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RNA-Seq - Plotting and Data Visualisation in R

Author: Dr Irina Chelysheva
Date: 2024-11-20 & 2024-12-05

Session Overview​

We will explore how to effectively analyse and visualise RNA-seq data using R. The session will focus on applying data visualisation techniques to gain meaningful insights from transcriptomic datasets.

Learning Objectives​

By the end of this session, you will be able to:

  1. Employ the ggplot2 R package to create informative and aesthetic plots for visualising transcriptomic data.
  2. Describe key features of common plot types, including boxplots, violin plots, and heatmaps.
  3. Manipulate plotting aspects such as axis scales and labels, titles, legends, colours, and shapes.
  4. Create a range of plots for summarising analysis results and selecting appropriate plot types for the data.
  5. Use online tools to identify and interpret enriched or depleted pathways (GO terms) based on a differential gene expression (DGE) table.

Preparatory Materials​

RNA-Seq Dataset​

The RNA-seq dataset we will work with is publicly available:
GSE211066 - RNA-seq Dataset

Research Paper​

To understand the dataset and its biological context, please read the associated research paper:
PMC10050925 - Research Paper

Supplementary Table​

We will use the DGE supplementary table from the research paper during the session.
Download the DGE Table (Insert link here)

Online Tools for GO-Term Analysis​

We will use the following online tools for Gene Ontology (GO) term enrichment analysis and pathway visualisation:

  1. DAVID (Database for Annotation, Visualisation, and Integrated Discovery)

    • Features: Functional annotation clustering, gene-term enrichment analysis, visualisation tools.

  2. PANTHER (Protein Analysis Through Evolutionary Relationships)

    • Features: GO term enrichment, pathway analysis, functional classification, overrepresentation tests.

  3. ShinyGO

    • Features: Intuitive web interface for GO enrichment and visualisation, integrates other pathway data.

What to Expect​

During the session, we will:

  1. Explore basic and advanced plotting techniques using the ggplot2 package.
  2. Create visual summaries of RNA-seq data, including:
    • Boxplots
    • Violin plots
    • Heatmaps
  3. Learn to adjust visualisation elements (e.g., labels, legends, and colours) to enhance clarity and aesthetics.
  4. Use online tools for GO term enrichment analysis, linking visualisation results to biological pathways.