1. . Inferring Subnetworks from Perturbed Expression Profiles D. Pe’er A. Regev G. Elidan N. Friedman

2. Expression Profiling u An Expression Profile is a simultaneous measurement of the level of all mRNAs in a cell population u Experimental design: Measure profiles of mutated or treated cultures u Goal: infer regulatory and molecular interactions Wild-TypeMutant Profile Compare

3. Common Approaches u Comparative Analysis (Holstage et al. 1998) u Clustering (Hughes et al. 2000) u Limitations: l Cannot distinguish between direct and indirect interactions l Limited to pair-wise relations l Can not infer a finer context

4. Bayesian Network Framework Friedman, Linial, Nachman,Pe’er (JCB 2000) u Probabilistic: Characterize statistical relationships between expression patterns of different genes u Multi-variable interactions (beyond pair-wise): l Identify intermediate interactions l Handle combinatorial regulation by several gene-products u Statistical confidence: Asses the statistical significance of interactions found

5. Our Contributions u Modeling of mutations and treatments into the Bayesian network framework u Novel data discretization based on guided k- means clustering u New features: Mediator and Regulator u Automatic reconstruction of statistically significant sub-networks. - 0 + +

6. Modeling Gene Expression Gene 1 Expression level of each gene = Random variable Gene 3 Gene 4 Gene 5 Gene 2 Gene interaction = Probabilistic dependency Directed Acyclic graph Models dependency structure of distribution 0.90.1 1 2 1 0.20.8 0.6 0.4 0.90.1 21 2 2 1 21 P(3 | 1,2) Each node has a probabilistic function Conditioned on its parents in the graph Activator Inhibitor Graph structure + local probability Define a unique multivariate distribution

7. Mutational Assay Wild-Type Measurements 0.90.1 pgk1 0.10.9 pgk1 P(rap1|pgk1) Equivalence: Two models explain correlation between RAP1 & PGK1 RAP1PGK1 RAP1PGK1 Mutated pgk1 Measurements 0.5 pgk1 0.5 pgk1 P(rap1|pgk1) Note causality into mutated variable

8. Compendium Dataset ( Hughes et al., 2000) u 300 samples of yeast deletion mutants and other treatments u Deleted genes are from various functional families u A rich variety of profiles, but… u There is only one sample from each mutation

9. Guided K-means Discretization Guided K-means Discretization Expression data Markov Separator Edge Regulator Bayesian Network Learning Algorithm + Bootstrap Reconstruct SubNetworks Visualize Using Pathway Explorer Visualize Using Pathway Explorer Preprocess Learn model Feature extraction Feature assembly Visualization E R B A C S

10. Resulting PDAG

11. Confidence Estimates: Bootstrap D resample D1D1 D2D2 DmDm... Learn E R B A C E R B A C E R B A C Estimate: Bootstrap approach [FGW, UAI99]

12. Estimating Confidence Common Practice: Pick a single top scoring model Problem: Insufficient information!! In gene expression data: only few hundred experiments => many high scoring models Answer based on one model useless Solution: Search for features common to many likely models! Sample models from posterior distribution P(Model|Data) Confidence of feature : Feature of G, e.g., X  Y

13. Guided K-means Discretization Guided K-means Discretization Expression data Markov Separator Edge Regulator Bayesian Network Learning Algorithm + Bootstrap Reconstruct SubNetworks Visualize Using Pathway Explorer Visualize Using Pathway Explorer Preprocess Learn model Feature extraction Feature assembly Visualization

14. Markov Relations Question: Do X and Y directly interact? l Parent-child (one gene regulating the other) u Hidden Parent (two genes co-regulated by a hidden factor) (0.91,0.67) SST2STE6 SST2STE6 Mating pathway regulator Exporter of mating factor ARG5 ARG3 (0.84,0.79) ARG3 ARG5 GCN4 Arginine Biosynthesis Transcription factor

15. Low Correlation Relations u Previously unknown link strongly supported by evidence in the literature u High confidence, Low correlation l Processes occur under specific conditions l Captured by our context specific score ESC4KU70 (0.91, 0.16) DNA ds break repair Chromatin silencing

16. Separators  Question: Given that X and Y are indirectly dependant, who mediates this dependence? u Separator relation: l X affects Z who in turn affects Z l Z regulates both X and Y AGA1FUS1 KAR4 Mating transcriptional regulator of nuclear fusion Cell fusion

17. Separators: Intra-cluster Context CRH1YPS3 SLT2 Cell wall protein MAPK of cell wall integrity pathway Cell wall protein YPS1 Cell wall protein SLR3 Protein of unknown function + + u All gene pairs have high correlation, l clustering groups them together u assigned putative function to SLR3 - cell wall protein u We can assign regulatory role to SLT2 u Many other signaling and regulatory proteins were identified as direct and indirect separators

18. Guided K-means Discretization Guided K-means Discretization Expression data Markov Separator Edge Regulator Bayesian Network Learning Algorithm + Bootstrap Reconstruct SubNetworks Visualize Using Pathway Explorer Visualize Using Pathway Explorer Preprocess Learn model Feature extraction Feature assembly Visualization

19. Sub-Networks u Reconstruct a Conserved sub-network l Provides a more global picture l Allows to include features with lower-confidence l Preserved in most networks with high posterior l Probably reflects a real biological process u Automatic algorithm l Score: high concentration of pairwise features l Greedy search for high scoring subgraphs

20. Increased Confidence (simulated data) Percent of False positives 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 00.10.20.30.40.50.60.70.80.91 Confidence Entire network Subnetwork

21. Guided K-means Discretization Guided K-means Discretization Expression data Markov Separator Edge Regulator Bayesian Network Learning Algorithm + Bootstrap Reconstruct SubNetworks Visualize Using Pathway Explorer Visualize Using Pathway Explorer Preprocess Learn model Feature extraction Feature assembly Visualization

22. Rosetta networks in Pathway Explorer u http://www.cs.huji.ac.il/labs/compbio/ismb01

23. Summary u Primary contribution: automated methodology for finding patterns of interactions among genes l Clear semantics l Principled handing of mutations and interventions u Built in handling of statistical significance l Feature confidence l Extracts significant sub-networks u Differs from clustering l Inter-cluster relations l Finer intra-cluster structure u Provides biologist with promising hypothesis