High Throughput Sequencing

This course introduces analysis of high-throughput sequencing data through RNA-seq and ChIP-seq, covering normalization, dimensionality reduction, clustering, differential expression, and statistical testing. Examples include PCA in R and Python and linear-model workflows in R.

Syllabus

StatQuest: A gentle introduction to RNA-seq.
StatQuest: A gentle introduction to ChIP-Seq.
StatQuest: Principal Component Analysis (PCA), Step-by-Step.
StatQuest: PCA in R.
StatQuest: PCA in Python.
RPKM, FPKM and TPM, Clearly Explained!!!.
StatQuest: MDS and PCoA.
StatQuest: t-SNE, Clearly Explained.
StatQuest: Hierarchical Clustering.
Drawing and Interpreting Heatmaps.
StatQuest: DESeq2, part 1, Library Normalization.
StatQuest: edgeR, part 1, Library Normalization.
StatQuest: edgeR and DESeq2, part 2 - Independent Filtering.
StatQuest: MDS and PCoA in R.
StatQuest: P Values, clearly explained.
False Discovery Rates, FDR, clearly explained.
Fisher's Exact Test and the Hypergeometric Distribution.
StatQuest: RNA-seq - the problem with technical replicates.
StatQuest: Logs (logarithms), clearly explained.
StatQuest: PCA main ideas in only 5 minutes!!!.
Principal Component Analysis (PCA) clearly explained (2015).
StatQuest: Linear Models Pt.1 - Linear Regression.
StatQuest: Linear Regression in R.
StatQuest: Linear Models Pt.2 - t-tests and ANOVA.
StatQuest: Linear Models Pt.3 - Design Matrices (old version).
StatQuest: Linear Models Pt.3 - Design Matrix Examples in R.
StatQuest: K-means clustering.