Enrichment Analysis
Date: 27 November 2026 @ 09:00 - 17:00
Duration: PT7H
Language of instruction: English
Overview
Experiments designed to quantify gene expression often yield hundreds of genes that exhibit statistically significant differences between groups of interest. After identifying differentially expressed genes, enrichment analysis (EA) methods can be used to explore the biological functions associated with them. These methods allow us to identify groups of genes (e.g., grouped into pathways) that are overrepresented, thereby offering insights into biological mechanisms. Gene Set Enrichment Analysis (GSEA) is one of the EA methods frequently used for high-throughput gene expression data analysis. This course will cover GSEA and alternative enrichment methods. Because GSEA implementation is directly linked to databases that annotate gene function in cells, the course will also provide an overview of functional annotation databases, such as Gene Ontology. All course materials are available on the GitHub course web page.
Audience
This course is designed for PhD students, postdoctoral and other researchers in the life sciences from both academia and industry who are eager to perform functional annotation of a set of differentially expressed genes.
Learning outcomes
At the end of the course, the participants should be able to:
- Distinguish available enrichment analysis method
- Apply overrepresentation and Gene Set Enrichment Analysis methods using R
- Determine whether the genes of a Gene Ontology term have a statistically significant difference in expression or not
- Find gene sets in databases (e.g. KEGG, oncogenic gene sets) and use them in R
Prerequisites
Knowledge / competencies
This course is part of the Omics Data Analysis learning path. To get the most out of this course, you should meet the learning outcomes of First Steps with R in Life Sciences or Introduction to Statistics and Data Visualisation with R courses.
In case of doubt, evaluate your R skills here.
Technical
You are required to have an internet connection and your own computer with the latest R and RStudio versions installed.
Schedule – CE(S)T time zone
Application
The registration fees for academics are 100 CHF and 500 CHF for for-profit companies.
While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.
Applications will close on 12/11/2026 or as soon as the places will be filled up. Cancellation after 12/11/2026 will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.
You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 working days.
Venue and Time
This course will be streamed.
The course will start at 9:00 CET and end around 17:00 CET.
Precise information will be provided to the registered participants in due time.
Additional information
Coordination: Diana Marek, SIB Training group.
A Certificate of Attendance will be sent provided you were present at the course, whereas a Certificate of Achievement recommending 0.25 ECTS will be sent provided you passed the exam.
You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.
SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.
For more information, please contact [email protected].
Keywords: biostatistics, experimental biology, functional genomics, genes and genomes, training, raphael gottardo group
City: Streamed
Country: Switzerland
Organizer: SIB Swiss Institute of Bioinformatics (https://ror.org/002n09z45)
Event types:
- Workshops and courses
Instructors: Tania Wyss Lozano, Gustavo Ruiz Buendia
Activity log

Switzerland