Week 1
Lecture 1 - introduction and motivation here
Lecture 2 - review, ML, stats, genomics here
Reading & videos:
Short videos describing central dogma of biology here and here
Intro to Mol Biology for Computer Scientists (Hunter) here
Week 2
Lecture 3 - GWAS here
Lecture 4 - GWAS and confounding here
GWAS review paper here
Videos by StatQuest: P-values here, Bonferroni correction here , and FDR here.
Week 3
Lecture 5 - GWAS and confounding here
Lecture 6 - Guest lecture: population diversity and GWAS here
Suggested reading & videos:
Videos on statistical power here and power analysis here.
Population structure in genetics review article here
PCA videos: 20min video from StatQuest
Week 4
Lecture 7 - Gene regulation Intro here
Lecture 8 - Explainable AI Intro here
Video on gene expression process here.
Video on regulation of gene transcription here.
Review on regulatory elements here.
Short videos on regularized regression Part 1, Part 2, Part 3, and Part 4
First TWAS paper here
Week 5
Lecture 9 - Gene regulation con'd here
Lecture 10 - Confounding and causality con'd here
Historically important paper by Jeff Leek and co-authors, studies batch effect issues in gene expression datasets here.
Causality, "an introduction" blog post here.
Week 6
Lecture 11 - Regulatory Genomics - Epigenomics here
Lecture 12 - Regulatory Genomics - CNNs here
Review of xAI in regulatory genomics here
Week 8
Lecture 15 - Single cell RNA-seq intro here
Lecture 16 - Single cell data integration here
Suggested reading:
Nature Reviews Genetics (NRG) intro here
Best practices NRG here
Week 9
Lecture 17 - single cell data integration II here
Integrative single cell analysis NRG here
Connecting single cell genomics and human genetics NRG here