Introduction to Reproducibility in Cancer Informatics from Coursera

The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods.

This course is written for individuals who:

- Have some familiarity with R or Python - have written some scripts.

- Have not had formal training in computational methods.

- Have limited or no familiar with GitHub, Docker, or package management tools.

Motivation

Data analyses are generally not reproducible without direct contact with the original researchers and a substantial amount of time and effort (BeaulieuJones et al, 2017). Reproducibility in cancer informatics (as with other fields) is still not monitored or incentivized despite that it is fundamental to the scientific method. Despite the lack of incentive, many researchers strive for reproducibility in their own work but often lack the skills or training to do so effectively.

Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved. Reproducible analyses are more likely to be understood, applied, and replicated by others. This helps expedite the scientific process by helping researchers avoid false positive dead ends. Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing.

Curriculum

This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub.

The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these activities as they follow along with the course material to help increase the reproducibility of their analyses.

**Goal of this course:**

Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an "ease into it" approach. We attempt to give learners doable, incremental steps to increase the reproducibility of their analyses.

**What is not the goal**

This course is meant to introduce learners to the reproducibility tools, but _it does not necessarily represent the absolute end-all, be-all best practices for the use of these tools_. In other words, this course gives a starting point with these tools, but not an ending point. The advanced version of this course is the next step toward incrementally "better practices".

How to use the course

This course is designed with busy professional learners in mind -- who may have to pick up and put down the course when their schedule allows.

Each exercise has the option for you to continue along with the example files as you've been editing them in each chapter, OR you can download fresh chapter files that have been edited in accordance with the relative part of the course. This way, if you decide to skip a chapter or find that your own files you've been working on no longer make sense, you have a fresh starting point at each exercise.

What's inside

Syllabus

Introduction to this Course

In this first section, we will discuss the goals of this course and define what we mean by reproducibility.

Organizing your project

In this section we discuss motivation and strategies for project organization.

Using notebooks

In this section we discuss the motivation for using notebooks and integrated development environments to enhance the reproducibility of your project.

Making your project open source with GitHub

In this section we will describe how GitHub can make a project open source and encourage reproducibility.

Managing package versions

In this section we discuss two strategies for managing package versions in a project.

Writing durable code

In this section we discuss aspects of code that can make it more durable to enhance the reproducibility of a project.

Code review

This section discusses the importance of code review for creating reproducible analyses.

Documenting analysis

This section discusses how to document analyses to enhance their reproducibility.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches methods and skills for enhancing the reproducibility of data analyses in biomedical sciences and research, making findings more robust and reliable

Suitable for individuals with basic familiarity with R or Python but limited experience with computational methods and reproducibility tools, making it an accessible starting point

Guided by Candace Savonen, MS, an expert in biomedical sciences, ensuring the course content is up-to-date and aligns with industry best practices

Provides practical, hands-on exercises to reinforce concepts and empower learners to apply reproducibility principles to their own research

Covers essential reproducibility tools and techniques, including analysis notebooks, package managers, git, and GitHub, providing a solid foundation for enhancing reproducibility

Focuses on an "ease into it" approach, breaking down concepts into manageable steps to facilitate gradual adoption of reproducibility practices

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Reproducibility in Cancer Informatics with these activities:

Review R in preparation for course

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Review and practice basic R concepts to refresh your memory and enhance your understanding.

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Go over R syntax and data structures.
Practice writing simple R code to manipulate and analyze data.
Review R packages commonly used in data analysis.

Review R Programming Basics

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Provides stronger foundational skills in R, making concepts introduced in the early portions of this course easier to grasp.

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Practice writing and executing simple R scripts
Review basic R data types and structures

Join a peer study group on reproducibility

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Engage with peers in a study group dedicated to reproducibility, fostering collaboration and enhancing your understanding through shared experiences.

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Identify and join a peer study group focused on reproducibility.
Actively participate in discussions and share insights with group members.
Collaborate on projects or exercises related to reproducibility.

Six other activities

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Create a reproducible data analysis project

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Immediately puts into practice concepts introduced by the course, such as project organization and version control.

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Define a research question and gather relevant data
Create a project directory and set up version control
Write reproducible data analysis scripts
Document your analysis and share your project

Follow tutorials on GitHub and Docker

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Enhance your understanding of GitHub and Docker through guided tutorials, enabling you to manage code and create reproducible environments.

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Complete tutorials on GitHub basics.
Practice using GitHub for version control and collaboration.
Explore Docker tutorials to learn about containerization.

Explore tutorials on code review and documentation

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Expand your knowledge of code review and documentation through guided tutorials, enhancing your ability to produce high-quality, well-documented code.

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Follow tutorials on code review techniques.
Practice reviewing code for clarity, efficiency, and reproducibility.
Review tutorials on effective documentation practices.

Practice writing reproducible code

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Strengthen your skills in writing reproducible code through repetitive exercises, improving the reliability and transparency of your analyses.

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Review principles of reproducible code.
Complete coding exercises that focus on writing reproducible code.
Practice using tools and techniques for debugging and testing reproducible code.

Write a blog post on best practices for reproducible analyses

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Solidify your understanding and share your knowledge by creating a blog post that summarizes best practices for conducting reproducible analyses, benefiting both yourself and others.

Browse courses on Reproducibility

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Research and gather information on best practices for reproducible analyses.
Organize your thoughts and outline the content of your blog post.
Write the blog post clearly and concisely.

Contribute to an open-source project related to reproducibility

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Immerse yourself in the principles of reproducibility by contributing to an open-source project dedicated to developing tools or resources for reproducible analyses.

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Identify an open-source project focused on reproducibility.
Review the project's documentation and contribution guidelines.
Make a contribution to the project, such as reporting a bug, suggesting a feature, or writing code.

Career center

Learners who complete Introduction to Reproducibility in Cancer Informatics will develop knowledge and skills that may be useful to these careers:

Biostatistician

As a Biostatistician, you will use your training in computer programming and statistics to manage and analyze medical data. This course can help you develop the skills needed to ensure the reproducibility of your analyses, which is critical for ensuring the accuracy and reliability of medical research.

See salaries and explore the career path for Biostatistician

Bioinformatician

As a Bioinformatician, you will use your skills in computer programming and biology to analyze biological data. This course can help you develop the skills needed to ensure the reproducibility of your analyses, which is critical for ensuring the accuracy and reliability of biological research.

See salaries and explore the career path for Bioinformatician

Data Scientist

As a Data Scientist, you will use your programming skills and knowledge of data analysis to solve complex problems. This course can help you build a foundation in reproducible data analysis techniques, which are essential for working with large datasets and ensuring the accuracy and reliability of your results.

See salaries and explore the career path for Data Scientist

Statistician

As a Statistician, you will use your training in statistics to design, analyze, and interpret data. This course can help you develop the skills needed to create reproducible analyses, which are essential for ensuring the accuracy and reliability of your findings.

See salaries and explore the career path for Statistician

Data Analyst

As a Data Analyst, you will use your skills in data analysis to extract insights from data. This course can help you build a foundation in reproducible data analysis techniques, which are essential for working with large datasets and ensuring the accuracy and reliability of your results.

See salaries and explore the career path for Data Analyst

Financial Analyst

As a Financial Analyst, you will use your skills in mathematics and finance to analyze financial data. This course can help you build a foundation in reproducible data analysis techniques, which are essential for working with large datasets and ensuring the accuracy and reliability of your results.

See salaries and explore the career path for Financial Analyst

Data Engineer

As a Data Engineer, you will use your skills in data management and engineering to build and maintain data pipelines. This course can help you develop the skills needed to create reproducible data pipelines, which are essential for ensuring the accuracy and reliability of your data.

See salaries and explore the career path for Data Engineer

Research Analyst

As a Research Analyst, you will use your skills in data analysis and research to provide insights to businesses and organizations. This course can help you develop the skills needed to create reproducible analyses, which are essential for ensuring the accuracy and reliability of your findings.

See salaries and explore the career path for Research Analyst

Machine Learning Engineer

As a Machine Learning Engineer, you will use your skills in machine learning to build and deploy machine learning models. This course can help you develop the skills needed to create reproducible machine learning models, which are essential for ensuring the accuracy and reliability of your models.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

As a Quantitative Analyst, you will use your skills in mathematics and statistics to develop and implement quantitative models. This course can help you develop the skills needed to create reproducible models, which are essential for ensuring the accuracy and reliability of your models.

See salaries and explore the career path for Quantitative Analyst

Actuary

As an Actuary, you will use your skills in mathematics and statistics to assess risk and uncertainty. This course can help you develop the skills needed to create reproducible models, which are essential for ensuring the accuracy and reliability of your assessments.

See salaries and explore the career path for Actuary

Software Engineer

As a Software Engineer, you will use your programming skills to design, develop, and test software applications. This course can help you develop the skills needed to create reproducible code, which is essential for ensuring the accuracy and reliability of your software.

See salaries and explore the career path for Software Engineer

Computer Scientist

As a Computer Scientist, you will use your skills in computer programming and algorithms to solve complex problems. This course can help you build a foundation in reproducible programming techniques, which are essential for ensuring the accuracy and reliability of your software.

See salaries and explore the career path for Computer Scientist

Operations Research Analyst

As an Operations Research Analyst, you will use your skills in mathematics and optimization to solve complex problems. This course can help you develop the skills needed to create reproducible models, which are essential for ensuring the accuracy and reliability of your solutions.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

As a Risk Analyst, you will use your skills in mathematics and statistics to assess risk and uncertainty. This course can help you develop the skills needed to create reproducible models, which are essential for ensuring the accuracy and reliability of your assessments.

See salaries and explore the career path for Risk Analyst