## ----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