Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

This workshop was developed by Luke C. Gandolfo for Melbourne Bioinformatics.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Introduction	What is RNA-seq and what kind of biological questions can it answer? What is a “counts table” and why is it central to RNA-seq data analysis? Why are biological replicates essential for meaningful statistical analysis? How can we organize RNA-seq count data in R for downstream analysis? What preliminary steps and R packages are needed to begin a DE analysis?
Duration: 00h 30m	2. First steps: filtering, visualisation, and basic normalisation	Why should we filter out genes with little or no expression before differential expression analysis? What does “library size” mean, and why is it important to check? How can visualisation help us detect technical variation in RNA-seq data? What are the main sources of technical variation in RNA-seq experiments? How and why do we normalise RNA-seq data? What are CPM and TMM, and how do they help make samples comparable? How can we assess whether normalisation has worked correctly?
Duration: 01h 00m	3. Differential expression with limma	How does the limma method identify differentially expressed genes? Why are linear models used to analyse RNA-seq data? What role does empirical Bayes moderation play in improving DE analysis? How can we assess whether DE results are trustworthy? What is the difference between the limma-trend and limma-voom approaches?
Duration: 01h 30m	4. Summary and exercise	How can we summarise the key steps in an RNA-seq differential expression workflow? When should we use limma-trend versus limma-voom? How can we visualise and interpret DE results? What plots help communicate DE findings effectively? Where can we find RNA-seq count data to practise these analyses?
Duration: 02h 00m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Please follow the steps below and install the required software before the scheduled workshop.

Data Sets

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Data: Obers et al. “Retinoic acid and TGF-β orchestrate organ-specific programs of tissue residency”, Immunity (2024)

Download the GSE232852_CountsTable.txt data file and place it in the workshop folder.

RStudio Setup

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We use RStudio for coding in R.

Click here and follow the instructions to install RStudio Desktop in your system.

Discussion

R packages

Most workshops using R will require the installation of specific packages. Make sure to check in advance with the workshop organisers what packages need to be installed.

You can install packages from CRAN using:

R

install.packages("package_name")

If your package is in a different R repository, such as Bioconductor or GitHub, you may need the BiocManager or devtools packages to install them. To install BiocManager:

R

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install()

For devtools, you can simply do:

R

install.packages("devtools")

You can then install packages directly from GitHub with:

R

devtools::install_github("username/reponame")