High Throughput Molecular Genomic and Epigenomic Data Analysis
This course will cover basic analysis of microarrays, RNA-Seq, and ChIP-Seq data including hands-on lab sessions. The class also covers an introduction to the underlying biology and the technologies used for measuring RNA levels, transcription factor binding an epigenetic modifications, and quality control of microarray and deep sequencing data. Topics: technologies, experimental design, data preprocessing, normalization, quality control, statistical inference (group comparisons, peak detection), multiple comparison adjustments, power calculations, clustering, functional enrichment testing.
Topics
- technologies
- experimental design
- data preprocessing
- normalization
- quality control
- statistical inference (group comparisons
- peak detection)
- multiple comparison adjustments
- power calculations
- clustering
- functional enrichment testing
Topics explicitly listed in official catalogue description
Prerequisites & course information
STATS 400 (or equivalent) and graduate standing: or permission of instructor. Students should have completed at least programming class with a passing grade. Preparation in biiology or quantitative analysis also recommended. (3). (BS). May not be repeated for credit.
Sources
- Course catalogue ↗2026-2027
Catalogue variants
- High Throughput Molecular Genomic and Epigenomic Data Analysis · 3 · Graduate credit