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Aim:

  1. Identify statistically significant (FDR < 0.05) differentially expressed genes.

  2. Visualise results with PCA plots, heatmaps and volcano plots.

Requirements

  • Run your samples (FASTQ) using the nextflow nf-core/RNA-seq pipeline using ‘star_salmon’ (Task 4 session) or an alternative pipeline that generates feature counts.

  • Get Rstudio working for you - Option 1 below for in-class session

Installing R and Rstudio

The analysis scripts in this guide are written in R script. We will be using RStudio, a front-end gui for R, to run the analysis scripts.

You have three main options for running this analysis in RStudio:

  1. Use QUTs rVDI virtual desktop machines

  2. Install R and RStudio on your own PC

  3. 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’ and select R_Megenomics (if you have > 1 available to you).

Log on with your QUT username and password

*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.

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