DESeq2 Analysis with R: outline

Credits

There is lots of useful material out there. This course is heavily influenced by those tutorials:

• Harvard Chan bioinformatics core’s DGE workshop using Salmon counts
• DESeq2 vignette
• Hugo Tavares’ contrast tutorial

We will also be using publicly available data, which has been altered for educational purposes (and should thus be treated as such).

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Overview

1. Day I: Getting and understanding the data

• Warmup & R-epetition
• Data, Metadata & Design
• Data Exploration
• Quality Control

2. Day II: Data Transformations and Model

• Quality Controls & Filtering
• Data Transformations and Normalization
• Modeling Count Data
• Running DESeq2

3. Day III: Differentially Expressed Genes

• Complex Experimental Designs and Contrasts
• Hypothesis tests
• Inspection & Visualization
• Gene Annotations
• Exporting results

4. Day IV: On your own

• Run a multifactorial DESeq2 Analysis. We’ll be around to help.