🧬 Differential Expression Analysis in R (BYOD RNA-seq)

📝 Course Overview

RNA sequencing (RNA-seq) is a powerful tool for studying gene expression. This course provides a hands-on introduction to differential expression analysis in R, covering essential preprocessing, statistical methods, and functional interpretation.

The course is structured as a two-week intensive programme, balancing theory, hands-on exercises, and self-directed learning. Participants will learn to filter and normalise data, apply statistical models, visualise results, and interpret functional outcomes.

🚀 This course is part of the GS-LS R learning line. Completing it will strengthen your skills in bioinformatics. The course also includes optional advanced topics such as batch effect correction and time series analysis.

🎯 Learning Objectives

• Understand RNA-seq data types, normalisation, and transformation.
• Learn appropriate statistical tests for different RNA-seq experimental designs.
• Identify differentially expressed genes using multiple approaches.
• Perform clustering and visualisation (PCA, heatmaps, volcano plots).
• Explore functional analysis using Gene Ontology (GO) and pathway analysis.
• Gain hands-on experience with DESeq2 and linear models.

🗓️ Course Schedule

Session	Topic
1	Filtering and Normalisation
2	Data Transformation and Exploratory Analysis
3	Statistics on RNA-seq Data
4	Gene Ontology, GSEA, and Pathway Analysis
5	Final Presentations and Review

The course takes place over 5 sessions, spread over two weeks. Sessions contain lectures, live-coding, and exercises. The beginning of the course focusses on exercises with example data. During the course, we transition into a workshop, where analysing ones own dataset takes priority. Participants are encouraged to bring their own RNA-seq data, or one from a colleague. If none is available, I’m happy to help you find an interesting online dataset that fits your interests.

🎓 Final Assessment

The final assessment consists of a practical project, where participants will apply the full RNA-seq analysis pipeline on a dataset of their choice.

Assessment Options:

1. Personal Data Project – Participants analyse their own RNA-seq dataset (or one from a colleague) using the techniques learned during the course. They will generate a complete analysis report, including data preprocessing, differential expression analysis, and visualisation of key findings.
2. Provided Dataset Analysis – If participants do not have their own data, they can choose from a set of provided datasets and perform the same pipeline, ensuring they apply the methods correctly.

Final Presentation:

• Each participant will present their findings in a short presentation (10–15 minutes) during the final session.
• The focus will be on analysis choices, challenges faced, and biological interpretation of results.
• Feedback will be provided by instructors and peers.

Attendence of 80% and completion of the final project are required to receive a certificate of completion for the course.

🧰 Prerequisites

• Proficiency in R is required (At least completion Introduction to Rwithout much trouble).
• Experience with data manipulation and visualisation in R is highly recommended.
• Some familiarity with basic statistics and high-throughput sequencing data is helpful but not mandatory.

📚 Instructional Method

This is not a DESeq2 course. The DESeq2 package is very popular and thorough, and we do teach it in this course as a way of doing differential expression. We focus mostly on the why and the how of core principles, and execute these in various ways. When understanding the analysis, you can chose for yourself what is the best method for your dataset.

• Interactive In-Class Sessions: Live sessions focus on Q&A, coding challenges, and exercises.
• Self-Study & Practice: Participants are expected to spend additional time practising exercises.
• Personalised Support: Teachers will be available for guidance and feedback throughout the course.

💻 Tools and Resources

• Software: R, RStudio, and Bioconductor packages (DESeq2, ggplot2, pheatmap and more).
• Course materials: RNA-seq datasets, analysis scripts, and interactive notebooks.
• Additional resources: Cheat sheets, recommended readings, and example code.

⭐ What Participants Say

✅ Hands-on learning with real-world applications – Participants appreciate the focus on applying RNA-seq analysis to real datasets, making the course directly relevant to their research.
✅ Great balance of theory and practice – The course provides structured guidance while allowing for self-exploration and problem-solving.
✅ Clear explanations and well-structured materials – The combination of live coding, exercises, and step-by-step tutorials makes complex topics more accessible.
✅ Supportive learning environment – Many found the instructor’s support and peer discussions valuable in overcoming technical challenges.

💡 Heads-up: *Advanced fast-paced course Some participants noted that the course moves quickly, especially for those with limited R experience. We welcome all R coders, but know that differential expression is an advanced topic requiring adequate skill.

🚀 How to Enrol

To sign up, check the registration page or contact me directly at l.w.dijkhuizen@uu.nl.

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Need more details? Feel free to reach out! 📩