Overview
Installing R and Rstudio
You have three main options for running this analysis in RStudio:
Use QUTs rVDI virtual desktop machines
Install R and RStudio on your own PC
Use the provided PCs in the QUT computer labs
Option1: Use QUTs rVDI virtual desktop machines
This is the preferred method, as R and RStudio are already installed, as are all the required R packages needed for analysis. Installing all of these can take over 30 minutes on your own PC, so using an rVDI machine saves time.
rVDI provides a virtual Windows desktop that can be run in your web browser.
To access and run an rVDI virtual desktop:
Go to https://rvdi.qut.edu.au/
Click on ‘VMware Horizon HTML Access’
Log on with your QUT username and password.
Click the ‘R Metagenomics’ option, which is pre setup for this workshop.
*NOTE: you need to be connected to the QUT network first, either being on campus or connecting remotely via VPN.
Option2: Install R and RStudio on your own PC
Go to the following page https://posit.co/download/rstudio-desktop/ and follow the instructions provided to install first R and then Rstudio.
Download and install R, following the default prompts:
https://cran.r-project.org/bin/windows/base/
Download and install RStudio, following the default prompts:
https://posit.co/download/rstudio-desktop/
Option3: Use the provided PCs in the QUT computer labs
The PCs in the computer labs already have R and RStudio installed. If using this option, you will need to install the required R packages (unlike rVDI). The code for installing these packages is in the analysis section below.
Open PuTTy and paste the text below into the command prompt:
qsub -I -S /bin/bash -l walltime=4:00:00 -l select=1:ncpus=16:mem=128gb
After a few minutes interactive mode will start. You will now be able to do all your analysis - including running Nextflow and Nanopore workflows - in this interactive session.
Map your HPC home directory
On the rVDI machines, your HPC home directory is already mapped to H drive.
If you are using a different computer, follow the instructions here to map your HPC home directory:
Create your workshop folders and check your data
In your H drive (the drive you just mapped) check to see if there is an ASV_table.tsv in
H:\meta_workshop\illumina\results\dada2
This is the base data file we’ll be working with - an abundance table of read counts per ASV (i.e. taxonomic group) per sample. Using this we’ll be able to quantify and visualise taxonomic diversity and structure, using R.
The analysis also requires the metadata.tsv file we created at the start of last workshop. Check to see if this file is in H:\meta_workshop\illumina\data
If you can’t see the ASV_table.tsv or metadata.tsv files (your Nextflow job may have failed, for example), open PuTTY and run the following to copy these from a successful nfcore/ampliseq run to your HPC home directory:
cp -r /work/training/metagenomics/public_data/Illumina/results $HOME/meta_workshop/illumina cp -r /work/training/metagenomics/public_data/Illumina/data/metadata.tsv $HOME/meta_workshop/illumina/data
We’ll need to also create a working directory to store our R scripts and the results of today’s analysis.
In PuTTY run the following:
mkdir $HOME/meta_workshop/R_analysis
Open RStudio and create a new R script
The RStudio icon is on your rVDI desktop. Open RStudio.
In this workshop we will not be teaching you R or RStudio, other than the very basics. For the most part you can just copy/paste the code into R and run it. There are a multitude of beginner’s R course out there, for example this QCIF course: https://www.qcif.edu.au/trainingcourses/introduction-to-programming%3A-r-for-reproducible-scientific-analysis . QCIF courses are free to QUT staff and HDR students.
An overview of how to navigate the RStudio GUI is here: https://heardlibrary.github.io/digital-scholarship/script/r/navigate/
Create a new script by ‘File’ → “New File” → “R script”. Now hit ‘File’ → ‘Save’ and save the script in H:/meta_workshop/R_analysis (i.e. the directory we just created). Give the script a name. Call it “R_metagenomics”.
Go to the next section, ‘Setting up your R environment’