Advanced Reproducibility in Cancer Informatics

Module 1: Getting started in this course

This module introduces the course and prepares learners for the content and hands-on exercises ahead.

Module 2: Defining Reproducibility

This module delves into the concept of reproducibility, setting the foundation for understanding the tools and techniques introduced in the course.

Module 3: Version control with GitHub

Learners will explore the use of git and GitHub for version control, understanding how these tools contribute to reproducibility in data analyses.

Module 4: Code review - as an author

This module focuses on the role of the author in code review, emphasizing the importance of collaborative and transparent practices in reproducible data analyses.

Module 5: Code review - as a reviewer

Here, learners will understand the perspective of a reviewer in code review processes, gaining insights into effective review practices for reproducibility.

Module 6: Launching Docker

This module introduces Docker and its role in creating reproducible environments for data analysis, providing practical guidance on its implementation.

Module 7: Modifying a Docker image

Learners will delve into the customization of Docker images, understanding how to tailor reproducible environments to specific analysis requirements.

Module 8: Automation as a reproducibility tool

This module explores the use of automation tools for enhancing reproducibility in data analyses, providing insights into efficient and scalable reproducible practices.