Inference Methods for High-Throughput CRISPR Screens - CGSI 2022

Inference Methods for High-Throughput CRISPR Screens - CGSI 2022

Computational Genomics Summer Institute CGSI via YouTube Direct link

Our updated model links the unobserved reported to the sampling distribution

11 of 13

11 of 13

Our updated model links the unobserved reported to the sampling distribution

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Classroom Contents

Inference Methods for High-Throughput CRISPR Screens - CGSI 2022

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  1. 1 Intro
  2. 2 How can so many genes contribute to complex traits?
  3. 3 Upstream regulators can be inferred by perturbations
  4. 4 Fluorescence activated cell sorting (FACS) + CRISPR enable high-throughput gene expression screening
  5. 5 Review of setup from the computational side
  6. 6 Start with the question
  7. 7 We have unique data - let's think carefully
  8. 8 Multiple guides target the same gene and thus should be correlated
  9. 9 What is my sampling distribution?
  10. 10 The model is a form of density estimation with overdispersion
  11. 11 Our updated model links the unobserved reported to the sampling distribution
  12. 12 Our new model incorporates sparsity at the gene- level
  13. 13 Hierarchical model enables accurate inference with few samples

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