ATGCCGGAATTGGCACATAACAAGTACTGCCTCGGTCCTTAAGCTGTATTGCACCATATGACGGATGCCGGAATTGGCACATAACAAGTAC
TGCCTCGGTCCTTAAGCTGTATTGCACCATATGACGGATGCCGGAATTGGCACATAACAACGGTCCTTAAGCTGTATTGCACCATATGACG
GATGCCGGAATTGGCACATAACAAGTACTGCCTCGGTCCTTAAGCTGTATTTCGGTCCTTAAGCTGTATTCCTTAACAACGGTCCTTAAGG
ATGCCGGAATTGGCACATAACAAGTACTGCCTCGGTCCTTAAGCTGTATTGCACCATATGACGGATGCCGGAATTGGCACATAACAAGTAC
TGCCTCGGTCCTTAAGCTGTATTGCACCATATGACGGATGCCGGAATTGGCACATAACAACGGTCCTTAAGCTGTATTGCACCATATGACG
GATGCCGGAATTGGCACATAACAAGTACTGCCTCGGTCCTTAAGCTGTATTTCGGTCCTTAAGCTGTATTCCTTAACAACGGTCCTTAAGG
Enrichment Analysis
19 November 2024
19 November 2024
For-profit: 500 CHF
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 associated with 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 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 Gene Ontology. All course material can be found on the GitHub course web page.
Audience
Biologists who are eager to perform functional annotation of a set of differentially expressed genes.
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
- Statistics, beginner level (T-test, multiple testing methods).
- R, beginner level (Rstudio, how to install a library, matrix and data frame manipulation, import and export data from text files). Test your R skills with the quiz here, before registering.
Technical
- This course will be streamed. You are thus required to have an internet connection and your own computer with the latest R and RStudio versions installed.
Application
Registration fees are 100 CHF for academics 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 as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 19/11/2024. 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 CEST and end around 17:00 CEST.
Precise information will be provided to the participants on due time.
Additional information
Coordination: Monique Zahn, 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.