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Data Analysis for Genomics

NEWS:

May 7, 2014: in order to clean up and trim down the memory size of this repo, we've moved out non-code objects out of this repo. Therefore, before submitting a pull request, please first clone a clean copy of the repository. The celfiles and agilent folder of week2 has been moved to the rawdata repo, and the reading microarray lab has been updated to reflect this.

May 5, 2014 : we are adding rendered content here: http://genomicsclass.github.io/book/

Labs and lectures:

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