140.635.11
Practical Genomics: Computational Tools for Cancer Research
Location
East Baltimore
Term
Summer Institute
Department
Biostatistics
Credit(s)
2
Academic Year
2025 - 2026
Instruction Method
In-person
Start Date
Monday, June 16, 2025
End Date
Friday, June 20, 2025
M,T,W,F 8:30a-12:30p;
M, Tu, W, F, 8:30am - 12:20pm
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only
Resources
Prerequisite
Basic knowledge of molecular biology, genomics, and cancer biology
Completion of preparatory tasks to set up cloud computing accounts
Some familiarity with R and RStudio
Designed for researchers and clinicians, this workshop introduces essential techniques for analyzing complex genomics datasets using R and Bioconductor. Learn to manipulate, visualize, and interpret data with real-world cancer biology examples, empowering you to integrate computational tools into your research workflows.
Provides an introduction to computational analysis of genomics datasets with applications in cancer research. Includes hands-on training in organizing, analyzing, and visualizing data using R, RStudio, and Bioconductor. Covers data manipulation, single-cell genomics, and differential gene expression analysis. Features live coding demonstrations, guided exercises, and capstone projects. Emphasizes real-world examples relevant to cancer biology and practical skills for integrating computational genomics into research workflows.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Organize, clean, and manage genomic datasets using cloud-based tools and R/RStudio.
- Create compelling visualizations using ggplot2 and R Notebooks, tailored to cancer datasets.
- Conduct foundational single-cell genomic analyses, including clustering, dimensionality reduction, and marker gene detection, with applications to cancer research.
- Analyze differential gene expression and correct for batch effects in cancer-related genomic datasets.
Methods of Assessment
This course is evaluated as follows:
- 40% Participation
- 30% Assignments
- 30% Final Project