## ----knitrSetup, include=FALSE---------------------------------------------
library(knitr)
opts_chunk$set(out.extra='style="display:block; margin: auto"', fig.align="center", tidy=TRUE)


## ----style, include=FALSE, echo=FALSE, results='asis'----------------------
BiocStyle::markdown()

## ----installPaxtoolsr, eval=FALSE------------------------------------------
#  source("http://bioconductor.org/biocLite.R")
#  biocLite("paxtoolsr")

## ----loadLibrary, message=FALSE, warning=FALSE-----------------------------
library(paxtoolsr)

## ----searchHelp, eval=FALSE, tidy=FALSE------------------------------------
#  help.search("paxtoolsr")

## ----showHelp, eval=FALSE, tidy=FALSE--------------------------------------
#  help(graphPc)
#  ?graphPc

## ----paxtoolsMergeExample--------------------------------------------------
file1 <- system.file("extdata", "raf_map_kinase_cascade_reactome.owl", package="paxtoolsr")
file2 <- system.file("extdata", "biopax3-short-metabolic-pathway.owl", package="paxtoolsr")

mergedFile <- mergeBiopax(file1, file2)

## ----summarize, results='hold'---------------------------------------------
s1 <- summarize(file1)
s2 <- summarize(file2)
s3 <- summarize(mergedFile)

s1$Catalysis
s2$Catalysis
s3$Catalysis

## ----paxtoolsValidationExample, eval=FALSE---------------------------------
#  errorLog <- validate(system.file("extdata", "raf_map_kinase_cascade_reactome.owl", package="paxtoolsr"), onlyErrors=TRUE)

## ----loadSifFromFile-------------------------------------------------------
sif <- toSif(system.file("extdata", "biopax3-short-metabolic-pathway.owl", package="paxtoolsr")) 

## ----viewSifHead-----------------------------------------------------------
head(sif)

## ----toSifnxExample, tidy=TRUE---------------------------------------------
# Select additional node and edge properties
inputFile <- system.file("extdata", "raf_map_kinase_cascade_reactome.owl", package="paxtoolsr")

results <- toSifnx(inputFile=inputFile) 

## ----downloadSif, eval=FALSE-----------------------------------------------
#  results <- downloadPc2()

## ----paxtoolsrCache, eval=FALSE--------------------------------------------
#  Sys.getenv("PAXTOOLSR_CACHE")

## ----searchResultsExample, eval=FALSE--------------------------------------
#  ## Search Pathway Commons for "glycolysis"-related pathways
#  searchResults <- searchPc(q="glycolysis", type="pathway")

## ----searchResultsExampleVerbose-------------------------------------------
## Search Pathway Commons for "glycolysis"-related pathways 
searchResults <- searchPc(q="glycolysis", type="pathway", verbose=TRUE)

## ----searchResultsType-----------------------------------------------------
str(searchResults)

## ----searchResultsXpathExample---------------------------------------------
xpathSApply(searchResults, "/searchResponse/searchHit/name", xmlValue)[1]
xpathSApply(searchResults, "/searchResponse/searchHit/pathway", xmlValue)[1]

## ----convertXmlToDf, message=FALSE-----------------------------------------
library(plyr)
searchResultsDf <- ldply(xmlToList(searchResults), data.frame)

# Simplified results
simplifiedSearchResultsDf <- searchResultsDf[, c("name", "uri", "biopaxClass")]

## ----saveBiopaxFileFromPcQuery, message=FALSE, results='hide'--------------
## Use an XPath expression to extract the results of interest. In this case, the URIs (IDs) for the pathways from the results
searchResults <- xpathSApply(searchResults, "/searchResponse/searchHit/uri", xmlValue)

## Generate temporary file to save content into
biopaxFile <- tempfile() 

## Extract a URI for a pathway in the search results and save into a file  
idx <- which(grepl("panther", simplifiedSearchResultsDf$uri) & 
             grepl("glycolysis", simplifiedSearchResultsDf$name, ignore.case=TRUE))
uri <- simplifiedSearchResultsDf$uri[idx]
saveXML(getPc(uri, "BIOPAX"), biopaxFile) 

## ----traverse--------------------------------------------------------------
# Convert the Uniprot ID to a URI that would be found in Pathway Commons
uri <- paste0("http://identifiers.org/uniprot/P31749")

# Get URIs for only the ModificationFeatures of the protein
xml <- traverse(uri=uri, path="ProteinReference/entityFeature:ModificationFeature")

# Extract all the URIs 
uris <- xpathSApply(xml, "//value/text()", xmlValue)

# For the first URI get the modification position and type
tmpXml <- traverse(uri=uris[1], path="ModificationFeature/featureLocation:SequenceSite/sequencePosition")
cat("Modification Position: ", xpathSApply(tmpXml, "//value/text()", xmlValue))

tmpXml <- traverse(uri=uris[1], path="ModificationFeature/modificationType/term")
cat("Modification Type: ", xpathSApply(tmpXml, "//value/text()", xmlValue))

## ----loadGraphLibraries, message=FALSE-------------------------------------
library(igraph)

## ----plotGraph-------------------------------------------------------------
sif <- toSif(system.file("extdata", "biopax3-short-metabolic-pathway.owl", package="paxtoolsr")) 

# graph.edgelist requires a matrix
g <- graph.edgelist(as.matrix(sif[,c(1,3)]), directed=FALSE)
plot(g, layout=layout.fruchterman.reingold)

## ----graphQueryExampleSingle-----------------------------------------------
gene <- "BDNF"
t1 <- graphPc(source=gene, kind="neighborhood", format="BINARY_SIF", verbose=TRUE)
t2 <- t1[which(t1[,2] == "controls-state-change-of"),]

g <- graph.edgelist(as.matrix(t2[,c(1,3)]), directed=FALSE)
plot(g, layout=layout.fruchterman.reingold)

## ----graphQueryExample-----------------------------------------------------
genes <- c("AKT1", "IRS1", "MTOR", "IGF1R")
t1 <- graphPc(source=genes,
              kind="PATHSBETWEEN",
              format="BINARY_SIF",
              verbose=TRUE)
t2 <- t1[which(t1[,2] == "controls-state-change-of"),]

g <- graph.edgelist(as.matrix(t2[,c(1,3)]), directed=FALSE)
plot(g, layout=layout.fruchterman.reingold)

## ----dataOverlayExample----------------------------------------------------
library(RColorBrewer)

# Generate a color palette that goes from white to red that contains 10 colors 
numColors <- 10
colors <- colorRampPalette(brewer.pal(9, "Reds"))(numColors)

# Generate values that could represent some experimental values 
values <- runif(length(V(g)$name))

# Scale values to generate indicies from the color palette  
xrange <- range(values)
newrange <- c(1, numColors)

factor <- (newrange[2]-newrange[1]) / (xrange[2]-xrange[1])
scaledValues <- newrange[1] + (values-xrange[1]) * factor
indicies <- as.integer(scaledValues)

# Color the nodes based using the indicies and the color palette created above 
g <- set.vertex.attribute(g, "color", value=colors[indicies])

#get.vertex.attribute(h, "color")

plot(g, layout = layout.fruchterman.reingold) 

## ----sifNetStats-----------------------------------------------------------
# Degree for each node in the igraph network 
degree(g)
#Number of nodes 
length(V(g)$name)
#Clustering coefficient 
transitivity(g)
#Network density 
graph.density(g)
# Network diameter
diameter(g)

## ----sifShortestPath-------------------------------------------------------
# Get the first shortest path between two nodes
tmp <- get.shortest.paths(g, from="IRS1", to="MTOR")

# igraph seems to return different objects on Linux versus OS X for get.shortest.paths()
if(class(tmp[[1]]) == "list") {
	path <- tmp[[1]][[1]]	# Linux
} else {
  path <- tmp[[1]] # OS X
}

# Convert from indicies to vertex names 
V(g)$name[path]

## ----gseaExampleLibraries, message=FALSE-----------------------------------
library(paxtoolsr) # To retrieve data from Pathway Commons
library(limma) # Contains geneSetTest
library(affy) # To load microarray data
library(hgu95av2) # Annotation packages for the hgu95av2 platform
library(hgu95av2cdf)
library(XML) # To parse XML files

## ----gseaExampleGenGeneSet, results='hide', eval=FALSE---------------------
#  # Generate a Gene Set
#  ## Search Pathway Commons for "glycolysis"-related pathways
#  searchResults <- searchPc(q="glycolysis", type="pathway")
#  
#  ## Use an XPath expression to extract the results of interest. In this case, the URIs (IDs) for the pathways from the results
#  searchResults <- xpathSApply(searchResults, "/searchResponse/searchHit/uri", xmlValue)
#  
#  ## Generate temporary files to save content into
#  biopaxFile <- tempfile()
#  
#  ## Extract the URI for the first pathway in the search results and save into a file
#  uri <- searchResults[2]
#  saveXML(getPc(uri, "BIOPAX"), biopaxFile)

## ----gseaExampleGenGeneSetSave, results='hide'-----------------------------
## Generate temporary files to save content into
gseaFile <- tempfile() 

## Generate a gene set for the BioPAX pathway with gene symbols 
### NOTE: Not all search results are guaranteed to result in gene sets 
tmp <- toGSEA(biopaxFile, gseaFile, "HGNC Symbol", FALSE)
geneSet <- tmp$geneSet

## ----gseaExampleMicroarrayAnalyis------------------------------------------
# Process Microarray Data
## Load/process the estrogen microarray data 
estrogenDataDir <- system.file("extdata", package="estrogen")
targets <- readTargets(file.path(estrogenDataDir, "estrogen.txt"), sep="")

abatch <- ReadAffy(filenames=file.path(estrogenDataDir, targets$filename))
eset <- rma(abatch)

f <- paste(targets$estrogen,targets$time.h,sep="")
f <- factor(f)
design <- model.matrix(~0+f)
colnames(design) <- levels(f)

fit <- lmFit(eset, design)
 
cont.matrix <- makeContrasts(E10="present10-absent10",E48="present48-absent48",Time="absent48-absent10",levels=design)

fit2  <- contrasts.fit(fit, cont.matrix)
fit2  <- eBayes(fit2)

## Map the gene symbols to the probe IDs 
symbol <- unlist(as.list(hgu95av2SYMBOL))

### Check that the gene symbols are on the microarray platform 
genesOnChip <- match(geneSet,symbol)
genesOnChip # CHECK FOR ERROR HERE 

genesOnChip <- genesOnChip[!is.na(genesOnChip)]

### Grab the probe IDs for the genes present
genesOnChip <- names(symbol[genesOnChip])
genesOnChip <- match(genesOnChip, rownames(fit2$t))
genesOnChip <- genesOnChip[!is.na(genesOnChip)]

## Run the Gene Set Test from the limma Package
geneSetTest(genesOnChip,fit2$t[,1],"two.sided")

## ----idMapping, eval=FALSE-------------------------------------------------
#  sif <- toSif(system.file("extdata", "raf_map_kinase_cascade_reactome.owl", package="paxtoolsr"))
#  
#  # Generate a mapping between the HGNC symbols in the SIF to the Uniprot IDs
#  library(biomaRt)
#  ensembl <- useMart("ensembl")
#  ensembl <- useDataset("hsapiens_gene_ensembl", mart=ensembl)
#  
#  hgnc_symbol <- c(sif$PARTICIPANT_A, sif$PARTICIPANT_B)
#  output <- getBM(attributes=c('hgnc_symbol', 'uniprot_sptrembl'), filters='hgnc_symbol', values=hgnc_symbol, mart=ensembl)
#  
#  # Remove blank entries
#  output <- output[output[,2] != "",]

## ----filePathExample, eval=FALSE-------------------------------------------
#  toSif("/directory/file")
#  #or
#  toSif("X:\\directory\\file")

## ----changeHeapSize, eval=FALSE--------------------------------------------
#  options(java.parameters="-Xmx1024m")
#  
#  library(paxtoolsr)
#  
#  # Megabyte size
#  mbSize <- 1048576.0
#  
#  runtime <- .jcall("java/lang/Runtime", "Ljava/lang/Runtime;", "getRuntime")
#  maxMemory <- .jcall(runtime, "J", "maxMemory")
#  maxMemoryMb <- maxMemory / mbSize
#  cat("Max Memory: ", maxMemoryMb, "\n")

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

