Lectures

Lecture schedule

Time : Tues Thurs 9:00 - 10:30am

Location: Building and room number listed on Canvas

Date Pre-reading Topic Instructor
Tue Jan 7 1 Lecture 1: Course intro; Molecular biology primer Keegan
Thu Jan 9 Lecture 2: High-dimensional biology intro: RNA, DNA, methylation, ChIP-Seq Keegan
Tue Jan 14 1 Lecture 3: Exploratory data analysis; confounding & batch effects Keegan
Thu Jan 16 1, 2, 3 Lecture 4: Statistics & probability primer Keegan
Tue Jan 21 1 Lecture 5: Statistical inference: two groups Keegan
Thu Jan 23 1, 2, 3 Lecture 6: Statistical inference: linear regression & ANOVA Keegan
Tue Jan 28 1 Lecture 7: Statistical inference: multiple linear regression Keegan
Thu Jan 30 Project proposal meetings
Tue Feb 4 1 Lecture 8: Statistical inference: continuous model & limma Keegan
Thu Feb 6 1 Lecture 9: Statistical inference: multiple testing Keegan
Tue Feb 11 1 Lecture 10: Application of statistical inference to RNA-seq Keegan
Thu Feb 13 Lecture 11: Application of statistical inference to epigenetics Yongjin
Tue Feb 18 Reading break (no class)
Thu Feb 20 Reading break (no class)
Tue Feb 25 1 Lecture 12: Gene set enrichment Yongjin
Thu Feb 27 Lecture 13: Supervised learning: GWAS and eQTL Yongjin
Tue Mar 4 Lecture 14: Supervised learning: polygenic risk prediction Yongjin
Thu Mar 6 Lecture 15: Supervised learning: post-GWAS & causal inference Yongjin
Tue Mar 11 Lecture 16: Single-cell data analysis: technology and data normalization Yongjin
Thu Mar 13 Lecture 17: Unsupervised learning: application of latent factor models to scRNA-seq Yongjin
Tue Mar 18 Lecture 18: Unsupervised learning: recent approaches Yongjin
Thu Mar 20 BIG Research Day (no class)
Tue Mar 25 Lecture 19: Model-based data analysis Yongjin
Thu Mar 27 Lecture 20: Spatial Transcriptomics Yongjin
Tue Apr 1 Final Project Presentations
Thu Apr 3 Final Project Presentations
Tue Apr 8 Final Project Presentations

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