Course Content
- Lesson 0: Welcome and Introductions
- Lesson 1: Data Visualization Theory
- Lesson 2: Spatially Resolved Transcriptomic Data
- Lesson 3: Summary Statistics and QC Metrics
- Lesson 4: Principal Components Analysis
- Lesson 5: T-distributed Stochastic Neighbor Embedding
- Lesson 6: Kmeans Clustering
- Lesson 7: Differential Gene Expression
- Lesson 8: Misleading Visualizations
- Lesson 9: Putting It All Together and Interactivity
- Lesson 10: RNA Velocity
- Lesson 11: Review and Midterm
- Lesson 12: Spatial Proteomics
- Lesson 13: Spatial Epigenomics
- Lesson 14: Single cell genomics and multi-omics
- Lesson 15: Applications in Neuroscience
- Lesson 16: Applications in Cancer
- Lesson 17: Review and Final
- Lesson 18: Guest Lecture - Lyla Atta
- Lesson 19: Guest Lecture - Brendan Miller
- Lesson 20: Final Project
- Lesson 21: Final Presentations