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Installing R and RstudioThe 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:
Option1: Use QUTs rVDI virtual desktop machinesThis 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 PCGo 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 labsThe 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
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Preparing your data for Differential Expression analysis. Two data files are needed for this analysis: a samples table and your count table
R packages
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
Load required R packages. Unlike installing the packages, this needs to be done every time you run the analysis
Import your data files (count table and samples table) into R
Checking for outliers and batch effects
PCA plot
Pairwise samples heatmap
Identify differentially expressed (DE) genes using DESeq2
Annotating your DE genes
Volcano plot
DE genes heatmap
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