BIOS6660

Genomic Data Analysis Using R and BioConductor

Week 1 - … - Course Overview

Description

An overview of the course. We will review our plan in using R, Bioconductor, and RStudio to perform Reproducible Research compliance analysis workflow on the first few weeks of the course. I will also give you a brief overview of each use cases we will be performing.

Outcomes and Learning Objectives

Upon completion, the students will be able to
  • Have a better idea of what the course is all about
  • Understand what other skill set need to be updated before taking the course successfully

Week 2 - Jan 27 & 29 - RStudio and R Primer

Jan 27 Learning Objective

  • Getting familiar with the RStudio software environment


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Jan 29 Learning Objectives

  • Feature Resoures: Guerilla Guild to R
  • Review what you learned from Roger Peng R tutorial video
  • Hands On: "We BLATted the internet"
  • Create RStudio Project
  • Style guide


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Week 3 - Feb 3 & Feb 5 - Programming and Plotting: Part 1

Feb 3 Learning Objectives


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Student contribute resources:

Week 4 - Feb 10 & Feb 12 - Programming and Plotting: Part 2

Feb 10 Learning Objectives

    • Regular Expression
    • Debugging in R
  • Hands On: Visual debugging in RStudio


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Feb 12 Learning Objectives

    • Reproducible Research
    • The 'knitr' package
  • Hands On: Generate Report


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Week 5- Feb 19 Bioconductor and Storage Solution

Learning Objectives

    • Bioconductor Storage Solution


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Week 6 & 7 Microarray Analysis

Day1 Feb 24: Learning Objectives

  • Case Study: Robert Stearman
    • Application to Pulmonary Arterial Hypertension
  • How Bioconductor handles microarray data


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Day 2 Feb 26: Learning Objectives

  • Microarray Analysis using "EMA" package


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Day 3 Mar 3: Learning Objectives

  • Trouble Shooting Case 1 Microarray Data Analysis


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Day 4 Mar 5: Learning Objectives


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Week 8 & 9 RNA-seq

Day1 March 10: Learning Objectives

  • Case Study: Dr. Eric Schmidt
    • Using RNA sequencing to investigate pulmonary endothelial glycocalyx recovery after sepsis
  • Using Bioconductor for RNA-seq data analysis


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Go to supplementary file
Thomas Girke Sample RNA-seq analysis PDF

Day 2 March 12: Learning Objectives

  • RNAseq analysis: Thomas Girke lesson


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Day 3 Mar 17: Learning Objectives

  • Trouble Shooting Case 2 RNA-seq Data Analysis


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Day 4 Mar 19: Learning Objectives
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Week 10 & 11 ChIP-seq

Day1 March 31: Learning Objectives

  • Case Study: Dr. David Bentley
    • ChIP-seq a match made in heaven: Chromatin Immunoprecipitation and Deep Sequencing
  • Using Bioconductor for ChIP-seq data analysis


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Day 2 April 2: Learning Objectives

  • ChIP-seq analysis: Thomas Girke lesson


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Day 3 April 7: Learning Objectives

  • Trouble Shooting Case 3 ChIP-seq Data Analysis


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Day 4 April 9: Learning Objectives
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Week 12 & 13 shRNA-seq

Day1 April 14: Learning Objectives

  • Case Study: Dr. Mark Gregory
    • Large-scale shRNA screens to identify novel combination therapies for the treatment of cancer


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Day 2 April 16: Learning Objectives


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Day 3 April 21: Learning Objectives

  • Trouble Shooting Case 4 shRNA-seq Data Analysis


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Day 4 April 23: Learning Objectives
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Week 14 & 15 Exome-seq Analysis

Day 2 April 30: Learning Objectives


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Day 3 April 21: Learning Objectives

  • Trouble Shooting Case 4 Exome-seq Data Analysis


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Day 4 April 23: Learning Objectives
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