The course is dedicated to statistical and computational methods for the design and analysis of bioinformatics experiments.
The topics that will be covered in this course will likely include:
- Introduction to R and RStudio
- Introduction to cell biology
- Introduction to measurement technologies: microarrays, sequencing, SNPs and ChIP
- Basic statistics
- Gene Expression Microarrays: experimental designs, preprocessing and normalization, differential expression.
- RNA-seq: experimental designs, preprocessing and normalization, differential expression, splice variants
- Replication and pooling
- Gene Set enrichment analysis
- Clustering samples and genes
- Classifying samples using statistical machine learning
- Dimension reduction
- Combining data from multiple platforms
- Selected topics such as gene networks, time course experiments and project presentations as time permits
Dr. Naomi Altman is the primary author of these course materials.
This course makes extensive use if the R statistical software. See the Department of Statistics' Statistical Software page for information about obtaining a copy of R.
There will be no required text-book. Online course materials will combine methodological background description and presentation of analyses and results from recent articles. References and notes will be posted.
- 4 - 6 Homework Assignment (50% of grade)
- Individual Project and Presentation (50% of grade)
The course has no pre-requisites, but some computational skills and/or familiarity with basic concepts in statistics, bioinformatics and/or cell biology will help. Undergraduates must obtain consent of the instructors to register for the course.