Computational methods to analyze
large-scale and high-dimensional
   gene perturbation screens

                 McGill Un...
A wealth of data
New technologies over the last 10–15 years have allowed
genome-wide measurements of . . .

              ...
How to understand a complex system?


                                     Richard Feynman:
                              ...
How to understand a complex system?


                                     Richard Feynman:
                              ...
External perturbations
                                                                             Drugs
      Small
    ...
One- or Low-dimensional Phenotypes

  viability or cell death                        growth rate              activity of ...
One- or Low-dimensional Phenotypes

  viability or cell death                        growth rate              activity of ...
Members of Swi/Snf-complex regulate Nanog




                                                                   Schaniel ...
Members of Swi/Snf-complex regulate Nanog




    Smarcc1 binding targets
    from ChIP-chip data
    Functional targets f...
f
                                                                                                       ti
       Members...
Phenotyping screens: what to observe?

One-dimensional Phenotypes:
    identify candidate genes on a genome-wide scale
   ...
Phenotyping screens: what to observe?

One-dimensional Phenotypes:
    identify candidate genes on a genome-wide scale
   ...
The information gap
Direct information
effects are visible at other
pathway components
     Pathway                       ...
The information gap
Direct information                                                  Indirect information
effects are v...
Bridging the information gap
1. Nested Effects Models ::
   pathway features can be inferred from high-
   dimensional phe...
Bridging the information gap
1. Nested Effects Models ::
   pathway features can be inferred from high-
   dimensional phe...
Bridging the information gap
1. Nested Effects Models ::
   pathway features can be inferred from high-
   dimensional phe...
1. Nested Effects Models
                                     Pathway




                                                ...
Immune response in Drosophila
                    Response to microbial challenge
                    (Boutros et al., Dev...
Immune response in Drosophila
                    Response to microbial challenge
                    (Boutros et al., Dev...
Immune response in Drosophila
                    Response to microbial challenge
                    (Boutros et al., Dev...
Nested Effects Models




                           Markowetz et al. (2005, 2007), Tresch and Markowetz (2008)

Florian M...
NEM: Model formulation




  Pathway genes: X, Y, Z                                Effects: E1, . . . , E6
  • core topolo...
NEM: Inference

Exhaustive enumeration: score all subset patterns to find the
one fitting the data best
                    ...
Extensions to NEMs




                 Drop the transitivity
                     requirement




                       ...
Extensions to NEMs




                 Drop the transitivity                              Likelihood based on
           ...
Extensions to NEMs




                 Drop the transitivity                              Likelihood based on
           ...
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Computational Analysis of large-scale and high-dimensional gene perturbation screens

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Computational Analysis of large-scale and high-dimensional gene perturbation screens

  1. 1. Computational methods to analyze large-scale and high-dimensional gene perturbation screens McGill University, Montreal, March 26, 2008 Florian Markowetz florian@genomics.princeton.edu http://genomics.princeton.edu/∼florian Lewis-Sigler Institute for Integrative Genomics Princeton University
  2. 2. A wealth of data New technologies over the last 10–15 years have allowed genome-wide measurements of . . . Genome sequences Gene and protein expression Protein-Protein interactions Transcription factor binding Tissue/cellular localization Histone modifications DNA methylation Figure from fig.cox.miami.edu Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 1
  3. 3. How to understand a complex system? Richard Feynman: “What I cannot create, I do not understand.” Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 2
  4. 4. How to understand a complex system? Richard Feynman: “What I cannot create, I do not understand.” Functional Genomics: “What I cannot break, I do not understand.” Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 2
  5. 5. External perturbations Drugs Small molecules RNAi Protein Stress Knockout mRNA DNA Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 3
  6. 6. One- or Low-dimensional Phenotypes viability or cell death growth rate activity of reporter genes Size A- Time Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 4
  7. 7. One- or Low-dimensional Phenotypes viability or cell death growth rate activity of reporter genes Size A- Time Example: Finding regulators of Nanog in Mouse ES cells Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 4
  8. 8. Members of Swi/Snf-complex regulate Nanog Schaniel C, et al., submitted to Nature Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 5
  9. 9. Members of Swi/Snf-complex regulate Nanog Smarcc1 binding targets from ChIP-chip data Functional targets from microarray after Smarcc1 knockdown Schaniel C, et al., submitted to Nature Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 5
  10. 10. f ti Members of Swi/Snf-complex regulate Nanog mo d ze mi ti C11 op 2 Smarcc1 binding targets C CGG from ChIP-chip data bits 1 CC A AC Functional targets from microarray 0 TT G CG after Smarcc1 knockdown 1 2 3 4 5 6 7 8 9 Schaniel C, et al., submitted to Nature Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 5
  11. 11. Phenotyping screens: what to observe? One-dimensional Phenotypes: identify candidate genes on a genome-wide scale first step for follow-up analysis hard to relate to specific gene function and pathways Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 6
  12. 12. Phenotyping screens: what to observe? One-dimensional Phenotypes: identify candidate genes on a genome-wide scale first step for follow-up analysis hard to relate to specific gene function and pathways High-dimensional Phenotypes: Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 6
  13. 13. The information gap Direct information effects are visible at other pathway components Pathway Pathway B ? B D D A A C C - Cell survival or death - Growth rate - downstream genes Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 7
  14. 14. The information gap Direct information Indirect information effects are visible at other effects are only visible at pathway components ’downstream reporters’ Pathway Pathway Pathway Pathway B B ?? B B D D D D A A A A C C C C - Cell survival or death death - Cell survival or - Growth rate rate - Growth - downstream genes genes - downstream Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 7
  15. 15. Bridging the information gap 1. Nested Effects Models :: pathway features can be inferred from high- dimensional phenotypes. Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 8
  16. 16. Bridging the information gap 1. Nested Effects Models :: pathway features can be inferred from high- dimensional phenotypes. 2. Probabilistic data integration :: Physical interactions between proteins must explain perturbation effects. Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 8
  17. 17. Bridging the information gap 1. Nested Effects Models :: pathway features can be inferred from high- dimensional phenotypes. 2. Probabilistic data integration :: Physical interactions between proteins must explain perturbation effects. 3. Comprehensive Phenotypes :: Dissecting cell fate regulation in mESC by probing four levels of gene regulation. Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 8
  18. 18. 1. Nested Effects Models Pathway ? B D A C High-dimensional Phenotypes Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 9
  19. 19. Immune response in Drosophila Response to microbial challenge (Boutros et al., Dev Cell, 2002) Columns: silenced genes. Rows: effects on other genes. Figures courtesy of Michael Boutros Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 10
  20. 20. Immune response in Drosophila Response to microbial challenge (Boutros et al., Dev Cell, 2002) Columns: silenced genes. Rows: effects on other genes. Results: 1. Silencing tak1 reduces expression of all LPS-inducible transcripts. 2. Silencing rel (key) or mkk4/hep reduces expression of separate sets of induced transcripts. Figures courtesy of Michael Boutros Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 10
  21. 21. Immune response in Drosophila Response to microbial challenge (Boutros et al., Dev Cell, 2002) Columns: silenced genes. Rows: effects on other genes. Results: 1. Silencing tak1 reduces expression of all LPS-inducible transcripts. 2. Silencing rel (key) or mkk4/hep reduces expression of separate sets of induced transcripts. Figures courtesy of Michael Boutros Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 10
  22. 22. Nested Effects Models Markowetz et al. (2005, 2007), Tresch and Markowetz (2008) Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 11
  23. 23. NEM: Model formulation Pathway genes: X, Y, Z Effects: E1, . . . , E6 • core topology • states are observed • to be reconstructed = Data D • connection to pathway unknown = Model M = Parameters θ Likelihood P (D | M ) given false positive and false negative rates Markowetz et al., 2005 Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 12
  24. 24. NEM: Inference Exhaustive enumeration: score all subset patterns to find the one fitting the data best Markowetz et al. Bioinformatics, 2005 MCMC, Simulated Annealing: take small probabilistic steps to explore model space . . . with A Tresch; in preparation, 2008 Divide and conquer: break a big model into smaller, manageable pieces and then re-assemble Markowetz et al. ISMB 2007 Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 13
  25. 25. Extensions to NEMs Drop the transitivity requirement Tresch and Markowetz (2008) Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 14
  26. 26. Extensions to NEMs Drop the transitivity Likelihood based on log-ratios of effects requirement Tresch and Markowetz (2008) Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 14
  27. 27. Extensions to NEMs Drop the transitivity Likelihood based on log-ratios of effects

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