## ----pre,echo=FALSE,results='hide'--------------------------------------- library(knitr) opts_chunk$set(warning=FALSE,message=FALSE,cache=TRUE) ## ---- eval=FALSE--------------------------------------------------------- # source("http://bioconductor.org/biocLite.R") # biocLite("FGNet") ## ---- echo=FALSE, message=FALSE------------------------------------------ library(FGNet) ## ---- eval=FALSE--------------------------------------------------------- # biocLite(c("RGtk2", "RCurl", # "RDAVIDWebService", "gage", "topGO", "KEGGprofile", # "GO.db", "KEGG.db", "reactome.db", "org.Sc.sgd.db")) ## ---- eval=FALSE--------------------------------------------------------- # library(FGNet) # FGNet_GUI() ## ---- eval=FALSE--------------------------------------------------------- # geneExpr <- c("YBL084C", "YDL008W", "YDR118W", "YDR301W", "YDR448W", # "YFR036W", "YGL240W", "YHR166C", "YKL022C", "YLR102C", "YLR115W", # "YLR127C", "YNL172W", "YOL149W", "YOR249C") # geneExpr <- setNames(c(rep(1,10),rep(-1,5)), geneExpr) # # FGNet_GUI(geneExpr) ## ---- eval=FALSE--------------------------------------------------------- # ?FGNet_report ## ---- eval=FALSE--------------------------------------------------------- # getwd() ## ------------------------------------------------------------------------ genesYeast <- c("ADA2", "APC1", "APC11", "APC2", "APC4", "APC5", "APC9", "CDC16", "CDC23", "CDC26", "CDC27", "CFT1", "CFT2", "DCP1", "DOC1", "FIP1", "GCN5", "GLC7", "HFI1", "KEM1", "LSM1", "LSM2", "LSM3", "LSM4", "LSM5", "LSM6", "LSM7", "LSM8", "MPE1", "NGG1", "PAP1", "PAT1", "PFS2", "PTA1", "PTI1", "REF2", "RNA14", "RPN1", "RPN10", "RPN11", "RPN13", "RPN2", "RPN3", "RPN5", "RPN6", "RPN8", "RPT1", "RPT3", "RPT6", "SGF11", "SGF29", "SGF73", "SPT20", "SPT3", "SPT7", "SPT8", "TRA1", "YSH1", "YTH1") library(org.Sc.sgd.db) geneLabels <- unlist(as.list(org.Sc.sgdGENENAME)) genesYeast <- sort(geneLabels[which(geneLabels %in% genesYeast)]) # Optional: Gene expression (1=UP, -1=DW) genesYeastExpr <- setNames(c(rep(1,28), rep(-1,30)),genesYeast) ## ---- eval=FALSE--------------------------------------------------------- # feaResults_David <- fea_david(names(genesYeast), geneLabels=genesYeast, # email="example@email.com") # ?fea_david ## ---- eval=FALSE--------------------------------------------------------- # feaResults_topGO <- fea_topGO(genesYeast, geneIdType="GENENAME", organism="Sc") # ?fea_topGO ## ---- eval=FALSE--------------------------------------------------------- # jobID <- fea_gtLinker(geneList=genesYeast, organism="Sc") # ?fea_gtLinker ## ------------------------------------------------------------------------ jobID <- 3907019 feaResults_gtLinker <- fea_gtLinker_getResults(jobID=jobID, organism="Sc") ## ---- eval=FALSE--------------------------------------------------------- # library(gage) # data(gse16873) # # # Set gene labels? (they need to have unique identifiers) # library(org.Hs.eg.db) # geneSymbols <- select(org.Hs.eg.db,columns="SYMBOL",keytype="ENTREZID", # keys=rownames(gse16873)) # # geneLabels <- geneSymbols$SYMBOL # names(geneLabels) <- geneSymbols$ENTREZID # head(geneLabels) # # # GAGE: # feaResults_gage <- fea_gage(eset=gse16873, # refSamples=grep('HN',colnames(gse16873)), # compSamples=grep('DCIS',colnames(gse16873)), # geneLabels=geneLabels, annotations="REACTOME", # geneIdType="ENTREZID", organism="Hs") # ?fea_gage ## ---- eval=FALSE--------------------------------------------------------- # ?format_results() ## ---- eval=FALSE--------------------------------------------------------- # feaResults_David <- format_david("http://david.abcc.ncifcrf.gov/data/download/90128.txt") # feaResults_gtLinker <- fea_gtLinker_getResults(jobID=3907019) ## ---- eval=FALSE--------------------------------------------------------- # FGNet_report(feaResults_David, geneExpr=genesYeastExpr, plotKeggPw=FALSE) # FGNet_report(feaResults_topGO, geneExpr=genesYeastExpr) # FGNet_report(feaResults_gtLinker, geneExpr=genesYeastExpr) # FGNet_report(feaResults_gage) ## ---- eval=FALSE--------------------------------------------------------- # data(FEA_tools) # FEA_tools[,4:6] ## ---- eval=FALSE--------------------------------------------------------- # FGNet_report(feaResults_gtLinker, filterThreshold=0.3) ## ---- eval=FALSE--------------------------------------------------------- # ?FGNet_report ## ------------------------------------------------------------------------ feaResults <- feaResults_gtLinker incidMat <- fea2incidMat(feaResults) incidMat$metagroupsMatrix[1:5, 1:5] incidMat_terms <- fea2incidMat(feaResults, key="Terms") incidMat_terms$metagroupsMatrix[5:10, 1:5] ## ------------------------------------------------------------------------ functionalNetwork(incidMat, geneExpr=genesYeastExpr, plotTitleSub="Default gene view") ## ------------------------------------------------------------------------ getTerms(feaResults)[1] ## ---- eval=FALSE--------------------------------------------------------- # functionalNetwork(incidMat_terms, plotOutput="dynamic") ## ------------------------------------------------------------------------ functionalNetwork(incidMat_terms, plotType="bipartite", plotTitleSub="Terms in several metagroups") ## ------------------------------------------------------------------------ jobID <- 1639610 feaAlzheimer <- fea_gtLinker_getResults(jobID=jobID, organism="Hs") ## ------------------------------------------------------------------------ names(feaAlzheimer) ## ---- results='hide'----------------------------------------------------- head(feaAlzheimer$metagroups) ## ------------------------------------------------------------------------ getTerms(feaAlzheimer)[3:4] ## ------------------------------------------------------------------------ incidMat <- fea2incidMat(feaAlzheimer) ## ------------------------------------------------------------------------ head(incidMat$metagroupsMatrix) incidMat$gtSetsMatrix[1:5, 14:18] ## ---- eval=FALSE--------------------------------------------------------- # data(FEA_tools) # FEA_tools[,4:6] ## ------------------------------------------------------------------------ incidMatFiltered <- fea2incidMat(feaAlzheimer, filterAttribute="Silhouette Width", filterOperator="<", filterThreshold=0.2) ## ---- eval=FALSE--------------------------------------------------------- # incidMatFiltered$filteredOut ## ------------------------------------------------------------------------ # (Fake expression data) genesAlz <- rownames(incidMat$metagroupsMatrix) genesAlzExpr <- setNames(c(rep(1,50), rep(-1,27)), genesAlz) ## ------------------------------------------------------------------------ fNw <- functionalNetwork(incidMatFiltered, geneExpr=genesAlzExpr, keepColors=FALSE, vLabelCex=0.5) ## ---- eval=FALSE--------------------------------------------------------- # functionalNetwork(incidMatFiltered, geneExpr=genesAlzExpr, plotOutput="dynamic") # fNw <- functionalNetwork(incidMatFiltered, plotOutput="none") ## ------------------------------------------------------------------------ names(fNw) names(fNw$iGraph) library(igraph) clNw <- fNw$iGraph$commonClusters clNw ## ---- eval=FALSE--------------------------------------------------------- # vcount(clNw) # ecount(clNw) # sort(betweenness(clNw), decreasing=TRUE)[1:10] # igraph.to.graphNEL(clNw) ## ---- eval=FALSE--------------------------------------------------------- # functionalNetwork(incidMatFiltered, plotOutput="dynamic") # # Modify the layout... # saveLayout <- tkplot.getcoords(1) # tkp.id (ID of the tkplot window) # functionalNetwork(incidMatFiltered, vLayout=saveLayout) ## ------------------------------------------------------------------------ mgKeyTerm <- keywordsTerm(getTerms(feaAlzheimer), nChar=100)[-c(as.numeric(incidMatFiltered$filteredOut))] functionalNetwork(incidMatFiltered, plotType="bipartite", legendText=mgKeyTerm) ## ------------------------------------------------------------------------ functionalNetwork(incidMatFiltered, geneExpr=genesAlzExpr, plotType="bipartite", keepAllNodes=TRUE, plotTitleSub="Bipartite network will all nodes", legendText=mgKeyTerm, vLabelCex=0.5) ## ------------------------------------------------------------------------ incidMatTerms <- fea2incidMat(feaAlzheimer, key="Terms") ## ------------------------------------------------------------------------ functionalNetwork(incidMatTerms, plotType="bipartite", plotTitle="Terms in several metagroups") ## ---- eval=FALSE--------------------------------------------------------- # functionalNetwork(incidMatTerms, weighted=TRUE, plotOutput="dynamic") ## ------------------------------------------------------------------------ incidMatTerms <- fea2incidMat(feaAlzheimer$metagroups, clusterColumn="Metagroup", key="Terms", filterAttribute="Silhouette.Width", filterThreshold=0.2) functionalNetwork(incidMatTerms, legendText=FALSE, plotOutput="dynamic") ## ------------------------------------------------------------------------ functionalNetwork(incidMatTerms, legendText=FALSE) ## ---- fig.height=5, fig.width=10----------------------------------------- incidMatTerms <- fea2incidMat(feaAlzheimer, key="Terms", removeFilteredGtl=FALSE) par(mfrow=c(1,2)) functionalNetwork(incidMatTerms, vLabelCex=0.2, plotTitle="Including filtered terms", legendText=FALSE) functionalNetwork(incidMatTerms, plotType="bipartite", vLabelCex=0.4, plotTitle="Including filtered terms") ## ------------------------------------------------------------------------ txtFile <- paste(file.path(system.file('examples', package='FGNet')), "DAVID_Yeast_raw.txt", sep=.Platform$file.sep) feaResults_David <- format_david(txtFile, jobName="David_example", geneLabels=genesYeast) ## ---- eval=FALSE--------------------------------------------------------- # feaResults_David <- fea_david(names(genesYeast), email="...", geneLabels=genesYeast) ## ------------------------------------------------------------------------ gtSets <- feaResults_David$geneTermSets gtSets <- gtSets[gtSets$Cluster %in% c(9),] gtSets <- gtSets[gtSets$Pop.Hits<500,] ## ---- message=FALSE------------------------------------------------------ termsGenes <- t(fea2incidMat(gtSets, clusterColumn="Terms")$clustersMatrix) library(R.utils) rownames(termsGenes) <- sapply(strsplit(rownames(termsGenes), ":"), function(x) capitalize(x[length(x)])) termsGenes[1:5,1:5] ## ------------------------------------------------------------------------ functionalNetwork(t(termsGenes), plotType="bipartite", keepAllNodes=TRUE, legendPrefix="", plotTitle="Genes - Terms network", plotTitleSub="", geneExpr=genesYeastExpr, plotExpression="Fill") ## ------------------------------------------------------------------------ functionalNetwork(termsGenes, plotType="bipartite", keepAllNodes=TRUE, legendPrefix="", plotTitle="Genes - Terms network", plotTitleSub="") ## ------------------------------------------------------------------------ incidMat <- fea2incidMat(feaResults_David) functionalNetwork(incidMat) ## ------------------------------------------------------------------------ incidMatTerms <- fea2incidMat(feaResults_David, key="Terms") ## ---- eval=FALSE--------------------------------------------------------- # functionalNetwork(incidMatTerms$clustersMatrix, plotOutput="dynamic", # weighted=TRUE, eColor="grey") ## ------------------------------------------------------------------------ functionalNetwork(incidMatTerms$clustersMatrix, plotType="bipartite", plotTitle="Terms in several clusters") ## ------------------------------------------------------------------------ colnames(feaResults_David$clusters) ## ---- fig.height=5, fig.width=10----------------------------------------- par(mfrow=c(1,2)) # Highest enrichment score filterProp <- as.numeric(as.character(feaResults_David$clusters$ClusterEnrichmentScore)) quantile(filterProp, c(0.10, 0.25, 0.5, 0.75, 0.9)) incidMatFiltered <- fea2incidMat(feaResults_David, filterAttribute="ClusterEnrichmentScore", filterOperator="<", filterThreshold=7.65) functionalNetwork(incidMatFiltered, eColor=NA, plotTitle="Highest enrichment score") # Lowest genes quantile(as.numeric(as.character(feaResults_David$clusters$nGenes)), c(0.10, 0.25, 0.5, 0.75, 0.9)) incidMatFiltered <- fea2incidMat(feaResults_David, filterAttribute="nGenes", filterOperator=">", filterThreshold=20) functionalNetwork(incidMatFiltered, plotTitle="Smallest clusters") ## ------------------------------------------------------------------------ keywordsTerm(getTerms(feaResults_David), nChar=100) keywords <- c("hydrolase") selectedClusters <- sapply(getTerms(feaResults_David), function(x) any(grep(paste("(", paste(keywords, collapse="|") ,")",sep=""), tolower(x)))) ## ---- eval=FALSE--------------------------------------------------------- # getTerms(feaResults_David)[selectedClusters] ## ------------------------------------------------------------------------ tmpFea <- feaResults_David tmpFea$clusters <- cbind(tmpFea$clusters, keywords=selectedClusters) incidMatSelection <- fea2incidMat(tmpFea, filterAttribute="keywords", filterOperator="!=",filterThreshold="TRUE") functionalNetwork(incidMatSelection, plotType="bipartite") ## ------------------------------------------------------------------------ distMat <- clustersDistance(incidMat) ## ---- echo=FALSE, results='hide'----------------------------------------- dev.off() ## ------------------------------------------------------------------------ selectedClusters <- rep(FALSE, nrow(feaResults_David$clusters)) selectedClusters[c(8,9,11)] <- TRUE tmpFea <- feaResults_David tmpFea$clusters <- cbind(tmpFea$clusters, select=selectedClusters) incidMatSelection <- fea2incidMat(tmpFea, filterAttribute="select", filterOperator="!=",filterThreshold="TRUE") functionalNetwork(incidMatSelection, eColor=NA) ## ---- eval=FALSE--------------------------------------------------------- # # Same analysis, setting overlap to 6: # feaResults_David_ov6 <- fea_david(names(genesYeast), geneLabels=genesYeast, email="example@email.com", # argsWS=c(overlap=6, initialSeed=3, finalSeed=3, linkage=0.5, kappa=50)) ## ---- echo=FALSE--------------------------------------------------------- txtFile <- paste(file.path(system.file('examples', package='FGNet')), "DAVID_Yeast_overl6_raw.txt", sep=.Platform$file.sep) feaResults_David_ov6 <- format_david(txtFile, jobName="David_example", geneLabels=genesYeast) ## ------------------------------------------------------------------------ # Filter/select sum(feaResults_David_ov6$geneTermSets$Pop.Hits < 100) gtSets <- feaResults_David_ov6$geneTermSets[feaResults_David_ov6$geneTermSets$Pop.Hits < 100,] # Save write.table(gtSets, file="david_filteredGtsets.txt", sep="\t", col.names = TRUE, quote=FALSE) # Load with "readGeneTermSets" feaResults_filteredGtsets <- readGeneTermSets("david_filteredGtsets.txt", tool="DAVID") # ... functionalNetwork(fea2incidMat(feaResults_filteredGtsets), vLabelCex=0.5) ## ---- eval=FALSE--------------------------------------------------------- # # Yeast # library(org.Sc.sgd.db) # goGenesCountSc <- table(sapply(as.list(org.Sc.sgdGO2ORF), length)) # barplot(goGenesCountSc, main="Number of genes annotated to GO term (Sc) ", # xlab="Number of genes", ylab="Number of GO terms") # # # Human # library(org.Hs.eg.db) # goGenesCountHs <- table(sapply(as.list(org.Hs.egGO2EG), length)) # barplot(goGenesCountHs, main="Number of genes annotated to GO term (Human)", # xlab="Number of genes", ylab="Number of GO terms") ## ------------------------------------------------------------------------ incidMatFiltered <- fea2incidMat(feaAlzheimer, filterAttribute="Silhouette Width", filterOperator="<", filterThreshold=0.2) stats <- analyzeNetwork(incidMatFiltered) ## ------------------------------------------------------------------------ names(stats) stats$transitivity ## ------------------------------------------------------------------------ head(stats$betweennessMatrix) ## ------------------------------------------------------------------------ stats$hubsList$Global ## ------------------------------------------------------------------------ stats$hubsList$"9" ## ------------------------------------------------------------------------ incidMat_metab <- fea2incidMat(feaResults_David) analyzeNetwork(incidMat_metab) ## ------------------------------------------------------------------------ goIds <- getTerms(feaResults_David, returnValue="GO")[[7]] plotGoAncestors(goIds, ontology="MF", nCharTerm=40, labelCex=0.8) ## ---- eval=FALSE--------------------------------------------------------- # keggIds <- getTerms(feaAlzheimer, returnValue="KEGG")[[3]] # plotKegg("hsa05010", geneExpr=genesAlzExpr, geneIDtype="GENENAME") # # Saved as .png in current directory