Weighted Gene Co-expression Network Analysis

This package is provided through the R-language integration into Perseus and therefore requires R as well as the package itself to be installed in order to be used. Visit the PluginInterop and PerseusR for additional technical information on the integration of R and Perseus.

• Download the R installer install it.
• Remember the R installation directory. If you do not know where you have installed R, check the default location C:\Program Files\R\R-3.5.0\bin, or search for Rscript.exe.
• Add R to your Path environment variable. Open the start menu and search for “environment variables”, and select “Edit system environment variables”.
• Find the Path variable in the editor and select Edit.
• Now we can add R to the Path by clicking New and entering the location of the …/bin folder of the R installation, e.g C:\Program Files\R\R-3.5.0\bin
• In certain Windows version you might have to edit the Path directly. The `Path` consists of folder locations separated by ‘;’. Therefore, just append the installation location of R and the ‘;’ separator. No spaces or quotes are required. If the Path variable is not defined on your system, you can define it yourself.
• Check that R is installed correctly by opening cmd.exe from the start menu and entering

  > R 

  An R session should start.

• Inside the running R session, install the WGCNA and PerseusR libraries by entering/pasting the following code line-by-line. When prompted for local install, type ‘yes’ twice and select a close-by server for the package download.

  source("http://bioconductor.org/biocLite.R")
  biocLite(c("AnnotationDbi", "impute", "GO.db", "preprocessCore"))
  install.packages(c("WGCNA", "devtools"))
  library(devtools)
  install_github('jdrudolph/PerseusR') 

• You should now be able to load the WGCNA and PerseusR libraries by entering the following code. Upon loading, WGCNA will print some output, while PerseusR will not print any.

  library(WGCNA)
  library(PerseusR) 

You are now ready to perform co-expression analysis from within Perseus.

• Soft-threshold
• Co-expression clustering