This course aims to help learners understand and apply high-throughput sequence analysis techniques. By the end of the course, students will be able to perform RNA-seq and ChIP-Seq analysis, conduct Principal Component Analysis (PCA) in R and Python, interpret RPKM, FPKM, and TPM values, apply MDS, PCoA, t-SNE, and Hierarchical Clustering, normalize libraries using DESeq2 and edgeR, explain P Values and False Discovery Rates, understand Fisher's Exact Test and the Hypergeometric Distribution, analyze technical replicates in RNA-seq, work with logarithms, grasp the concepts of Linear Regression and ANOVA, and apply K-means clustering. The course utilizes a series of StatQuest videos to explain these concepts in a clear and concise manner. This course is designed for individuals involved in high-throughput sequence analysis.
Overview
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.
Taught by
StatQuest with Josh Starmer
