🧬 Data Visualization for Genomics & Biological Sciences

A comprehensive 3-day workshop on creating publication-quality visualizations for genomics and biological data using R and ggplot2.

📚 Course Materials

Day 1: Foundations

• Introduction to Data Visualization
  • Why visualization matters (Anscombe's Quartet, Datasaurus Dozen)
  • Big data challenges in biology
  • Typography and color theory
  • Color palettes for genomics
• ggplot2 Fundamentals
  • Grammar of Graphics philosophy
  • Building plots layer by layer
  • Custom themes and styling
• Essential Plot Types
  • Scatter plots (PCA, correlations)
  • Bar charts (expression comparisons)
  • Box plots & violin plots (distributions)
  • Stacked plots (microbiome composition)

Day 2: Advanced Genomics Visualizations

• Dimensionality Reduction
  • PCA, UMAP, t-SNE theory and implementation
  • Choosing the right method
  • Practical examples with gene expression data
• Volcano Plots
  • Step-by-step construction
  • Methods: base ggplot2, EnhancedVolcano, ggvolc
  • Multi-level classifications
• Peak Visualization
  • ChIP-seq and ATAC-seq data
  • Track plots with karyoploteR
  • Detailed locus views with Gviz

Day 3: Specialized Visualizations

• Set Comparisons
  • Venn diagrams (ggvenn, ggVennDiagram)
  • UpSet plots for complex overlaps
  • When to use each method
• Phylogenetic Trees
  • Multiple layouts (rectangular, circular, radial)
  • Highlighting clades and adding metadata
  • Advanced annotations with ggtreeExtra
• Circular Genome Plots
  • circlize package fundamentals
  • Multi-track visualizations
  • Links for structural variants
• Gene Arrangements & Synteny
  • gggenes for gene-level detail
  • Comparative genomics
  • Prophage insertion visualization

🎨 Key Features

• Publication-ready outputs using custom themes
• Cross color palette throughout for consistency
• Real biological examples from genomics research
• Hands-on exercises for each major topic
• Best practices for scientific visualization
• Colorblind-friendly design principles

📦 Required Packages

Core Visualization

library(tidyverse)      # Data manipulation & ggplot2
library(patchwork)      # Combine plots
library(MetBrewer)      # Color palettes
library(RColorBrewer)   
library(pheatmap)       # Heatmaps

Specialized Genomics

library(ggtree)         # Phylogenetic trees
library(circlize)       # Circular plots
library(karyoploteR)    # Karyotype visualization
library(Gviz)           # Genomic tracks
library(gggenes)        # Gene arrangements
library(UpSetR)         # Set comparisons
library(ggvenn)         # Venn diagrams

Dimensionality Reduction

library(umap)
library(Rtsne)

🚀 Getting Started

1. Clone this repository
2. Install required packages
3. Open the .Rproj file in RStudio
4. Navigate to slides/ for presentation materials
5. Follow along with the embedded code examples

📖 Resources

• Custom theme function included for consistent styling
• Example datasets provided for all exercises
• Color palette guide (Cross palette from MetBrewer)
• Links to awesome-genome-visualization collection

🎓 Learning Outcomes

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

• ✅ Create publication-quality figures for genomics research
• ✅ Choose appropriate visualizations for different data types
• ✅ Customize plots extensively using ggplot2
• ✅ Visualize complex genomic relationships
• ✅ Apply best practices in scientific data visualization
• ✅ Integrate multiple data types in comprehensive figures

👨‍🏫 Instructor

Dr. Loukas Theodosiou
Senior Data Scientist | Population Genomics & AI/ML
GitHub

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Course Materials: December 2024
License: Materials are provided for educational purposes