Home

RNA‐Seq Data Analysis in R Workshop

Important

Date: November 12th
Instructor: Simona Merlini
Level: Basic/Intermediate
Requirements: R, RStudio (download link)

---

Overview

RNA-Seq is a widely used method for analyzing gene expression. This workshop focuses on differential expression analysis using R, guiding participants through essential steps from raw count data to identifying differentially expressed genes.

Objectives

• Understand the basics of RNA-seq data analysis
• Perform normalization, model fitting, and hypothesis testing
• Identify differentially expressed genes
• Interpret and visualize results

Prerequisites

• Basic knowledge of R and RStudio
• Install required R packages listed in requirements.R

---

Welcome, everyone! Today, we'll be diving into RNA-Seq data analysis, specifically looking at differential gene expression analysis using R. RNA sequencing, or RNA-seq, is a powerful technique that allows us to study gene expression at a depth and resolution far beyond what we could achieve before. It has transformed our ability to understand how genes are expressed under various conditions, whether in healthy tissue versus disease, different time points, or across various experimental treatments. Throughout this session, we'll go from raw sequencing counts to identifying differentially expressed genes, covering the essential steps and statistical methods along the way.

The goal of today’s workshop is to equip you with the knowledge to process, analyze, and interpret RNA-seq data. We’ll also cover practical tips and hands-on R code, so by the end, you’ll have both the foundational understanding and some practical tools you can use in your own projects.

Agenda

1. 🧬 Introduction to RNA-Seq and Differential Expression
   Overview of RNA-seq technology, data structure, and objectives.

2. 🔬 Data Preprocessing and Normalization
   Steps to preprocess and normalize gene count data.

3. 🛠️ Differential Expression Analysis
   Perform differential expression analysis with DESeq2.

4. 📊 Results Interpretation and Visualization
   Practical tips for visualizing and interpreting differential expression results.

---

Reference

• RNA Sequencing and Analysis Kukurba KR, Montgomery SB. RNA Sequencing and Analysis. Cold Spring Harb Protoc. 2015 Apr 13;2015(11):951-69. doi: 10.1101/pdb.top084970. PMID: 25870306; PMCID: PMC4863231.
• A Beginner’s Guide to Analysis of RNA Sequencing Data Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR. A Beginner's Guide to Analysis of RNA Sequencing Data. Am J Respir Cell Mol Biol. 2018 Aug;59(2):145-157. doi: 10.1165/rcmb.2017-0430TR. PMID: 29624415; PMCID: PMC6096346.
• RNA-seq data science: From raw data to effective interpretation Deshpande D, Chhugani K, Chang Y, Karlsberg A, Loeffler C, Zhang J, Muszyńska A, Munteanu V, Yang H, Rotman J, Tao L, Balliu B, Tseng E, Eskin E, Zhao F, Mohammadi P, P Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation. Front Genet. 2023 Mar 13;14:997383. doi: 10.3389/fgene.2023.997383. PMID: 36999049; PMCID: PMC10043755.
• Introduction to differential gene expression analysis using RNA-seq
• Standards, Guidelines and Best Practices for RNA-Seq
• RNA-seq Data Analysis Workshop
• Fundamentals of Data Visualization
• Analyzing RNA-seq Data with DESeq2
• RNA-seq and Differential Expression Analysis Using DESeq2
• Single-cell RNA-seq Analysis Tutorial

Resources

• DESeq2 package
• limma package
• edgeR package

For questions or further guidance, feel free to reach out!