The sequencing of the human genome ushered in the “post-genome” biological revolution
Biology is now a data science
Biology as Data Science
Domains of Biological Data Analysis
Modern biological data analysis requires skills and knowledge from many domains:
- molecular biology, genetics, biochemistry
- statistics, mathematics
- computer science
- programming & software engineering
- data visualization
- high performance & cloud computing
No one person can be expert in all these areas!
Experts create tools and techniques that we can use.
The R Programming Language
- R is a statistical programming language
- https://www.r-project.org/
- Designed to conduct statistical analyses and visualize data
- NOT a general purpose programming language!
tidyverse
tidyverseis a set of R packages first developed by Hadley Wickham- https://www.tidyverse.org/
- Transforms R into a data science language
- Adds functionality and avoids some pitfalls of base R
Book & Course Objectives
Learn R and its related packages to analyze biological data
Communicate results of R analyses with effective visualizations and notebooks
Learn how to use the RStudio development environment
Write correct and reproducible code using formal testing strategies
Share analyses with others using RShiny applications
Course Topics
Who This Book Is For
You are: a practicing biologist wishing to learn how to use R to analyze biological data
We assume a basic working knowledge of:
- genetics
- genomics
- molecular biology
- biochemistry
- statistics
We endeavor to explain required background whenever possible to relax these assumptions
Sources and References
R Materials
- Hands-On Programming with R, by Garrett Grolemund
- R for Data Science, by Hadley Wickam, Garrett Grolemund, et al
- Advanced R, by Hadley Wickam
- STAT 545 - Data wrangling, exploration, and analysis with R
- What They Forgot to Teach You About R
- Reproducible Analysis with R, by State of Alaska’s Salmon and People Project, NCEAS
- Data Science for Psychologists, by Hansjörg Neth
Sources and References
Data visualization
- How Charts Lie: Getting Smarter about Visual Information, by Alberto Cairo
- The Functional Art - An Introduction to Information Graphics and Visualization, by Alberto Cairo
- The Truthful Art - Data, Charts, and Maps for Communication, by Alberto Cairo
Course Structure
Weekly lectures
7 assignments, roughly one per week
Final project: RShiny application combining the techniques you learned in the assignments
Grading:
- Assignments 5% each (35% total)
- Final project 60%
- Class attendance/participation 5%
Zoom link is available
Things that are more important than this course
Your physical, emotional and mental health
Your family and friends
Policy on absences / missed classes
- You never need to disclose anything private to me, just let me know if you will be absent for an extended period of time or need an extension, and I will work with you to accommodate your situation.
Assignments
Detailed instructions are in the book
Assignments hosted on GitHub
We will use GitHub Classroom to make them available
Assignment Structure
Each assignment has similar format and workflow, files:
├── reference_report.html ├── main.R ├── README.md ├── report.Rmd └── test_main.R
Assignment Repository Structure
Assignment Workflow
