[4 half days] - [starts on 27-01-2026 at 09:00] - [English] - [Woluwé Saint-Lambert]
Do you want to get started with RNAseq data processing and learn how to analyse this type of data?
RNA sequencing is a powerful technology that is now commonly used to measure gene expression. A differential expression analysis is a statistical analysis that aims to identify quantitative changes in gene expression levels between different experimental groups.
Training aims
The goal of this training is to give an overview of the RNAseq technology and to learn how to analyse and interpret the data using dedicated R Bioconductor packages. It's aimed at biologists who want to learn how to do a differential expression analysis on their RNaseq data.
Content
Requirements
Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have installed R (https://cloud.r-project.org/) and Rstudio (https://rstudio.com/products/rstudio/download/#download).
Location
Woluwé Saint-Lambert : Salle ICP4 - Find the location on Google Maps
Salle ICP4 - Bâtiment ICP - Etage -1 (sous-sol) - 74 Avenue Hippocrate - 1200 Woluwé Saint-Lambert
Attendance
The training is open to all but requires prior registration.
Time slots for training
27-01-2026 from 09:00 to 17:00
29-01-2026 from 09:00 to 17:00
Rate
UCLouvain member (with an internal account), UCLouvain student, SHS Namur member : 150 euros per day for a total of 300 euros
Invoice paid by university, UCLouvain clinic member, Researcher : 160 euros per day for a total of 320 euros
Company, Non-UCLouvain member : 250 euros per day for a total of 500 euros
Financial support
This training course is recognized by the IABE, enabling participants to earn CPD points.
(Note that this is true for all SMCS courses.)
For more information (open)
Tools used during training
R
Methods and method families discussed
Differential expression analysis
Regression model
Multiple linear regression
Multivariate exploratory analyses
PCA - Principal component analysis