Network analysis and visualization of proteomics data using weighted gene correlation network analysis. This file details how to run MetaNetwork. MetaNetwork's wiki contains an overview of the options available for MetaNetwork's WGCNA workflow and a vignette that details how to use and interpret MetaNetwork's results.
- Download Docker compatible with your operating system.
- Open Terminal (Mac) or Powershell (Windows).
- Run the following code to download and launch MetaNetwork:
docker pull avc167/metanetwork:latest
docker run -p 3838:3838 avc167/metanetwork:latest
Open a web browser and type localhost:3838 (Windows) or 0.0.0.0:3838 into the search bar. MetaNetwork will now be visible.
Docker for MacOS requires users to allocate memory for its processes. If not enough memory is allocated to MetaNetwork, it will crash without warning or throwing an error message while processing data. To increase the amount of memory allocated to MetaNetwork, you must open the Docker Desktop application and navigate to settings (circled in red in the below image):
Then click Resources, and use the slider to increase the amount of memory and/or CPUs allocated to MetaNetwork's processes. Typically 10 GB of memory enables an analysis of ~8000 proteins without memory issues.
When running MetaNetwork for the first time, it will take a few moments to load the necessary R packages. The GUI will then be visible.
While running MetaNetwork, the R console will contain all the messages and any potential errors occuring. Once MetaNetwork is finished running, it will automatically generate plots and data tables in the WGCNA Diagnostics, Module Eigenprotein Analysis, g:Profiler Enrichment, and Data Analysis tabs. The data will also be downloadable under the Download tab.
The data file should be in the format of UniProt Accessions, any optional identifiers (like gene symbol for example), then data, as shown in the example below.
In the example, the first column is Accessions, and the following columns are data. The heading is the name of the sample. The first column must have a heading of "Accession."
The experimental groups file should contain one column of sample identifiers and one column of experimental groups. These columns must be labeled as SampleID and Experiment. The column labeled SampleID must be matched to the column heading for the columns in the data file, as seen in the image below.
The second columns, Experiment, lists the experimental condition to which the sample belongs.
Finally, the UniProt Data Table upload should be a .tsv or .tab file downloaded from UniProt containing an Entry column, Protein name column, and Gene name column.
- Install RStudio
- Type the following commands into the console of RStudio to run MetaNetwork on either Windows or MacOS:
if(!require(shiny)) install.packages("shiny", dependencies = TRUE)
shiny::runGitHub("MetaNetwork", "smith-chem-wisc")
- Please see instructions below for setting up Pandoc in the system environment.
- Then, MetaNetwork will be ready to run using the following commands:
install.packages("shiny")
shiny::runGitHub("MetaNetwork", "smith-chem-wisc")
MetaNetwork requires:
- R 4.0.2 or later
- RStudio 1.3.1073 (optional but recommended)
On MacOS, we recommend using RStudio to run MetaNetwork because the install.pandoc() command is not supported, complicating Pandoc installation. Fortunately, RStudio is packaged with its own Pandoc, so MetaNetwork will be fully functional on MacOS.
If running in the RGui, MetaNetwork requires the installation of (Pandoc)[https://pandoc.org/] and (Rtools)[https://cran.r-project.org/bin/windows/Rtools/]. For Windows computers, run the following code in RGui to install Pandoc:
install.packages(c("installr", "rmarkdown"))
installr::install.pandoc()
Pandoc will begin downloading and require a restart before MetaNetwork will function correctly. After the computer is restarted, open RGui again and run.
rmarkdown::find_pandoc()
This command should return the version and location of the Pandoc installation.
For Anaconda users: Due to a bug in RStudio, installations of RStudio from conda will not be able to find the Pandoc folder. As a result, we recommend downloading RStudio directly from the RStudio website.