NetBoxR Updates GSoC 2022

An R package to mark functional modules in the network analysis using data from pathwayCommons

View the Project on GitHub sajo25/netboxr

20 June 2022

Week Two | GitHub Actions bugs and Galaxy tool interface

by Sara J

Tasks

  1. Fix remaining GitHub Actions bugs Status: In progress Branch: None PR: None

    1a. Add tests to netboxr for existing examples

    1b. The Date field is over a month old

    1c. Use of orphaned gtools?

    1d. Non-Standard Files Found

    1e. Example Taking Too Long

    1f. Fix: Namespace in Imports field not imported from: ‘gtools’

  2. Make plan for Galaxy tool interface Status: Complete Branch: None PR: None

  3. Meeting with supervisors Status: Thursday Branch: None PR: None

Progress report

GitHub Actions bugs progress

https://github.com/sajo25/netboxr/runs/7068714025?check_suite_focus=true

Currently, the R script for the prototype Galaxy netboxr tool is able to Load Human Interactions Network (HIN) network Load altered gene list map altered gene list on HIN network using (1) different community discovery methods, (2) resolution values, and

Alternative Module Discovery Methods Statistical Significance of Discovered Network Global Network Null Model Local Network Null Model Write NetBox Output to Files

Term Enrichment in Modules using Gene Ontology (GO) Analysis Alternative Pathway Data
Using Tabular Simple Interaction Format (SIF)-Based Network Data Using PaxtoolsR for Pathway Commons Data Selecting Input Gene Lists for use with NetBox

graph

# Set up R error handling to go to stderr
options(show.error.messages=F,
        error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})

# Avoid crashing Galaxy with an UTF8 error on German LC settings
loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")

# Import required libraries and data
suppressPackageStartupMessages({
  library(optparse)
  library(netboxr)
  library(igraph)
  library(RColorBrewer)
})

data(netbox2010)

option_list <- list(
  make_option("--geneList", type="character", help="Tab-delimited list of genes of interest"),
  make_option("--cutoff", type="double", help="p-value cutoff value"),
  make_option("--communityMethod", type="character", help="commumanity detection method"),
  make_option("--resolutionParam", type="integer", help="community size"),
  #make_option("--networkType", type="character", help="edge weights"),
  #make_option("--weightsInput", type="", help="edge weights"),
  make_option("--globalModel", type="logical", help="Used to assess the global connectivity 
              (number of nodes and edges) of the largest module in the identified network 
              compared with the same number but randomly selected gene list"),
  make_option("--localModel", type="logical", help="Used to assess the network modularity in 
              the identified network compared with random re-wired network"),
  
  make_option("--networkPlot", type="logical", help="Plot of edge-annotated netboxr graph"),
  make_option("--outputSIF", type="logical", help="NetBox algorithm output in SIF format"),
  make_option("--neighborList", type="logical", help="Contains information of all neighbor nodes"),
  make_option("--moduleMembership", type="logical", help="Identified pathway module numbers"),
  make_option("--nodeType", type="logical", help="Indicates whether node is linker or candidate")
)
parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
args = parse_args(parser)

# Vars
geneList = scan(args$geneList, what = character(), sep = "\t")
cutoff = args$cutoff
communityMethod = args$communityMethod
resolutionParam = args$resolutionParam
#networkType = args$networkType
#weightsInput = args$weightsInput
globalModel = args$globalModel
localModel = args$localModel
networkPlot = args$networkPlot
outputSIF = args$outputSIF
neighborList = args$neighborList
moduleMembership = args$moduleMembership
nodeType = args$nodeType

# Network analysis as described in netboxr vignette
sifNetwork <- netbox2010$network
graphReduced <- networkSimplify(sifNetwork, directed = FALSE)
threshold <- cutoff
results <- geneConnector(geneList = geneList, networkGraph = graphReduced,
                         directed = FALSE, pValueAdj = "BH", pValueCutoff = threshold,
                         resolutionParam = resolutionParam, #weightsInput = weightsInput,
                         communityMethod = communityMethod, keepIsolatedNodes = FALSE)

# Check the p-value of the selected linker
linkerDF <- results$neighborData
linkerDF[linkerDF$pValueFDR < threshold, ]
graph_layout <- layout_with_fr(results$netboxGraph)

# Global Network Null Model
if (globalModel) {
  globalTest <- globalNullModel(netboxGraph = results$netboxGraph, networkGraph = graphReduced,
                                iterations = 10, numOfGenes = 274)
}

# Local Network Null Model
if (localModel) {
  localTest <- localNullModel(netboxGraph = results$netboxGraph, iterations = 10)
}

## Output
# Plot the edge annotated graph
if (networkPlot) {

  edges <- results$netboxOutput
  interactionType <- unique(edges[, 2])
  interactionTypeColor <- brewer.pal(length(interactionType), name = "Spectral")
  edgeColors <- data.frame(interactionType, interactionTypeColor, stringsAsFactors = FALSE)
  colnames(edgeColors) <- c("INTERACTION_TYPE", "COLOR")
  netboxGraphAnnotated <- annotateGraph(netboxResults = results, edgeColors =
                                        edgeColors, directed = FALSE, linker = TRUE)
  pdf("network_plot.pdf", width=8)
  plot(results$netboxCommunity, netboxGraphAnnotated, layout = graph_layout,
       vertex.size = 10, vertex.shape = V(netboxGraphAnnotated)$shape, edge.color
       = E(netboxGraphAnnotated)$interactionColor, edge.width = 3)

# Add interaction type annotations
  legend(x = -1.8, y = -1, legend = interactionType, col =
           interactionTypeColor, lty = 1, lwd = 2, bty = "n", cex = 1)
  dev.off()
}

# Local Network Null Model
#if (localModel) {
#  h <- hist(localTest$randomModularityScore, breaks = 35, plot = FALSE)
#  h$density = h$counts/sum(h$counts)
#  plot(h, freq = FALSE, ylim = c(0, 0.1), xlim = c(0.1, 0.6), col = "lightblue")
#  abline(v = localTest$modularityScoreObs, col = "red")
#  dev.off()
#}

# NetBox algorithm output in SIF format.
if (outputSIF) {
  write.table(results$netboxOutput, file = "network.sif", sep = "\t", quote = FALSE,
              col.names = FALSE, row.names = FALSE)
}

# Save neighbor data
if (neighborList) {
  write.table(results$neighborData, file = "neighbor_data.txt", sep = "\t",
              quote = FALSE, col.names = TRUE, row.names = FALSE)
}

# Save identified pathway module numbers
if (moduleMembership) {
  write.table(results$moduleMembership, file = "community.membership.txt", sep = "\t",
              quote = FALSE, col.names = FALSE, row.names = FALSE)
}

# Save file that indicates whether the node is a 'linker' or 'candidate'
if (nodeType) {
  write.table(results$nodeType, file = "nodeType.txt", sep = "\t", quote = FALSE, col.names = FALSE,
              row.names = FALSE)
}

Rscript.exe galaxy_r.R --geneList "genes.txt" --cutoff 0.05 --communityMethod "ebc" --resolutionParam 3 
--globalModel TRUE --localModel TRUE --networkPlot TRUE --outputSIF TRUE --neighborList TRUE 
--moduleMembership TRUE --nodeType TRUE

Remaining tasks

Discussion points for next meeting

tags: gsoc