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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/

DOI

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")

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

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 http://shiny.rstudio.com/tutorial/lesson1/
  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

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

Code References

Create PDF animation

  1. in animatedPlotAB.R uncomment lines: 48, 49, 50, 51, 52 and 306
  2. install LaTeX (e.g. https://miktex.org/)
  3. install animate Package http://tug.ctan.org/macros/latex/contrib/animate/animate.pdf
  4. copy to working directory and run LaTeX template document: CreatePDFanimation.tex

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.
png(paste0(myfilepath, '/', myfilename), 
     width = 1500, height = 1200, 
     units = "px", pointsize = 12)
  1. add dev.off() at the end of the function
  2. 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/

sub apps

Ath GSE56094 experimental data analysis

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

obsolete

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