Learning resources · For trainees
A practical
starting point.
This page brings together introductory materials for R, the command line, and single-cell RNA-seq analysis. Start with the required resources in sequence and return to the references as questions come up. You do not need to master everything before beginning a project.
Back to academic profileCore learning path
Start with the foundations.
These materials provide a useful baseline in programming, command-line work, and single-cell analysis.
R programming and data visualization
Build a practical foundation in R and learn to explore and communicate data clearly.
- Introduction to R Programming
Introductory materials prepared for learning R.
Required - Data Visualization with R
An introduction to data visualization using R.
Required - R for Data Science, 2nd edition
A useful reference, especially for data manipulation with dplyr and visualization with ggplot2.
Reference
Linux and the command line
Learn to navigate directories, manage files, and run commands in a shell.
- The Unix Shell
Software Carpentry’s hands-on introduction to the command line.
Required - Install WSL2 with Ubuntu
Recommended Linux environment for Windows. macOS and Linux users can use the terminal included with their system.
Windows setup
Single-cell RNA-seq analysis
Work through a standard analysis workflow, from quality control to cell annotation and marker discovery.
- Seurat PBMC 3K tutorial
A hands-on introduction to quality control, normalization, dimensionality reduction, clustering, annotation, and marker genes.
Required - HCA Thailand 2024 workshop materials
Practical exercises and examples from the workshop.
Required
Reference
Go deeper as needed.
Single-cell Best Practices
A broad reference on quality control, normalization, integration, annotation, and differential expression. Its examples are primarily in Python, but many of the methodological concepts also apply to R and Seurat.
Optional materials
Lectures and workshops.
Use these slides for additional background as your questions and project needs develop.
Additional references
Useful tools for later.
- Version Control with Git
Learn the basics of tracking and organizing analysis scripts. AI tools can help explain Git commands, but review each change before applying it.
- Scanpy tutorials
Python-based single-cell analysis workflows, if they are useful for your project.