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Data Analysis for Genomics

NEWS:

March 30, 2014 : we are still adding R markdown files to this repository. Expect fully finished files for all weeks by the end of April 2014.

Week 1: Introduction

  • What we measure and why
  • R programming skills
  • Probability distributions
  • Exploratory data analysis

Week 2: Measurement technology

  • Microarray technology
  • Next generation sequencing technology
  • Working with data in R

Week 3: Inference

  • Inference
  • Linear models

Week 4: Background correction and normalization

  • Modeling
  • Background
  • Normalization

Week 5: Distance, clustering, and prediction

  • Distance and clustering
  • Prediction

Week 6: Batch effects

  • Statistical solutions to batch effects
  • Applying batch effects solutions

Week 7: Advanced differential expression

  • Hierarchical modeling
  • Multiple comparisons
  • Gene set testing
  • Gene and technology annotations

Week 8: Advanced topics

  • Manipulating NGS data using Bioconductor
  • Genome variation
  • RNA sequencing
  • DNA methylation
  • ChIP sequencing

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