## ---- echo = FALSE------------------------------------------------------------
knitr::opts_chunk$set(
  eval=FALSE
)

## -----------------------------------------------------------------------------
#  if(!"rWikiPathways" %in% installed.packages()){
#      if (!requireNamespace("BiocManager", quietly = TRUE))
#          install.packages("BiocManager")
#      BiocManager::install("rWikiPathways", update = FALSE)
#  }
#  library(rWikiPathways)

## -----------------------------------------------------------------------------
#  load.libs <- c(
#    "DOSE",
#    "GO.db",
#    "GSEABase",
#    "org.Hs.eg.db",
#    "clusterProfiler",
#    "dplyr",
#    "tidyr",
#    "ggplot2",
#    "stringr",
#    "RColorBrewer",
#    "rWikiPathways",
#    "RCy3")
#  options(install.packages.check.source = "no")
#  options(install.packages.compile.from.source = "never")
#  if (!require("pacman")) install.packages("pacman"); library(pacman)
#  p_load(load.libs, update = TRUE, character.only = TRUE)
#  status <- sapply(load.libs,require,character.only = TRUE)
#  if(all(status)){
#      print("SUCCESS: You have successfully installed and loaded all required libraries.")
#  } else{
#      cat("ERROR: One or more libraries failed to install correctly. Check the following list for FALSE cases and try again...\n\n")
#      status
#  }

## -----------------------------------------------------------------------------
#  cytoscapePing()  #this will tell you if you're able to successfully connect to Cytoscape or not

## -----------------------------------------------------------------------------
#  installApp('WikiPathways')
#  installApp('CyTargetLinker')
#  installApp('stringApp')
#  installApp('enrichmentMap')

## -----------------------------------------------------------------------------
#  lung.expr <- read.csv(system.file("extdata","data-lung-cancer.csv", package="rWikiPathways"),stringsAsFactors = FALSE)
#  nrow(lung.expr)
#  head(lung.expr)

## -----------------------------------------------------------------------------
#  up.genes <- lung.expr[lung.expr$log2FC > 1 & lung.expr$adj.P.Value < 0.05, 1]
#  dn.genes <- lung.expr[lung.expr$log2FC < -1 & lung.expr$adj.P.Value < 0.05, 1]
#  bkgd.genes <- lung.expr[,1]

## -----------------------------------------------------------------------------
#  up.genes.entrez <- clusterProfiler::bitr(up.genes,fromType = "ENSEMBL",toType = "ENTREZID",OrgDb = org.Hs.eg.db)
#  cat("\n\nWhich column contains my new Entrez IDs?\n")
#  head(up.genes.entrez)

## -----------------------------------------------------------------------------
#  keytypes(org.Hs.eg.db)

## -----------------------------------------------------------------------------
#  dn.genes.entrez <- bitr(dn.genes,fromType = "ENSEMBL",toType = "ENTREZID",OrgDb = org.Hs.eg.db)
#  bkgd.genes.entrez <- bitr(bkgd.genes,fromType = "ENSEMBL",toType = "ENTREZID",OrgDb = org.Hs.eg.db)

## -----------------------------------------------------------------------------
#  egobp <- clusterProfiler::enrichGO(
#          gene     = up.genes.entrez[[2]],
#          universe = bkgd.genes.entrez[[2]],
#          OrgDb    = org.Hs.eg.db,
#          ont      = "BP",
#          pAdjustMethod = "fdr",
#          pvalueCutoff = 0.05, #p.adjust cutoff (https://github.com/GuangchuangYu/clusterProfiler/issues/104)
#          readable = TRUE)
#  
#  head(egobp,10)

## -----------------------------------------------------------------------------
#  barplot(egobp, showCategory = 20)
#  dotplot(egobp, showCategory = 20)
#  emapplot(egobp, showCategory = 20)
#  goplot(egobp)

## -----------------------------------------------------------------------------
#  ggplot(egobp[1:20], aes(x=reorder(Description, -pvalue), y=Count, fill=-p.adjust)) +
#      geom_bar(stat = "identity") +
#      coord_flip() +
#      scale_fill_continuous(low="blue", high="red") +
#      labs(x = "", y = "", fill = "p.adjust") +
#      theme(axis.text=element_text(size=11))

## -----------------------------------------------------------------------------
#  ## extract a dataframe with results from object of type enrichResult
#  egobp.results.df <- egobp@result
#  
#  ## create a new column for term size from BgRatio
#  egobp.results.df$term.size <- gsub("/(\\d+)", "", egobp.results.df$BgRatio)
#  
#  ## filter for term size to keep only term.size => 3, gene count >= 5 and subset
#  egobp.results.df <- egobp.results.df[which(egobp.results.df[,'term.size'] >= 3 & egobp.results.df[,'Count'] >= 5),]
#  egobp.results.df <- egobp.results.df[c("ID", "Description", "pvalue", "qvalue", "geneID")]
#  
#  ## format gene list column
#  egobp.results.df$geneID <- gsub("/", ",", egobp.results.df$geneID)
#  
#  ## add column for phenotype
#  egobp.results.df <- cbind(egobp.results.df, phenotype=1)
#  egobp.results.df <- egobp.results.df[, c(1, 2, 3, 4, 6, 5)]
#  
#  ## change column headers
#  colnames(egobp.results.df) <- c("Name","Description", "pvalue","qvalue","phenotype", "genes")
#  
#  egobp.results.filename <-file.path(getwd(),paste("clusterprofiler_cluster_enr_results.txt",sep="_"))
#  write.table(egobp.results.df,egobp.results.filename,col.name=TRUE,sep="\t",row.names=FALSE,quote=FALSE)

## ----eval=FALSE---------------------------------------------------------------
#  em_command = paste('enrichmentmap build analysisType="generic" ',
#                     'pvalue=',"0.05", 'qvalue=',"0.05",
#                     'similaritycutoff=',"0.25",
#                     'coeffecients=',"JACCARD",
#                     'enrichmentsDataset1=',egobp.results.filename ,
#                     sep=" ")
#  
#    #enrichment map command will return the suid of newly created network.
#    em_network_suid <- commandsRun(em_command)
#  
#    renameNetwork("Cluster1_enrichmentmap_cp", network=as.numeric(em_network_suid))

## -----------------------------------------------------------------------------
#  wp.hs.gmt <- rWikiPathways::downloadPathwayArchive(organism="Homo sapiens", format = "gmt")
#  
#  # supported organisms (and case-dependent spelling)...
#  listOrganisms()

## -----------------------------------------------------------------------------
#  wp2gene <- readPathwayGMT(wp.hs.gmt)
#  wpid2gene <- wp2gene %>% dplyr::select(wpid,gene) #TERM2GENE
#  wpid2name <- wp2gene %>% dplyr::select(wpid,name) #TERM2NAME
#  wpid2gene
#  wpid2name

## -----------------------------------------------------------------------------
#  ewp.up <- clusterProfiler::enricher(
#          up.genes.entrez[[2]],
#          universe = bkgd.genes.entrez[[2]],
#          pAdjustMethod = "fdr",
#          pvalueCutoff = 0.1, #p.adjust cutoff; relaxed for demo purposes
#          TERM2GENE = wpid2gene,
#          TERM2NAME = wpid2name)
#  
#  head(ewp.up)

## -----------------------------------------------------------------------------
#  ewp.up <- DOSE::setReadable(ewp.up, org.Hs.eg.db, keyType = "ENTREZID")
#  head(ewp.up)

## -----------------------------------------------------------------------------
#  barplot(ewp.up, showCategory = 20)
#  dotplot(ewp.up, showCategory = 20)
#  emapplot(ewp.up, showCategory = 20)

## -----------------------------------------------------------------------------
#  ewp.dn <- enricher(
#    dn.genes.entrez[[2]],
#    #universe = bkgd.genes[[2]],  #hint: comment out to get any results for demo
#    pAdjustMethod = "fdr",
#    pvalueCutoff = 0.1, #p.adjust cutoff; relaxed for demo purposes
#    TERM2GENE = wpid2gene,
#    TERM2NAME = wpid2name)
#  
#   ewp.dn <- setReadable(ewp.dn, org.Hs.eg.db, keyType = "ENTREZID")
#   head(ewp.dn)
#   dotplot(ewp.dn, showCategory = 20)

## -----------------------------------------------------------------------------
#  lung.expr$fcsign <- sign(lung.expr$log2FC)
#  lung.expr$logfdr <- -log10(lung.expr$P.Value)
#  lung.expr$sig <- lung.expr$logfdr/lung.expr$fcsign
#  sig.lung.expr.entrez<-merge(lung.expr, bkgd.genes.entrez, by.x = "GeneID", by.y = "ENSEMBL")
#  gsea.sig.lung.expr <- sig.lung.expr.entrez[,8]
#  names(gsea.sig.lung.expr) <- as.character(sig.lung.expr.entrez[,9])
#  gsea.sig.lung.expr <- sort(gsea.sig.lung.expr,decreasing = TRUE)
#  
#  gwp.sig.lung.expr <- clusterProfiler::GSEA(
#    gsea.sig.lung.expr,
#    pAdjustMethod = "fdr",
#    pvalueCutoff = 0.05, #p.adjust cutoff
#    TERM2GENE = wpid2gene,
#    TERM2NAME = wpid2name)
#  
#  gwp.sig.lung.expr.df = data.frame(ID=gwp.sig.lung.expr$ID,
#                                   Description=gwp.sig.lung.expr$Description,
#                                   enrichmentScore=gwp.sig.lung.expr$enrichmentScore,
#                                   NES=gwp.sig.lung.expr$NES,
#                                   pvalue=gwp.sig.lung.expr$pvalue,
#                                   p.adjust=gwp.sig.lung.expr$p.adjust,
#                                   rank=gwp.sig.lung.expr$rank,
#                                   leading_edge=gwp.sig.lung.expr$leading_edge
#  )
#  gwp.sig.lung.expr.df[which(gwp.sig.lung.expr.df$NES > 1),] #pathways enriched for upregulated lung cancer genes
#  gwp.sig.lung.expr.df[which(gwp.sig.lung.expr.df$NES < -1),] #pathways enriched for downregulated lung cancer genes

## -----------------------------------------------------------------------------
#  findPathwayNamesByText("lung cancer")

## -----------------------------------------------------------------------------
#  lc.pathways <- findPathwaysByText('"lung cancer"')  #quotes inside query to require both terms
#  human.lc.pathways <- lc.pathways %>%
#    dplyr::filter(species == "Homo sapiens") # just the human lung cancer pathways
#  human.lc.pathways$name # display the pathway titles

## -----------------------------------------------------------------------------
#  lc.wpids <- human.lc.pathways$id
#  lc.wpids

## -----------------------------------------------------------------------------
#  ewp.up.wpids <- ewp.up$ID
#  ewp.up.wpids

## -----------------------------------------------------------------------------
#  url <- getPathwayInfo("WP179")$url
#  browseURL(url)

## -----------------------------------------------------------------------------
#  cytoscapePing()

## -----------------------------------------------------------------------------
#  RCy3::commandsRun('wikipathways import-as-pathway id=WP179')

## -----------------------------------------------------------------------------
#  toggleGraphicsDetails()

## -----------------------------------------------------------------------------
#  loadTableData(lung.expr, data.key.column = "GeneID", table.key.column = "Ensembl")

## -----------------------------------------------------------------------------
#  min.lung.expr = min(lung.expr["log2FC"],na.rm=TRUE)
#  max.lung.expr = max(lung.expr["log2FC"],na.rm=TRUE)
#  abs.lung.expr = max(abs(min.lung.expr),max.lung.expr)
#  data.values = c(-abs.lung.expr,0,abs.lung.expr)

## -----------------------------------------------------------------------------
#  display.brewer.all(length(data.values), colorblindFriendly=TRUE, type="div") # div,qual,seq,all
#  node.colors <- c(rev(brewer.pal(length(data.values), "RdBu")))

## -----------------------------------------------------------------------------
#  setNodeColorMapping("log2FC", data.values, node.colors, default.color = "#FFFFFF", style.name = "WikiPathways")

## -----------------------------------------------------------------------------
#  data.values<-c(-3,0,3)
#  setNodeColorMapping("log2FC", data.values, node.colors, default.color = "#FFFFFF", style.name = "WikiPathways")

## -----------------------------------------------------------------------------
#  lapply(ewp.up.wpids, function (x) {
#      commandsRun(paste0('wikipathways import-as-pathway id=',x))
#      loadTableData(lung.expr, data.key.column = "GeneID", table.key.column = "Ensembl")
#      toggleGraphicsDetails()
#      })

## -----------------------------------------------------------------------------
#  lapply(lc.wpids, function (x){
#      commandsRun(paste0('wikipathways import-as-pathway id=',x))
#      loadTableData(lung.expr, data.key.column = "GeneID", table.key.column = "Ensembl")
#      toggleGraphicsDetails()
#      })

## -----------------------------------------------------------------------------
#  commandsRun('wikipathways import-as-network id=WP179')
#  loadTableData(lung.expr, data.key.column = "GeneID", table.key.column = "Ensembl")
#  setNodeColorMapping("log2FC", data.values, node.colors, default.color = "#FFFFFF", style.name = "WikiPathways-As-Network")

## -----------------------------------------------------------------------------
#  unzip(system.file("extdata","drugbank-5.1.0.xgmml.zip", package="rWikiPathways"), exdir = getwd())
#  drugbank <- file.path(getwd(), "drugbank-5.1.0.xgmml")

## -----------------------------------------------------------------------------
#  commandsRun(paste0('cytargetlinker extend idAttribute="Ensembl" linkSetFiles="', drugbank, '"') )
#  commandsRun('cytargetlinker applyLayout network="current"')

## -----------------------------------------------------------------------------
#  my.drugs <- selectNodes("drug", by.col = "CTL.Type", preserve = FALSE)$nodes #easy way to collect node SUIDs by column value
#  clearSelection()
#  setNodeColorBypass(my.drugs, "#DD99FF")
#  setNodeShapeBypass(my.drugs, "hexagon")
#  
#  drug.labels <- getTableColumns(columns=c("SUID","CTL.label"))
#  drug.labels <- na.omit(drug.labels)
#  mapply(function(x,y) setNodeLabelBypass(x,y), drug.labels$SUID, drug.labels$CTL.label)

## -----------------------------------------------------------------------------
#  save(ewp.up, file = "lung_cancer_ewp_up.Rdata")
#  save(ewp.dn, file = "lung_cancer_ewp_down.Rdata")

## -----------------------------------------------------------------------------
#  saveSession('tutorial_session') #.cys

## -----------------------------------------------------------------------------
#  exportImage('tutorial_image2', type='PDF') #.pdf
#  exportImage('tutorial_image2', type='PNG', zoom=200) #.png; use zoom or width args to increase size/resolution
#  ?exportImage

## -----------------------------------------------------------------------------
#  sessionInfo()

