This course explores public datasets and computational tools used to analyze human genomic data, to better understand how patterns in these data can be used to test hypotheses about evolution and human phenotypes. In the course, students will:
These modules (and the accompanying digital textbook) can be used as course materials or as resources for independent learning.
We run our course through Posit Cloud and recommend this approach if possible (although you'll likely have to pay for the Cloud Instructor plan).
For each semester of the course, create a new workspace on Posit Cloud. You can send students a contributor link to invite them to join as workspace members.
XX-<module_name>_starter.Rmd
) to the project.
XX-<module_name>.Rmd
file is for website rendering only.Access
tab (available by clicking the gear icon in the upper right) to make the project an assignment.During class, students can click on the assignment to derive their own projects to code in, which will contain the same software, data, etc. as your original copy.
We recommend running through the code before teaching, and adjusting the RStudio project parameters if necessary (ex: allocating more memory or CPU if it's having trouble reading in large data files).
If you want to learn this material independently, you can read through the digital textbook and use the starter code for each module to follow along with coding sections.
You will need to download RStudio. For each module:
<module_name>_starter.Rmd
) to your directory.
XX-<module_name>.Rmd
file is for website rendering only.tidyverse
, are used in multiple modules but only need to be installed once.)Then you're all set to start!
Raw data (+ pre-processing scripts), figure design files, and a second copy of the data for each module are available in this Google Drive folder.
The course website was designed with the JHU Data Science Lab's OTTR template.
If you have questions or encounter problems with the course materials, submit a GitHub issue or contact us at rajiv.mccoy[at]jhu.edu.
All materials in this course are licensed under a Creative Commons Attribution 4.0 International License unless noted otherwise.