dendrograms.
clusters_cor(clusters, method = "adjusted.rand") ## S4 method for signature 'list,character' clusters_cor(clusters, method = "adjusted.rand")
clusters |
a |
method |
available methods ("vi", "nmi", "split.join", "rand", or "adjusted.rand") from igraph package |
a distance or a correlation matrix.
Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.org.
Other GO_clusters:
GO_clusters-class
,
GOclusters_heatmap()
,
compare_clusters()
,
show_heatmap()
,
show_table()
# load example object data( myGOs, package="ViSEAGO" ) ## Not run: # compute Semantic Similarity (SS) myGOs<-ViSEAGO::compute_SS_distances( myGOs, distance=c("Resnik","Lin","Rel","Jiang","Wang") ) # Resnik distance GO terms heatmap Resnik_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Resnik", aggreg.method="ward.D2" ), cut=list( dynamic=list( deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL ) # Lin distance GO terms heatmap Lin_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Lin", aggreg.method="ward.D2" ), cut=list( dynamic=list( deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL ) # Resnik distance GO terms heatmap Rel_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Rel", aggreg.method="ward.D2" ), cut=list( dynamic=list( deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL ) # Resnik distance GO terms heatmap Jiang_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Jiang", aggreg.method="ward.D2" ), cut=list( dynamic=list( deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL ) # Resnik distance GO terms heatmap Wang_clusters_wardD2<-ViSEAGO::GOterms_heatmap( myGOs, showIC=TRUE, showGOlabels=TRUE, GO.tree=list( tree=list( distance="Wang", aggreg.method="ward.D2" ), cut=list( dynamic=list( deepSplit=2, minClusterSize =2 ) ) ), samples.tree=NULL ) ## End(Not run) # clusters to compare clusters<-list( Resnik="Resnik_clusters_wardD2", Lin="Lin_clusters_wardD2", Rel="Rel_clusters_wardD2", Jiang="Jiang_clusters_wardD2", Wang="Wang_clusters_wardD2" ) ## Not run: # global dendrogram clustering correlation clust_cor<-ViSEAGO::clusters_cor( clusters, method="adjusted.rand" ) ## End(Not run)