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merlinis12 edited this page Nov 12, 2024 · 37 revisions

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.

This tutorial provides a comprehensive summary of differential gene expression (DGE) analysis techniques that are broadly applicable across various research scenarios. Specifically, it focuses on workflows using the popular tools limma, DESeq2, and edgeR—three of the most widely adopted packages in RNA-seq analysis.

While numerous tutorials and workshops on DGE exist, this tutorial aims to deliver a practical guide, clarifying the essential steps and decision points in the pipeline. It offers concrete suggestions and best practices to help users navigate the complexities of DGE analysis with confidence. Additionally, the tutorial provides curated resources, sample code, and highlights critical aspects of DGE, from data preparation and normalization to model selection and result interpretation.

Whether you're new to DGE or seeking to refine your analysis skills, this tutorial is designed to equip you with the foundational knowledge and tools to perform reliable and reproducible differential expression analysis in your own research.

Caution

  • This is not an introductory R class.
  • This is not a statistics course.
  • This is not a comprehensive RNA-seq theory/practice course. See Resources for more background.
  • This workshop does not cover upstream pre-processing.
  • Our starting point is a read count matrix: each cell indicates the number of reads originating from a particular gene (in rows) for each sample (in columns).

Prerequisites

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

Objectives

  • Understand the basics of RNA-seq data analysis.
  • Summarize key principles and steps in DGE analysis that are applicable across diverse research contexts.
  • Provide a focused overview of three commonly used DGE tools: limma, DESeq2, and edgeR.
  • Clarify the DGE pipeline with step-by-step guidance and decision-making tips.
  • Interpret and visualize results.
  • Supply materials and example code to support learning and hands-on practice.

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.

Resources

References

  • 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.

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

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