inst/doc/SS_choice.R

In ViSEAGO: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

## ----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)