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DGE-scheme.jpg

Aim of today

  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.

We will now perform the following tasks using Rstudio - DE

  1. Preparing your data. Two data files are needed for this analysis: a samples table and your count table

  2. R packages

    1. Install required R packages (only need to run once) - after installation, we only need to load the packages. NOTE: If using an rVDI virtual machine, the R packages are already installed

    2. Load required R packages. Unlike installing the packages, this needs to be done every time you run the analysis

    3. Import your data files (count table and samples table) into R

  3. Checking for outliers and batch effects

    1. PCA plot

    2. Pairwise samples heatmap

  4. Identify differentially expressed (DE) genes using DESeq2

    1. Annotating your DE genes

    2. Volcano plot

    3. DE genes heatmap

  1. Preparing your data for DE - next

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