Differential Network Analysis in R

Examine your omics datasets in the prior knowledge context.

Follow the steps as indicated in interactive menu.

For the help overlay the mouse over the info button or go to Quick help section.

Large knowledge networks of Arabidopsis thaliana and Solanum tuberosum immune signalling are provided.

📋🖋 Zagorščak, M., Blejec, A., Ramšak, Ž. et al. DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R. Plant Methods 14, 78 (2018). https://doi.org/10.1186/s13007-018-0345-0

🔍 https://omictools.com/dinar-tool (obsolete)

🔍 https://bio.tools/dinar

🔦 http://isbe.si/2018/09/04/dinar-article-published-in-plant-methods/

🔦 https://www.facebook.com/NIBSlovenia/videos/318025485411839/

Run DiNAR from GitHub

install R-3.x.y or higher :

Win

https://cran.r-project.org/

Ubuntu

sudo apt-get install r-base
sudo apt-get install r-base-dev
sudo apt-get -y install libcurl4-gnutls-dev
sudo apt-get -y install libssl-dev
sudo apt-get install libv8-dev

open R and paste to console

Win

if (!require("devtools")) install.packages("devtools")
if (!require('Rcpp')) install.packages('Rcpp')
devtools::install_github("rstudio/shiny")

install.packages("shiny", dependencies=TRUE)

Ubuntu

install.packages("devtools", lib="~/R/lib")

shiny:::runGitHub("DiNAR", "NIB-SI", subdir = "DiNARscripts/")

*Note: this will install/load libraries: (V8), igraph, colourpicker, plotly, ggplot2, calibrate, stringi, magrittr, yaml, animatoR, stringr, wordcloud2, shinyjs, shinydashboard, shinyBS, colorspace, knitr, markdown, Rcpp, dplyr, rdrop2, fBasics, shinyIncubator, shinysky, downloader, visNetwork, htmltools, htmlwidgets, intergraph, network, ndtv, shinyFiles and pryr

Run DiNAR from shinyapps

🍏 https://NIB-SI.shinyapps.io/DiNAR (Basic - Performance Boost; Instance Size: 8GB; Max Worker Processes: 10; Max Connections per Worker: 1; Max Instances: 3)

Other options

1. download zip and run locally in RStudio: https://www.rstudio.com/products/rstudio/download/#download https://shiny.rstudio.com/tutorial/
2. download zip and deploy: http://shiny.rstudio.com/articles/shinyapps.html http://shiny.rstudio.com/articles/scaling-and-tuning.html
3. download zip and https://support.rstudio.com/hc/en-us/articles/214771447-Shiny-Server-Administrator-s-Guide

Help

http://conferences.nib.si/DiNAR/

Additional Data Files

• CKN
• PIS
• PEPnetwork

Code References

• colour picker
• check package

Create PDF animation

1. in animatedPlotAB.R uncomment lines:

  subDir <- "./plots"
  dir.create(file.path(subDir), showWarnings = FALSE)
  myfilename = paste0("SampleGraph", length(list.files(subDir))+1, '.pdf')
  myfilepath = file.path(subDir)
  par(mai=c(2.0, 2.0, 2.0, 2.0))`

and

   dev.copy2pdf(file = paste0(myfilepath, '/', myfilename), width=24, # height=18, out.type="pdf")

2. install LaTeX (e.g. https://miktex.org/) or use overleaf
3. install animate Package
4. copy to working directory and run LaTeX template document: CreatePDFanimation.tex (for more details see this)

Create gif

1. in animatedPlotAB.R uncomment few lines below # To generate .pdf animation comment
2. replace myfilename = paste0("SampleGraph", length(list.files(subDir))+1, '.pdf') with myfilename = paste0("SampleGraph", formatC(length(list.files(subDir))+1, width=4, flag="0"), '.png')
3. add few lines of code before newplot to save all produced images in .png format; e.g.

  dev.copy(device = png,
           filename = paste0(myfilepath, '/', myfilename),
           width = 1500, height = 1500,
           units = "px", pointsize = 12)

5. add dev.off() at the end of the function
6. run short python2 script containing the following code (take care of dependencies!):

import imageio
import os
with imageio.get_writer('./my.gif', mode='I') as writer:
    for filename in sorted(os.listdir("./images/")): # images == myfilepath == where .png images of interest are
        filename="./images/"+filename
        print(filename)
        image = imageio.imread(filename)
        writer.append_data(image)

Find more information at: https://rfunction.com/archives/812 and https://imageio.github.io/

subApps

• input pre-processing: 🍎 https://github.com/NIB-SI/DiNAR/tree/master/subApps/pre-processing (🍏 https://nib-si.shinyapps.io/pre-processing/)
• network clustering: 🍎 https://github.com/NIB-SI/DiNAR/tree/master/subApps/clustering (🍏 https://nib-si.shinyapps.io/clustering/)
• shortestPaths: 🍇 https://github.com/NIB-SI/DiNAR/tree/master/subApps/shortestPaths
• CustomNetwork from GMM-KnetMiner-SKM combo: 🦋 https://github.com/NIB-SI/DiNAR/tree/master/subApps/GMM-SKM-KnetMiner (🦄 https://nib-si.shinyapps.io/GMM-SKM-KnetMiner/)

Ath GSE56094 experimental data analysis

🥝 https://github.com/NIB-SI/DiNAR/tree/master/GEODataAnalysis

Cross-references

• 🦅 Plant Data Visualization/Orthology Bundle - Cork Oak Use Case
• 🦊 FAIR Data-finder for Agronomic Research (FAIDARE)
• skm.nib.si
• knetminer.com
• gomapman.nib.si

Referencing, i.e. see also

• org.At.tair.db - Genome wide annotation for Arabidopsis
• AraCyc
• PLAZA
• STRING
• Ath Interactome
• ATRM: Arabidopsis Transcriptional Regulatory Map
• Cytoscape

obsolete

https://github.com/NIB-SI/DiNAR/tree/master/NetworkClustering

Random Links

(*) UnicodePlus