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Welcome to Data Visualization in R

This workshop is designed to teach you the fundamentals of creating compelling, publication-quality visualizations using R and ggplot2. Whether you’re a biology student, researcher, or data analyst, mastering data visualization is essential for exploring your data and communicating your findings effectively.

Learning Objectives

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

  • Understand the principles of effective data visualization
  • Create the 6 essential plot types used in 90% of scientific publications
  • Apply the grammar of graphics using ggplot2
  • Customize plots with themes, colors, and annotations
  • Work with real genomics data from The Cancer Genome Atlas (TCGA)
  • Create publication-ready figures for your research
  • Create the plots from scratch instead of relying on the Seurat wrappers.

Workshop Structure

Lesson 1: Introduction to Data Visualization

Learn the fundamental principles of effective data visualization, including when to use different plot types and best practices for visual communication.

Lesson 2: Practical ggplot2

Master the core visualization types using real TCGA cancer genomics data:

  • Scatter plots and correlation analysis.
  • Histograms for distribution exploration.
  • Boxplots and violin plots for group comparisons.
  • Bar plots for summary statistics.

Lesson 3: Heatmaps Demystified

Deep dive into creating and customizing heatmaps - the 6th essential plot type for genomics research.

Lesson 4: Advanced Single-cell RNA-seq Visualization

visualizing single-cell RNA sequencing data from scratch. Compare the Seurat wrappers with ggplot2 native solutions side by side.

Prerequisites

  • Basic knowledge of R programming
  • R and RStudio/Positron installed
  • Required R packages: tidyverse, readr, dplyr, ggplot2

Data

The workshop uses gene expression data from The Cancer Genome Atlas (TCGA), one of the largest and most comprehensive cancer genomics datasets available. This provides students with experience working with real-world, high-dimensional biological data.

Wait, I am jumping ahead of myself, actually we are using a pre-processed RNAseq Transcript per million (TPM) data from here.

But if you want to start with the Raw counts file, I have a blog post on how to get the data and convert it to TPM in this blog post.

The single cell RNAseq visualization part will use the commonly used PMBC3k dataset from SeuratData.

Getting Started

  1. Download the TCGA gene expression dataset
  2. Install required packages:
install.packages(c("tidyverse", "readr", "dplyr", "ggplot2", "Polychrome", "forcats"))
  1. Start with Lesson 1: Introduction to Data Visualization

This workshop uses the workflowr framework for reproducible research. Each lesson builds upon the previous one, so we recommend following them in order.


sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.3         knitr_1.48       
 [5] rlang_1.1.4       xfun_0.52         stringi_1.8.4     processx_3.8.4   
 [9] promises_1.3.0    jsonlite_1.8.8    glue_1.8.0        rprojroot_2.0.4  
[13] git2r_0.35.0      htmltools_0.5.8.1 httpuv_1.6.15     ps_1.7.7         
[17] sass_0.4.9        fansi_1.0.6       rmarkdown_2.27    jquerylib_0.1.4  
[21] tibble_3.2.1      evaluate_0.24.0   fastmap_1.2.0     yaml_2.3.10      
[25] lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1     compiler_4.4.1   
[29] fs_1.6.4          pkgconfig_2.0.3   Rcpp_1.0.13       rstudioapi_0.16.0
[33] later_1.3.2       digest_0.6.36     R6_2.5.1          utf8_1.2.4       
[37] pillar_1.9.0      callr_3.7.6       magrittr_2.0.3    bslib_0.8.0      
[41] tools_4.4.1       cachem_1.1.0      getPass_0.2-4