RNAseq data analysis

[4 half days] - [English]

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.

Prerequisites

Basic R knowledge is assumed, including data import and export, basic data structures (numeric, characters, logicals, data.frames and matrices), general data manipulation with base R or the tidyverse and data visualisation.

If you are not familiar with the R statistical programming language, we strongly encourage you to start by an introductory R course (see below). Note however that simply following these introductory courses without further practice is unlikely to suffice.

Content

• Overview on RNAseq data processing, exploring the different steps from raw reads to the generation of a gene count matrix
• Introduction to theoretical concepts behind a differential expression analysis (Negative Binomial distribution, Principal Component Analysis, Linear models, Multiple testing correction)
• Real-life example of DE analysis running DESeq2 package step by step
• Learn how to do a Gene Set Enrichment analysis

Tools used during training
R

Methods and method families discussed
Multivariate exploratory analyses
   PCA - Principal component analysis
Regression model
   Multiple linear regression
Differential expression analysis