Coding for Cancer
This project is currently under construction!
The information presented below includes some options for how these materials might be developed.
Description
These materials are supported by the Science Education and Training program at Fred Hutchinson Cancer Research Center.
General objectives:
- developing computational thinking
- understanding the process of biomedical research
- coding with R
Target audience:
- assumes no prior coding experience
- aimed at upper-level high school students (and potentially their teachers)
Delivery mechanism:
- two-week instructor-led summer course
- potential incorporation with Articulate
- self-directed, self-paced learning
Suggestions for curriculum development
For a two week summer program, the first week could focus on training and skills development, with the second week providing time to explore inquiry-based questions with various levels of scaffolding
To create basic intro to R materials, the introductory materials using a clinical cancer dataset could be streamlined by:
- framing the example coding as research questions
- minimize redundancy in examples
- remove second half of class 2 (factors and creating data frames by hand)
- add very basic statistical testing (t-tests?) to complement data visualizations
- explicitly incorporating aspects of computational thinking and the research lifecycle, perhaps also reproducibility and open science principles?
- including additional challenge exercises
The inquiry-based coding explorations in the second week could build on:
- these prompts created for practice coding using COVID-19 data; see R prompts here
- these materials that include RNAseq analysis, taught to interns in summer 2019
Class material
Materials for the original fredhutch.io materials include:
- Class 1: R syntax, assigning objects, using functions
- Class 2: Data types and structures; slicing and subsetting data
- Class 3: Data manipulation with
dplyr - Class 4: Data visualization in
ggplot2
The data used for this course are from the National Cancer Institute’s Genomic Data Commons. Please see Introduction to R from fredhutch.io for more information about how these data were compiled.
Please see the instructor’s guide for information on teaching these materials and the contributing guide for assistance in developing or modifying these materials.
Required software: Software requirements for this course include:
This course is adapted from the following sources:
- R for data analysis and visualization of Ecological Data from Data Carpentry
- Introduction to R from fredhutch.io