1 / 112

Inferring gene regulatory networks from transcriptomic profiles

Dirk Husmeier. Inferring gene regulatory networks from transcriptomic profiles. Biomathematics & Statistics Scotland. Overview. Introduction Application to synthetic biology Lessons from DREAM. Network reconstruction from postgenomic data. Accuracy. Mechanistic models. Bayesian networks.

dexter-neal
Download Presentation

Inferring gene regulatory networks from transcriptomic profiles

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Dirk Husmeier Inferring gene regulatory networks from transcriptomic profiles Biomathematics & Statistics Scotland

  2. Overview • Introduction • Application to synthetic biology • Lessons from DREAM

  3. Network reconstruction from postgenomic data

  4. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  5. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  6. Shortcomings Pairwise associations do not take the context of the systeminto consideration direct interaction common regulator indirect interaction co-regulation

  7. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  8. 1 2 Direct interaction 1 2 Conditional Independence Graphs (CIGs) Inverse of the covariance matrix strong partial correlation π12 Partial correlation, i.e. correlation conditional on all other domain variables Corr(X1,X2|X3,…,Xn) Problem: #observations < #variables  Covariance matrix is singular

  9. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  10. Model Parameters q Probability theory  Likelihood

  11. 1) Practical problem: numerical optimization q 2) Conceptual problem: overfitting ML estimate increases on increasing the network complexity

  12. Overfitting problem True pathway Poorer fit to the data Equal or better fit to the data Poorer fit to the data

  13. Regularization E.g.: Bayesian information criterion Regularization term Data misfit term Maximum likelihood parameters Number of parameters Number of data points

  14. Likelihood BIC Complexity Complexity

  15. Model selection: find the best pathway Select the model with the highest posterior probability: This requires an integration over the whole parameter space:

  16. Problem: huge computational costs q

  17. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  18. Marriage between graph theory and probability theory Friedman et al. (2000), J. Comp. Biol. 7, 601-620

  19. Bayes net ODE model

  20. Model Parameters q Bayesian networks: integral analytically tractable!

  21. UAI 1994

  22. Linearity assumption [A]= w1[P1]+ w2[P2] + w3[P3] + w4[P4] + noise P1 w1 P2 A w2 w3 P3 w4 P4

  23. Homogeneity assumption

  24. Accuracy Mechanistic models Bayesian networks Conditional independence graphs Methods based on correlation and mutual information Computational complexity

  25. Example: 4 genes, 10 time points

  26. Standard dynamic Bayesian network: homogeneous model

  27. Limitations of the homogeneity assumption

  28. Our new model: heterogeneous dynamic Bayesian network. Here: 2 components

  29. Our new model: heterogeneous dynamic Bayesian network. Here: 3 components

  30. Learning with MCMC q Allocation vector h k Number of components (here: 3)

  31. Learning with MCMC q Allocation vector h k Number of components (here: 3)

  32. Non-homogeneous model  Non-linear model

  33. BGe: Linear model [A]= w1[P1]+ w2[P2] + w3[P3] + w4[P4] + noise P1 w1 P2 A w2 w3 P3 w4 P4

  34. Can we get an approximate nonlinear model without data discretization? y x

  35. Can we get an approximate nonlinear model without data discretization? Idea: piecewise linear model y x

  36. Inhomogeneous dynamic Bayesian network with common changepoints

  37. Inhomogenous dynamic Bayesian network with node-specific changepoints

  38. NIPS 2009

  39. Non-stationarity in the regulatory process

  40. Non-stationarity in the network structure

  41. Flexible network structure .

  42. Flexible network structure with regularization

  43. Flexible network structure with regularization

  44. Flexible network structure with regularization

  45. ICML 2010

  46. Morphogenesis in Drosophila melanogaster • Gene expression measurements over 66 time steps of 4028 genes (Arbeitman et al., Science, 2002). • Selection of 11 genes involved in muscle development. Zhao et al. (2006), Bioinformatics22

  47. Transition probabilities: flexible structure with regularization Morphogenetic transitions: Embryo  larva larva pupa pupa  adult

  48. Overview • Introduction • Application to synthetic biology • Lessons from DREAM

More Related