## ----setup,include=FALSE------------------------------------------------------ # load library(ViSEAGO) # knitr document options knitr::opts_chunk$set( eval=FALSE,fig.path='./data/output/',echo=TRUE,fig.pos = 'H', fig.width=8,message=FALSE,comment=NA,warning=FALSE ) ## ----vignette_data_used------------------------------------------------------- # # load vignette data # data( # myGOs, # package="ViSEAGO" # ) ## ----SS_build,eval=FALSE------------------------------------------------------ # # compute Semantic Similarity (SS) # myGOs<-ViSEAGO::compute_SS_distances( # myGOs, # distance=c("Resnik","Lin","Rel","Jiang","Wang") # ) ## ----SS_terms_Resnik-wardD2--------------------------------------------------- # # 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 # ) ## ----SS_Lin-wardD2------------------------------------------------------------ # # 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 # ) ## ----SS_ Rel-wardD2----------------------------------------------------------- # # 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 # ) ## ----SS_Jiang-wardD2---------------------------------------------------------- # # 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 # ) ## ----SS_Wang-wardD2----------------------------------------------------------- # # 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 # ) ## ----parameters_dend_correlation---------------------------------------------- # # build the list of trees # dend<- dendextend::dendlist( # "Resnik"=slot(Resnik_clusters_wardD2,"dendrograms")$GO, # "Lin"=slot(Lin_clusters_wardD2,"dendrograms")$GO, # "Rel"=slot(Rel_clusters_wardD2,"dendrograms")$GO, # "Jiang"=slot(Jiang_clusters_wardD2,"dendrograms")$GO, # "Wang"=slot(Wang_clusters_wardD2,"dendrograms")$GO # ) # # # build the trees matrix correlation # dend_cor<-dendextend::cor.dendlist(dend) ## ----parameters_dend_correlation_print---------------------------------------- # # corrplot # corrplot::corrplot( # dend_cor, # "pie", # "lower", # is.corr=FALSEALSE, # cl.lim=c(0,1) # ) ## ----parameters_dend_comparison,fig.cap="dendrograms comparison"-------------- # # dendrogram list # dl<-dendextend::dendlist( # slot(Resnik_clusters_wardD2,"dendrograms")$GO, # slot(Wang_clusters_wardD2,"dendrograms")$GO # ) # # # untangle the trees (efficient but very highly time consuming) # tangle<-dendextend::untangle( # dl, # "step2side" # ) # # # display the entanglement # dendextend::entanglement(tangle) # 0.08362968 # # # display the tanglegram # dendextend::tanglegram( # tangle, # margin_inner=5, # edge.lwd=1, # lwd = 1, # lab.cex=0.8, # columns_width = c(5,2,5), # common_subtrees_color_lines=FALSE # ) ## ----parameters_clusters_correlation------------------------------------------ # # 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" # ) # # # global dendrogram partition correlation # clust_cor<-ViSEAGO::clusters_cor( # clusters, # method="adjusted.rand" # ) ## ----parameters_clusters_correlation_print------------------------------------ # # global dendrogram partition correlation # corrplot::corrplot( # clust_cor, # "pie", # "lower", # is.corr=FALSEALSE, # cl.lim=c(0,1) # ) ## ----parameters_clusters_comparison,fig.height=8------------------------------ # # clusters content comparisons # ViSEAGO::compare_clusters(clusters)