Enrichment Analysis

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Overview

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.

Audience

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.

Prerequisites

Knowledge / Competencies

• statistics beginner level (T-test, multiple testing methods).
• R beginner level (Rstudio, install a library, matrix manipulation, read files). Test your R skills with the quiz here, before registering.

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

Registration fees for academics 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.

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.

Applications will close once the places are filled. Deadline for registration and free-of-charge cancellation is set to 03/03/2022. Cancellation after this date will not be reimbursed. Please note that participation in 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).

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For more information, please contact training@sib.swiss.