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.

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

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

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

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

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

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

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

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

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

• http://deanattali.com/2015/06/28/introducing-shinyjs-colourinput/
• http://stackoverflow.com/questions/15155814/check-if-r-package-is-installed-then-load-library

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

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)

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/

• 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/)

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

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