MATH 891.002, GNET / BCB 891.001
Time: February 18 – April 15, Monday 5:00-7:10pm
Location: Marsico 5004 (Except April 1 – Marsico 6004)
Instructors: Chuck Perou, Katie Hoadley, Steve Marron, Andrew Nobel, Joel Parker, and Greg Forest
The objectives of this course are to:
- Gain an understanding of the genomic data types being collected on human tumors
- Learn about the basics of RNA and DNA NGS sequence analysis
- Learn about pattern discovery tools including hierarchical clustering and biclustering
- Understand the challenges of integrating heterogeneous data types
- Learn real world examples of complex data integration for class discovery
Course expectations:
- Group project with hands-on analysis of genomics data
- Final report (3 pages)
2/18/19 – Lecture 1 (Chuck Perou and Katie Hoadley)
- Lecture
- Reading Material
2/25/19 – Lecture 2 (Andrew Nobel)
- Lecture
- Goals for week
- Form groups of 3-4 students
3/4/19 – Lecture 3 (Steve Marron)
- Lecture
- Reading Material:
-
Liu, Y., Hayes, D. N., Nobel, A., & Marron, J. S. (2008). Statistical significance of clustering for high-dimension, low–sample size data. Journal of the American Statistical Association, 103(483), 1281-1293.
-
Huang, H., Liu, Y., Yuan, M., & Marron, J. S. (2015). Statistical significance of clustering using soft thresholding. Journal of Computational and Graphical Statistics, 24(4), 975-993.
-
3/11/19 – No Class Spring Break
- Goals for week
- Plan/work on analyses to perform
- Email instructors as needed for advice
3/18/19 – Lecture 4 (Joel Parker)
- Lecture
- Goals for week
- 1-2 page summary of proposed analysis for each group due
- Feedback will be returned to each group within a few days
3/25/19 – Lecture 5 (Katie Hoadley)
- Lecture
- Goals for week
- finalize data sets and analyses to be performed with instructors
4/1/19 – In Class project discussions with Professors **Room Change Marsico 6004
4/8/19 – No Class, work on project analyses
4/15/19 – In Class Student Presentations (group) and 2-3 page written Report (individual)