Duke NGS Course (Summer 2015) 1.0
  • 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
      • Lecture notes
        • Biology
        • Experimental Design
        • Statistical Inference
        • Bioinformatics
  • « Assigned Reading
  • How to Claim ... »
  • Source

Lecture notes¶

Biology¶

  • Introduction to RNA-seq I

Experimental Design¶

  • Experimental Design I
  • Experimental Design II

Statistical Inference¶

  • Statistics for Weeks 1-2
  • Clustering exercise I

Bioinformatics¶

  • Overview of Bioinfromatics for RNA-seqI
  • Short Read Alignment Algorithms

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