1. I NSTITUTE for G ENOMIC B IOLOGY Nathan D. Price Department of Chemical and Biomolecular Engineering Center for Biophysics and Computational Biology Institute for Genomic Biology University of Illinois, Urbana-Champaign May 22, 2009

2. Meta-Analysis: What can we learn from integrating data from 2000 microarrays?  How distinct and separable are each of the phenotypes?  What are the molecular features that are unique for each phenotype?  Are these differences sufficient to enable identification of phenotype just given the microarray?  What are shared molecular features between subsets of phenotypes?  Can we reliably reconstruct networks active in bee brains and at what scale?

3. A few key issues  Data normalization from the loop design arrays  Using knowledge from drosophila to aid in interpretation at multiple levels  Single gene  Pathway  Network  Cell specificity?  Interpretation of data from homogenized brain  Multiple cell types  Scale of network reconstruction/inference possible  Particularly since we don’t have time series or molecular perturbation experiments

4. Minimal sample needs for statistical learning  Differential expression  Pathway Analysis  Classification  Network Inference  10s  10s-100s  100s-1000s Data sets in the 1000s are rare in biology today – so tremendous opportunity!

5. I NSTITUTE for G ENOMIC B IOLOGY

6. I NSTITUTE for G ENOMIC B IOLOGY Classification: molecular signatures to differentiate phenotypes Price, N.D. et al, PNAS 104:3414-9 (2007) 10 1 2 3 4 5 1 2 3 4 5 C9orf65 expression OBSCN expression ClinicopathologicalDiagnosis X–GIST O-LMS Classified as GIST Classified as LMS Accuracy on data = 99% Predicted accuracy on future data (LOOCV) = 98%

7. Multi-class classification: Example from brain disease

8. Novel method for pathway analysis: Differential rank conservation (DIRAC) tightly regulated pathway weakly regulated pathway shuffled pathway ranking between phenotypes GISTLMS …across pathways in a phenotype …across phenotypes for a pathway Highest conservation Lowest conservation g4g4 g1g1 g2g2 g3g3 g4g4 g1g1 g2g2 g3g3 g4g4 g2g2 g1g1 g3g3 g4g4 g1g1 g2g2 g3g3 g5g5 g6g6 g8g8 g7g7 g5g5 g7g7 g8g8 g6g6 g8g8 g6g6 g7g7 g5g5 g5g5 g8g8 g6g6 g7g7 g4g4 g1g1 g2g2 g3g3 g2g2 g3g3 g1g1 g4g4 Eddy et al, In preparation

9. Rank difference scores in GBM and normal

10. Diverse rank conservation in brain disease Phenotypes Pathways Highest rank conservation Lowest rank conservation Low conservation: Astrocytoma, grade I Lower conservation: Astrocytoma, grade III Lowest conservation: Glioblastoma (grade IV)

11. Differential regulation of pathway ranking in disease

12. Classification with DIRAC

13. Network inference Bonneau, R. et al, 7:R36, Genome Biology, 2006 Training Set (268 conditions)Test Set (24 conditions) Similar accuracies –not overfitting and has predictive capacity! Bi-clustering for data reduction and learning: SAMBA, cMONKEY

14. May 5, 2009 Project Overview Slide 14 geneWAY May 5, 2009 Final Presentation Slide 14 geneWAY Visual Representations My lab has made a function Matlab-based version of the Inferelator and are looking forward to testing it out on the BeeSpace data!

15. Price Lab Postdocs Pan-Jun Kim Amit Ghosh Graduate Students James Eddy Shu-wen Huang Matt Gonnerman Swati Gupta Caroline Milne Ravali Raju Jaeyun Sung Chunjing Wang Sriram Chandrasekaran Funding Sources NIH Howard Temin Pathway to Independence Award NSF CAREER Department of Defense – TATRC Energy Biosciences Institute (BP) Collaborators Donald Geman, Johns Hopkins Lee Hood, Institute for Systems Biology Ilya Shmulevich, Institute for Systems Biology Jonathan Trent, MD Anderson Cancer Center Wei Zhang, MD Anderson Cancer Center