## ----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