inst/doc/ViSEAGO.R

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

## ----setup,include=FALSE------------------------------------------------------
# koad ViSEAGO
library(ViSEAGO)

# knitr document options
knitr::opts_chunk$set(
  eval=FALSE,echo=TRUE,fig.pos = 'H',
  fig.width=8,message=FALSE,comment=NA,warning=FALSE
)

## ----ViSEAGO_install----------------------------------------------------------
#  # Install ViSEAGO package from Bioconductor
#  BiocManager::install("ViSEAGO")

## ----geneList_input_topGO-----------------------------------------------------
#  # load genes background
#  background<-scan(
#      "background.txt",
#      quiet=TRUE,
#      what=""
#  )
#  
#  # load gene selection
#  selection<-scan(
#      "selection.txt",
#      quiet=TRUE,
#      what=""
#  )

## ----geneList_input_fgsea-----------------------------------------------------
#  # load gene identifiers column 1) and corresponding statistical value (column 2)
#  table<-data.table::fread("table.txt")
#  
#  # rank gene identifiers according statistical value
#  data.table::setorder(table,value)

## ----databases----------------------------------------------------------------
#  # connect to Bioconductor
#  Bioconductor<-ViSEAGO::Bioconductor2GO()
#  
#  # connect to EntrezGene
#  EntrezGene<-ViSEAGO::EntrezGene2GO()
#  
#  # connect to Ensembl
#  Ensembl<-ViSEAGO::Ensembl2GO()
#  
#  # connect to Uniprot-GOA
#  Uniprot<-ViSEAGO::Uniprot2GO()
#  
#  # connect to Custom file
#  Custom<-ViSEAGO::Custom2GO(system.file("extdata/customfile.txt",package = "ViSEAGO"))

## ----organisms----------------------------------------------------------------
#  # Display table of available organisms with Bioconductor
#  ViSEAGO::available_organisms(Bioconductor)
#  
#  # Display table of available organisms with EntrezGene
#  ViSEAGO::available_organisms(EntrezGene)
#  
#  # Display table of available organisms with Ensembl
#  ViSEAGO::available_organisms(Ensembl)
#  
#  # Display table of available organisms with Uniprot
#  ViSEAGO::available_organisms(Uniprot)
#  
#  # Display table of available organisms with Custom
#  ViSEAGO::available_organisms(Custom)

## ----annotate-----------------------------------------------------------------
#  # load GO annotations from Bioconductor
#  myGENE2GO<-ViSEAGO::annotate(
#      "bioconductor_id",
#      Bioconductor
#  )
#  
#  # load GO annotations from EntrezGene
#  myGENE2GO<-ViSEAGO::annotate(
#      "EntrezGene_id",
#      EntrezGene
#  )
#  
#  # load GO annotations from EntrezGene
#  # with the add of GO annotations from orthologs genes (see above)
#  myGENE2GO<-ViSEAGO::annotate(
#      "EntrezGene_id",
#      EntrezGene,
#      ortholog = TRUE
#  )
#  
#  # load GO annotations from Ensembl
#  myGENE2GO<-ViSEAGO::annotate(
#      "Ensembl_id",
#      Ensembl
#  )
#  
#  # load GO annotations from Uniprot
#  myGENE2GO<-ViSEAGO::annotate(
#      "Uniprot_id",
#      Uniprot
#  )
#  
#  # load GO annotations from Custom
#  myGENE2GO<-ViSEAGO::annotate(
#      "Custom_id",
#      Custom
#  )

## ----Enrichment_data----------------------------------------------------------
#  # create topGOdata for BP
#  BP<-ViSEAGO::create_topGOdata(
#      geneSel=selection,
#      allGenes=background,
#      gene2GO=myGENE2GO,
#      ont="BP",
#      nodeSize=5
#  )

## ----Enrichment_data_tests----------------------------------------------------
#  # perform TopGO test using clasic algorithm
#  classic<-topGO::runTest(
#      BP,
#      algorithm ="classic",
#      statistic = "fisher"
#  )

## ----fgsea--------------------------------------------------------------------
#  # perform fgseaMultilevel tests
#  BP<-ViSEAGO::runfgsea(
#      geneSel=table,
#      ont="BP",
#      gene2GO=myGENE2GO,
#      method ="fgseaMultilevel",
#      params = list(
#          scoreType = "pos",
#           minSize=5
#      )
#  )

## ----Enrichment_merge---------------------------------------------------------
#  # merge results from topGO
#  BP_sResults<-ViSEAGO::merge_enrich_terms(
#      Input=list(
#          condition=c("BP","classic")
#      )
#  )
#  
#  # merge results from fgsea
#  BP_sResults<-ViSEAGO::merge_enrich_terms(
#      Input=list(
#          condition="BP"
#      )
#  )

## ----Enrichment_merge_display-------------------------------------------------
#  # display the merged table
#  ViSEAGO::show_table(BP_sResults)
#  
#  # print the merged table in a file
#  ViSEAGO::show_table(
#      BP_sResults,
#      "BP_sResults.xls"
#  )

## ----Enrichment_merge_count---------------------------------------------------
#  # count significant (or not) pvalues by condition
#  ViSEAGO::GOcount(BP_sResults)

## ----Enrichment_merge_interactions,fig.height=4-------------------------------
#  # display interactions
#  ViSEAGO::Upset(
#      BP_sResults,
#      file="OLexport.xls"
#  )

## ----SS_build-----------------------------------------------------------------
#  # initialyse
#  myGOs<-ViSEAGO::build_GO_SS(
#      gene2GO=myGENE2GO,
#      enrich_GO_terms=BP_sResults
#  )
#  
#  # compute all available Semantic Similarity (SS) measures
#  myGOs<-ViSEAGO::compute_SS_distances(
#      myGOs,
#      distance="Wang"
#  )

## ----SS_terms_mdsplot,eval=FALSE----------------------------------------------
#  # display MDSplot
#  ViSEAGO::MDSplot(myGOs)
#  
#  # print MDSplot
#  ViSEAGO::MDSplot(
#      myGOs,
#      file="mdsplot1.png"
#  )

## ----SS_Wang-wardD2-----------------------------------------------------------
#  # GOterms heatmap with the default parameters
#  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(
#                  pamStage=TRUE,
#                  pamRespectsDendro=TRUE,
#                  deepSplit=2,
#                  minClusterSize =2
#              )
#          )
#      ),
#      samples.tree=NULL
#  )

## ----SS_Wang-wardD2_clusters-heatmap------------------------------------------
#  # Display the clusters-heatmap
#  ViSEAGO::show_heatmap(
#      Wang_clusters_wardD2,
#      "GOterms"
#  )
#  
#  # print the clusters-heatmap
#  ViSEAGO::show_heatmap(
#      Wang_clusters_wardD2,
#      "GOterms",
#      "cluster_heatmap_Wang_wardD2.png"
#  )

## ----SS_Wang-ward.D2_clusters-heatmap_table-----------------------------------
#  # Display the clusters-heatmap table
#  ViSEAGO::show_table(Wang_clusters_wardD2)
#  
#  # Print the clusters-heatmap table
#  ViSEAGO::show_table(
#      Wang_clusters_wardD2,
#      "cluster_heatmap_Wang_wardD2.xls"
#  )

## ----SS_Wang-ward.D2_mdsplot,eval=FALSE---------------------------------------
#  # display colored MDSplot
#  ViSEAGO::MDSplot(
#      Wang_clusters_wardD2,
#      "GOterms"
#  )
#  
#  # print colored MDSplot
#  ViSEAGO::MDSplot(
#      Wang_clusters_wardD2,
#      "GOterms",
#      file="mdsplot2.png"
#  )

## ----SS_Wang-wardD2_groups----------------------------------------------------
#  # calculate semantic similarites between clusters of GO terms
#  Wang_clusters_wardD2<-ViSEAGO::compute_SS_distances(
#      Wang_clusters_wardD2,
#      distance=c("max", "avg","rcmax", "BMA")
#  )

## ----SS_Wang-ward.D2_groups_mdsplot-------------------------------------------
#  # build and highlight in an interactive MDSplot grouped clusters for one distance object
#  ViSEAGO::MDSplot(
#      Wang_clusters_wardD2,
#      "GOclusters"
#  )
#  
#  # build and highlight in MDSplot grouped clusters for one distance object
#  ViSEAGO::MDSplot(
#      Wang_clusters_wardD2,
#      "GOclusters",
#      file="mdsplot3.png"
#  )

## ----SS_Wang-wardD2_groups_heatmap--------------------------------------------
#  # GOclusters heatmap
#  Wang_clusters_wardD2<-ViSEAGO::GOclusters_heatmap(
#      Wang_clusters_wardD2,
#      tree=list(
#          distance="BMA",
#          aggreg.method="ward.D2"
#      )
#  )

## ----SS_Wang-ward.D2_groups_heatmap_display-----------------------------------
#  # sisplay the GOClusters heatmap
#  ViSEAGO::show_heatmap(
#      Wang_clusters_wardD2,
#      "GOclusters"
#  )
#  
#  # print the GOClusters heatmap in a file
#  ViSEAGO::show_heatmap(
#      Wang_clusters_wardD2,
#      "GOclusters",
#      "Wang_clusters_wardD2_heatmap_groups.png"
#  )

## ----session,eval=TRUE,echo=FALSE---------------------------------------------
version