Fungin like fun+engine

So far, the idea is that you can view interactions among gene(s) of interest, and overlay expression data if available, OR view interactions among your top N significant genes from uploaded data

• Use cases:
• How to run
  • Install R and RStudio
  • Install Required Packages
  • Run from GitHub in RStudio
• Differential Expression Data
• Data Sources
  • Reactome FI
  • miRNA Targets
• To Do

• Enter a few genes to view their interacting neighbors, e.g. BRCA2,MSH2
• Enter mature microRNA(s) to view their interacting neighbors (targets)
  • Select at least mirTarBase as the data source
  • Select "Reactome_FI" to view interactions among target genes also
• Use expression data with any combination of the above
  • Use top differentially expressed genes from a file
  • Enter query genes and overlay expression information

Here's an installation guide if you don't have these already.

list.of.packages <- c("shiny","shinyjs","shinyBS","openxlsx","igraph","qgraph","ggnetwork","network","intergraph","RColorBrewer","ggnewscale","svgPanZoom","gridSVG","svglite","shinycssloaders","shinyWidgets", "shinyalert")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

list.of.bioc.packages <- c("maftools")
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
new.bioc.packages <- list.of.bioc.packages[!(list.of.bioc.packages %in% installed.packages()[,"Package"])]
if(length(new.bioc.packages)) BiocManager::install(new.bioc.packages)

library(shiny)
runGitHub( "shiny_FI_network", "mtandon09", ref="master")

You can also upload a tab/comma-delimited or .xlsx file with differential expression information. The following data are recognized:

• HUGO Gene Symbol (required)
• Fold-change Value (log fold-change values make for better coloring)
• Significance Value (adjusted p-value, FDR, etc.)

From the Wu et. al 2010 paper, this dataset expands on the Reactome pathway database by adding annotation data.

We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome.

The data can be found in the Downloads page at Reactome under "Functional interactions (FIs) derived from Reactome, and other pathway and interaction databases".

miRTarBase [paper][website]

• Enable file upload to overlay mutation data
• Lots of control flow changes for better Shiny-ness
• Lots of aesthetic changes for the network plot
  • Legends don't really work/look crappy
• Lots of UI changes