DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101

DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101

bioinformagician via YouTube Direct link

Generalized Linear Models

11 of 12

11 of 12

Generalized Linear Models

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DESeq2 Basics Explained - Differential Gene Expression Analysis - Bioinformatics 101

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  1. 1 Intro
  2. 2 A typical study design
  3. 3 Features of RNA-Seq counts data
  4. 4 Poisson distribution for counts data
  5. 5 Why is Poisson not the best model?
  6. 6 Negative Binomial is the way to go!
  7. 7 DESeq2 steps
  8. 8 Biases in counts data
  9. 9 Estimate Size Factor median of ratios method
  10. 10 Estimate Dispersions
  11. 11 Generalized Linear Models
  12. 12 Hypothesis testing

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