Course Materials for Data Science for Developing Scholars in Down Syndrome Research (DS3) 2026

The Data Science for Developing Scholars in Down Syndrome Research (DS3) course is a training program working to increase data sciences expertise across the INCLUDE Project and the Down syndrome research community.

Main course website: https://includeds3.org/

Morning sessions: Short Read Sequencing Workshop
- Website: https://biodatasci.colorado.edu/shortread/
- GitHub: Dowell-Lab/srworkshop

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Getting started with R and RStudio

• Slides
• Installing and updating R, RStudio, and Git: README

Alternatives to running locally:

• Posit Cloud
• Cavatica
• Apply for INCLUDE Cloud Credits to use on Cavatica.

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Git workflow for accessing and updating course materials in this repository

Git commands (git clone, git pull) are NOT R code.
Run them in a Terminal (not the R Console):

• RStudio: Terminal tab (next to Console)
• Mac: Terminal
• Windows: Git Bash or Command Prompt

1. Clone this repository (first time only)

   git clone https://github.com/DS3-Course/DS3_2026.git

2. Pull updates each day to access new content:

   git pull

   Alternative: direct download from GitHub
3. Open the appropriate .Rproj file for each lesson in RStudio
4. Follow instructions in README.md files and R scripts

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Introduction to the Tidyverse

Slides
Rproject template: README
Tidy data exercise: README

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Afternoon sessions

Github (this repository): DS3_2026

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Day 1: The Human Trisome Project

Instructor: Matthew Galbraith
An overview of the Human Trisome Project and the journey from data to discovery to clinical trials.
Slides

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Day 2: Omics and Data Science Concepts

Instructor: Matthew Galbraith
An introduction to important concepts in omics and best practices in data science.
Slides

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Day 3: Reproducible data analysis using R and RStudio

Instructor: Matthew Galbraith
A guide to conducting reproducible data analysis in R using RStudio and tidy principles.
Slides
Rproject template: README
Tidy data exercise: README

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Day 4: Data Cleaning and Exploratory Data Analysis

Instructor: Matthew Galbraith
Exercises in data wrangling and exploration in R using tidyverse principles and tools.
Slides
Tidy data exercise: README
Intro to exploratory data analysis: README

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Day 5: Intro to Linear Regression and Hypothesis Testing in R

Instructor: Matthew Galbraith
An introduction to statistical modeling and testing in R
Slides
Linear regression exercise: README
HTP linear regression example: README

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Day 6: Advanced Data Visualization and Analysis in R

Instructor: Matthew Galbraith
Examples and discussion of effective andvanced data visualizations using ggplot in R.
Slides
HTP linear regression example: README
HTP DESeq2 analysis: README
HTP single-cell dataviz mass cytometry: README

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Day 7: Advanced Data Visualization and Analysis in R

Instructor: Jim Costello
Applications of simple machine learning methods to predict outcomes leveraging HTP datasets.
Visualize Cluster HTP: README

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Day 8: Supervised Machine Learning with HTP examples

Instructor: Jim Costello
Applications of simple machine learning methods to predict outcomes leveraging HTP datasets.
Predictive Models: README

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Day 9: Analysis of INCLUDE data with R using Cavatica

Instructors: Matthew Galbraith and Velsera
Slides
A guide to locating data in the INCLUDE Data Hub and transfering to the Cavatica platform for analysis.
Apply for INCLUDE Cloud Credits
HTP linear regression Cavatica: README

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Day 10: Responsible Conduct of Research and AI in Research

Instructors: Mary Ellen and Matthew Galbraith
Slides
Discussion of responsible conduct of research concepts and case studies, and the use of AI in research.

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