• Site
  • Assigned Reading
  • Lecture notes
  • How to Claim and Access Your Virtual Machine
  • Installing R, RStudio and IPython notebook with the R kernel
  • Basic R in the Jupyter Notebook and RStudio
  • Introduction to R
  • Preparing Data for Analysis
  • Working with Data
  • Grouping and Aggregation
  • Hypothesis Testing and Power Calculations
  • Probability distributions and Random Number Genereation
  • Writing Custom Functions
  • Functional Programming
  • Linear Regression
  • Using R for supervised learning
  • Unsupervised Learning
  • Unsupervised Learning and NGS
  • Multiple Testing
  • Counting Models/Discrete Distributions
  • Generalized Linear Models
  • Base Graphics
  • Comparing Base Graphics with ggplot2
  • Coding Exercises
  • Introduction to DESeq2
• Page