• 2023 CBW Analysis Using R Workshop
  • Welcome
    • Meet your Faculty
    • Pre-workshop Materials and Laptop Setup Instructions
      • Laptop Setup Instructions
      • R packages
  • Lecture slides
  • Module 1: Exploratory Data Analysis and Clustering
    • Load mouse data
    • RColorBrewer for colour palettes
    • Correlations, distances, and clustering
    • Hierarchical clustering
    • K-means clustering
    • Using clValid to determine number of clusters
    • Exercise
  • Module 1: Exercise Results
  • Module 2: Dimensionality reduction
    • Principal Component Analysis
      • Step 1. Preparing Our Data
      • Step 2. Apply PCA
      • Step 3. Visualisation of PCA results
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Uniform Manifold Approximation and Projection (UMAP)
    • Exercise
  • Module 2: Exercise Results
  • Module 3: Generalized Linear Models
    • Essential R: Read tables from files and merge
    • Read data from files and explore variable distribution
    • Explore missing data
    • Create plots with ggplot2 to explore variable relationships
    • Fit binary response variable using glm() and logistic regression
    • Exercise
  • Module 3: Exercise Results
  • Module 4: Finding differentially expressed genes with RNA-seq
    • Mini introduction to BioConductor
    • Fetch breast cancer data using curatedTCGAData
      • Prepare data for differential expression analysis
    • Differential expression analysis with edgeR
    • Exercise
  • Module 4: Exercise Results
  • Published with bookdown

Analysis Using R

Lecture slides

  • Module 1: Exploratory Data Analysis and Clustering
  • Module 2: Dimensionality reduction for visualization and analysis
  • Module 3: Generalized linear models
  • Module 4: Multiple hypothesis testing with RNA-seq differential expression analysis