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StatQuest: Linear Models Pt.3 - Design Matrix Examples in R
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Classroom Contents
High Throughput Sequencing
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- 1 StatQuest: A gentle introduction to RNA-seq
- 2 StatQuest: A gentle introduction to ChIP-Seq
- 3 StatQuest: Principal Component Analysis (PCA), Step-by-Step
- 4 StatQuest: PCA in R
- 5 StatQuest: PCA in Python
- 6 RPKM, FPKM and TPM, Clearly Explained!!!
- 7 StatQuest: MDS and PCoA
- 8 StatQuest: t-SNE, Clearly Explained
- 9 StatQuest: Hierarchical Clustering
- 10 Drawing and Interpreting Heatmaps
- 11 StatQuest: DESeq2, part 1, Library Normalization
- 12 StatQuest: edgeR, part 1, Library Normalization
- 13 StatQuest: edgeR and DESeq2, part 2 - Independent Filtering
- 14 StatQuest: MDS and PCoA in R
- 15 StatQuest: P Values, clearly explained
- 16 False Discovery Rates, FDR, clearly explained
- 17 Fisher's Exact Test and the Hypergeometric Distribution
- 18 StatQuest: RNA-seq - the problem with technical replicates
- 19 StatQuest: Logs (logarithms), clearly explained
- 20 StatQuest: PCA main ideas in only 5 minutes!!!
- 21 Principal Component Analysis (PCA) clearly explained (2015)
- 22 StatQuest: Linear Models Pt.1 - Linear Regression
- 23 StatQuest: Linear Regression in R
- 24 StatQuest: Linear Models Pt.2 - t-tests and ANOVA
- 25 StatQuest: Linear Models Pt.3 - Design Matrices (old version)
- 26 StatQuest: Linear Models Pt.3 - Design Matrix Examples in R
- 27 StatQuest: K-means clustering
