3030NSC Workshop6 - Differential Gene Expression (Binder-ready)
This repository was developed as an educational tutorial/workshop for
the Applied Bioinformatics course (3030NSC/7104ESC) offered at Griffith
University (see course
The tutorial covers basic differential gene expression analysis and
visualisation in R using
The teaching material for the workshop was developed by Dr. Alex Cristion (firstname.lastname@example.org) and prepared as a Binder repository by Dr. Ido Bar (email@example.com).
The tutorial is setup to be run live and interactively in class either setup locally on the students’ computers or using Binder (see instructions below).
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace. It requires R to be installed prior to be able to send commands to the interpreter.
Using R and RStudio from Cloud services
If we want to keep things simple (for this course) or we would like to
use R on shared computers, where we can’t install software, we can run R
and Rstudio through a web client that is hosted on a remote server.
We will use the Binder service, which is free, easy to use and can be launched from a single GitHub repository (more about this in the workshop).
Running R and RStudio on Binder
Using Binder is as simple as clicking on the Binder badge - .
Alternatively, you can navigate to the Binder homepage and enter the URL of this tutorial GitHub repository
https://github.com/IdoBar/3030NSC_Workshop6_DGE.git and click on the
launch button (see screenshot in Figure 1 below).
Now be patient while the environment is loading…
You should now see in your web browser an RStudio interface (if you got to the Jupyter page, click on new –> RStudio) and are ready to start working in R in “The Cloud”!
Downloading files from Binder
After we’ve finished working on Binder we would like to download the R
script that we wrote and any output files (summary tables and figures).
We can access those files by using the
files tab in RStudio (bottom
Select the files/folders that you would like to download and click on More Export… (see screenshot in Figure 2 below) to save the file on your computer.
Installing R and RStudio locally
Alternatively, both R and RStudio can be installed locally on any operating system (, , or , see a detailed tutorial), which provides complete control over the installation, added packages and can be used anywhere without requiring internet connection. This is recommended for anyone who is planning to do any future serious analysis in R (including the assignments in this course).
Project Management with RStudio
Regardless whether we installed R and RStudio locally or we use the Binder service, we interact with R through the RStudio integrated development environment (IDE), which let’s us easily write our code, test it, see our files, objects in memory and plots that we produce. If we run the analysis locally, it is highly recommended to use RStudio’s built-in Projects to contain our analysis in its own folder with all the files required. That will also help in reading data files and writing results and figures back to the hard drive.
- Start RStudio by clicking on its icon.
- Start a new project by selecting “File –> New Project” or clicking on the “New Project” icon (under “Edit” in the taskbar).
- Select “New Directory –> New Project” and then enter “Workshop1” in the Directory name text box and browse to the “wrokspace” folder to create the project folder in (see screenshots A-D in Figure 3 below)
- Create a new R script file by selecting “File –> New File –> R Script” or clicking on the “New File” icon (under the “File” in the taskbar)
- Save the script file by select “File –> Save” or pressing Ctrl+s or clicking on the floppy disk icon on the top bar
R can be extended with additional functionality by installing external packages (usually hosted at the Comprehensive R Archive Network repository – CRAN). To find which packages can be useful for your type analysis, use search engine (Google is your friend) and the available Task Views on CRAN, which provide some guidance on which packages on CRAN are relevant for tasks related to a certain topic. Another great source for Genomics and Bioinformatics packages is BioConductor (see their list of suggested workflows).
Please note that the required packages for this workshop are already
pre-installed in Binder, but they will need to be installed if you chose
to run the analysis locally.
To install these packages, we use the
BiocManager::install('package') if installing from
BioConductor), please note that the package name need to be quoted and
that we only need to be perform it once, or when we want or need to
update the package. Once the package was installed, we can load its
functions using the
library(package) command. Note that in this case
we use the package name without quotes!.
For more details and instructions how to setup a similar repository, please visit From Zero to Binder in R!