04 December 2020
Cancellation deadline:
20 November 2020
Tania Wyss Lozano, Isabelle Dupanloup
Omics data analysis
Academic: 60 CHF
For-profit: 300 CHF
0.25 ECTS credits

Next course(s):
25 Jun 2021 Streamed
03 Dec 2021 Streamed
17 Mar 2022 Streamed
24 Jun 2022 Streamed
02 Dec 2022 Streamed
31 Mar 2023 Streamed
23 Jun 2023 Streamed
01 Dec 2023 Streamed
11 Mar 2024 Streamed
28 Jun 2024 Streamed
29 Nov 2024 Streamed

We are sorry but this course is oversubscribed, with a long waiting list. Sign up here to be informed of the next course.


Experiments designed to quantify gene expression often yield hundreds of genes that show statistically significant differences between two classes (two biological states, two phenotype states, two experimental conditions, etc). Once differentially expressed genes are identified, enrichment analysis (EA) methods can be conducted to identify groups of genes (e.g. particular pathways) that are differentially expressed, and offer insights into biological mechanisms. One example of such a method is the Gene Set Enrichment Analysis (GSEA), which is very popular and frequently used for high-throughput gene expression data analysis.

This course will cover GSEA and alternative enrichment tools. Since most of their implementations are directly linked to databases that annotate the function of genes in the cell, the course will also introduce GO enrichment analysis.


Biologists eager to identify a statistically reliable set of genes that are differentially expressed.

Learning objectives

At the end of the course, the participants will be able to:

  • identify statistical methods that could be used to pinpoint differentially expressed genes quantified by methods such as microarrays or RNA sequencing
  • determine whether a set of genes shows statistically significant differences between two classes or not
  • distinguish available enrichment analysis methods
  • apply GSEA using R
  • apply enrichment analysis implementations using R
  • determine whether the genes of a GO term have a statistically significant difference in expression or not
  • learn where to find other gene sets in databases (e.g. KEGG, oncogenic gene sets) and use them in R.


Knowledge / Competencies


  • This course will be streamed, you are thus required to have your own computer with an internet connection, and with latest R and RStudio versions installed. An online access to R will also be provided for the practical exercises.


We are sorry but this course is oversubscribed, with a long waiting list.

Registration fees are 60 CHF for academics and 300 CHF for for-profit companies.

Deadline for free-of-charge cancellation is set to 20/11/2020. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

Venue and Time

This course will be streamed.

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants on due time.

Additional information

Coordination: Patricia Palagi, SIB training group.

We will recommend 0.25 ECTS credits for this course (given a passed exam at the end of the course).

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 training@sib.swiss.