Introduction to paxtoolsr

• BioPAX: http://biopax.org/
• Community-wide effort to represent biological pathways
  • Pathways are collections of interactions that biologists have found useful to group together for organizational, historic, biophysical or other reasons
• Types
  • Metabolic pathways
  • Signaling pathways
  • Protein-protein interactions
  • Gene regulatory pathways
• Advanced tutorial on BioPAX
  • https://github.com/cannin/biopaxTutorial

• Website and Tutorial (Vignette):
  
  • https://bioconductor.org/packages/release/bioc/html/paxtoolsr.html
• Publication:
  • http://www.ncbi.nlm.nih.gov/pubmed/26685306
• Read and write
  • Biological Pathway Exchange (BioPAX)
  • Binary Simple Interaction Format (SIF)
  • Extended SIF: Includes additional information about SIF network
  • Gene Set (GMT)
  • Systems Biology Graphical Notation Markup Language (SBGN-ML)
• Search and summarize local BioPAX files
• Search Pathway Commons

• Load library

library(paxtoolsr)

• List possible downloads

downloadPc2()

• Download databases

# Single databases
geneSets <- downloadPc2("PathwayCommons.8.Reactome.GSEA.hgnc.gmt.gz", version="8")
sif <- downloadPc2("PathwayCommons.8.kegg.EXTENDED_BINARY_SIF.hgnc.txt.gz", version="8")

# All databases 
geneSets <- downloadPc2("PathwayCommons.8.All.GSEA.hgnc.gmt.gz", version="8")

sif <- filterSif(sif$edges, ids=c("GPI"))

nrow(sif)

[1] 26

colnames(sif)

[1] "PARTICIPANT_A"           "INTERACTION_TYPE"       
[3] "PARTICIPANT_B"           "INTERACTION_DATA_SOURCE"
[5] "INTERACTION_PUBMED_ID"   "PATHWAY_NAMES"          
[7] "MEDIATOR_IDS"           

head(sif[, 1:3, with=FALSE], 2)

   PARTICIPANT_A       INTERACTION_TYPE PARTICIPANT_B
1:           GPI     catalysis-precedes         ADPGK
2:           GPI controls-production-of   CHEBI:17665

library(webchem)

cts_convert('16-hydroxypalmitate', 'Chemical Name', 'ChEBI')

$`16-hydroxypalmitate`
[1] "CHEBI:55328" "CHEBI:55329"

# KEGG 
sifKegg <- downloadPc2("PathwayCommons.8.kegg.EXTENDED_BINARY_SIF.hgnc.txt.gz", version="8")
sif <- sifKegg

paths <- unique(unlist(sif$edges$PATHWAY_NAMES))
purineIdx <- grepl("purine", paths, ignore.case=TRUE)
purinePaths <- paths[purineIdx]

metabFilteredSif <- filterSif(sif$edges, ids=metab$chebi)
tmp <- searchListOfVectors(purinePaths, metabFilteredSif$PATHWAY_NAMES)
purineIdx <- unique(unlist(tmp))

purineOnlySif <- metabFilteredSif[purineIdx]
setDF(purineOnlySif)
purineOnlySif[1:2, 1:6]

  PARTICIPANT_A          INTERACTION_TYPE PARTICIPANT_B
1   CHEBI:15422 consumption-controlled-by         ADCY3
2   CHEBI:15422           used-to-produce   CHEBI:15996
  INTERACTION_DATA_SOURCE INTERACTION_PUBMED_ID
1                    KEGG                    NA
2                    KEGG                    NA
                          PATHWAY_NAMES
1                     Purine metabolism
2 Metabolic pathways, Purine metabolism

tmp <- c(purineOnlySif[, 1], purineOnlySif[, 3])
idx <- which(!grepl("^CHEBI:", tmp))

resKegg <- sort(table(tmp[idx]))
length(resKegg)

[1] 93

• Example on conducting an enrichment analysis on CellMiner cell line data using gene sets from Pathway Commons

# Load libraries
library(paxtoolsr); library(rcellminer)

# Load data
geneSets <- downloadPc2("PathwayCommons.8.Reactome.GSEA.hgnc.gmt.gz", version="8")
mutData <- getAllFeatureData(rcellminerData::molData)[["mut"]]

hiMutGenes <- head(sort(rowSums(mutData), decreasing=TRUE), 25)

# Initialize variable
pvals <- NULL

for(set in geneSets) {
  #set <- hiMutGenes
  sampleSize <- length(hiMutGenes) # size drawn
  hitInSample <- length(which(hiMutGenes %in% set)) # black drawn
  hitInPop <- length(which(rownames(mutData) %in% set)) # all black 
  failInPop <- nrow(mutData)-hitInPop # number of red
  # Calculate over-enrichment for current gene set
  pval <- phyper(hitInSample-1, hitInPop, failInPop, sampleSize, lower.tail= FALSE)
  # Add current result
  pvals <- c(pvals, pval)
}

# Adjust p-values
pvals <- p.adjust(pvals, method="fdr")
length(pvals[pvals < 0.05])

[1] 0