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

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Published on January 13, 2009

Author: florianmarkowetz

Source: slideshare.net

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

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

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

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

External perturbations Drugs Small molecules RNAi Protein Stress Knockout mRNA DNA Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 3

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

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

Members of Swi/Snf-complex regulate Nanog Schaniel C, et al., submitted to Nature Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 5

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

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

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

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

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

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

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

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

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

1. Nested Effects Models Pathway ? B D A C High-dimensional Phenotypes Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 9

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

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

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

Nested Effects Models Markowetz et al. (2005, 2007), Tresch and Markowetz (2008) Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 11

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

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

Extensions to NEMs Drop the transitivity requirement Tresch and Markowetz (2008) Florian Markowetz, Analyzing Phenotyping Screens, March 26, 2008 14

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

Extensions to NEMs Drop the transitivity Likelihood based on log-ratios of effects

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