Enrichment Analysis

02 December 2022
Streamed
Application deadline:
16 November 2022

Cancellation deadline:
16 November 2022
Tania Wyss Lozano, Gustavo Ruiz Buendia
Omics data analysis
Statistics
Intermediate
ACADEMIC: 60 CHF
FOR-PROFIT: 300 CHF
ECTS
0.25 credits
Check out the
Course material


Application are closed because the course is full with a long waiting list or has just passed. to receive notification when a new course is scheduled.
CLOSED

Next courses:
24 Mar 2023 Streamed
23 Jun 2023 Streamed
01 Dec 2023 Streamed

This course is now full with a long waiting list. Other iterations will take place next year

Overview

Experiments designed to quantify gene expression often yield hundreds of genes that show statistically significant differences between groups of interest. Once differentially expressed genes are identified, enrichment analysis (EA) methods can be used to explore the biological functions of these genes. EA methods allow us to identify groups of genes (e.g. particular pathways) that are over-represented, thereby offering insights into biological mechanisms. One of the EA methods frequently used for high-throughput gene expression data analysis is the Gene Set Enrichment Analysis (GSEA). This course will cover GSEA and alternative enrichment methods. Because the implementation of GSEA is directly linked to databases that annotate the function of genes in a cell, the course will also give an overview of functional annotation databases such as the Gene Ontology. All course material can be found on the GitHub course web page.

Audience

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

Learning objectives

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

  • Distinguish available enrichment analysis methods
  • Apply GSEA and over-representation analysis 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.

Prerequisites

Knowledge / Competencies

Technical

  • 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.

Application

This course is now full with a long waiting list. Other iterations will take place next year

Registration fees are 60 CHF for academics and 300 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 as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 16/11/2022. Cancellation after this date will not be reimbursed. Please note that participation to 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 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 participants on due time.

Additional information

Coordination: Diana Marek, 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.

Please note that participation in SIB courses is subject to our general conditions.

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.