# Script: Step4_PPINetwork.R
# Description: In this script, we will explore the differential gene
# expression dataset comparing lung cancer vs. healthy tissue
# samples. The RNA-sequencing dataset was retrieved from
# TCGA (The Cancer Genome Atlas) and pre-processed in R.
# Differential gene expression analysis was performed with the
# DESeq2 R-package.
# Version: 3.0
# Last updated: 2025-06-24
# Author: mkutmon & peiprJS
# ==================================================================
# PPI network creation with the stringApp for Cytoscape
# ==================================================================
RCy3::cytoscapePing()
## You are connected to Cytoscape!
query <- format_csv(as.data.frame(degs$GeneName), col_names=F, escape = "double", eol =",")
commandsPOST(paste0('string protein query cutoff=0.99 newNetName="PPI network" query="',query,'" limit=0 species="Homo sapiens"'))
## SUID
## 33558
exportImage(paste0(out.folder,'PPI-network.png'), type='PNG', zoom=500)
## file
## "/home/peipr/gits/BBS2061-breast-cancer/scripts/../output/PPI-network.png"
# ??? Question 10 - answer in document
# ==================================================================
# Analysis of hub nodes and high betweenness nodes
# ==================================================================
# Let's analyze the network and plot the degree distribution
RCy3::analyzeNetwork()
## networkTitle nodeCount
## "STRING network - PPI network - 1 (undirected)" "4426"
## edgeCount avNeighbors
## "1686" "3.8638941398865785"
## diameter radius
## "23" "12"
## avSpl cc
## "8.792755055278684" "0.31696571598119094"
## density heterogeneity
## "0.007317981325542762" "0.9482948786329006"
## centralization ncc
## "0.03638016790293831" "3461"
## time
## "0.102"
hist(RCy3::getTableColumns(columns = "Degree")$Degree, breaks=100, main = "PPI Degree distribution", xlab = "Degree")
filename <- paste0(out.folder,"PPI-degree-distribution.png")
png(filename , width = 500, height = 1200, res = 150)
dev.off()
## png
## 2
# ??? Question 11 - answer in document
# Let's create a visualization based on the node degree (node color) and
# betweenness (node size)
RCy3::createVisualStyle("centrality")
RCy3::setNodeLabelMapping("display name", style.name = "centrality")
## NULL
control.points <- c (0, 0.3)
colors <- c ('#FFFFFF', '#DD8855')
setNodeColorMapping("Degree", c(0,60), colors, style.name = "centrality", default.color = "#C0C0C0")
## NULL
setNodeSizeMapping("BetweennessCentrality", table.column.values = c (0, 0.5), sizes = c(50,100), mapping.type = "c", style.name = "centrality", default.size = 10)
## NULL
RCy3::setVisualStyle("centrality")
## message
## "Visual Style applied."
toggleGraphicsDetails()
exportImage(paste0(out.folder,'PPI-centrality.png'), type='PNG', zoom=500)
## file
## "/home/peipr/gits/BBS2061-breast-cancer/scripts/../output/PPI-centrality.png"
# ??? Question 12 - answer in document
# ==================================================================
# Visualization of data
# ==================================================================
RCy3::loadTableData(data=data, data.key.column = "GeneName", table = "node", table.key.column = "query term")
## [1] "Success: Data loaded in defaultnode table"
RCy3::createVisualStyle("log2FC vis")
RCy3::setNodeLabelMapping("display name", style.name = "log2FC vis")
## NULL
control.points <- c (-3.0, 0.0, 3.0)
colors <- c ('#5588DD', '#FFFFFF', '#DD8855')
setNodeColorMapping("log2FC", control.points, colors, style.name = "log2FC vis", default.color = "#C0C0C0")
## NULL
RCy3::setVisualStyle("log2FC vis")
## message
## "Visual Style applied."
RCy3::lockNodeDimensions("TRUE", "log2FC vis")
exportImage(paste0(out.folder,'PPI-with-data.png'), type='PNG', zoom=500)
## file
## "/home/peipr/gits/BBS2061-breast-cancer/scripts/../output/PPI-with-data.png"
# ??? Question 13 - answer in document